# AI in Education Wiki > A comprehensive wiki of 594 research articles and 133 concepts covering AI in education — papers, frameworks, and methodologies. ## Articles - [A framework for characterising and capturing the quality of digital interactions and experiences in early childhood education](https://edtechdev.github.io/aied/articles/a-framework-for-characterising-and-capturing-the-quality-of-digital-interactions/): **Synthesis:** This study introduces a Digital Interactions Quality (DigIQ) framework and scale as a protocol to observe and index the quality of interactions and experiences involving digital technologies with children in Early Childhood Education and Care (ECEC) settings. Observations in 50 pre-school classrooms validated the framework, revealing that quality ratings were higher when learning in - [Generalizing a Highly Configurable Analytics Pipeline to Replicate and Support Educational Research Across Multiple Domains](https://edtechdev.github.io/aied/articles/a4l-analytics-pipeline/): Artificial intelligence assistants deployed in online learning environments create new opportunities to collect large volumes of learner interaction data and generate insights to improve student outcomes. Architecture for AI-Augmented Learning (A4L) is a modular data architecture that enables the collection, integration, and analysis of learner interaction data from educational AI systems, support - [Learning to Use AI for Learning: Teaching Responsible Use of AI Chatbot to K-12 Students Through an AI Literacy Module](https://edtechdev.github.io/aied/articles/aaai2026-prompting-literacy-k12/): **Synthesis:** An LLM-based interactive module teaches K-12 students prompting literacy through scenario-based deliberate practice with an AI auto-grader providing immediate, detailed feedback. Deployed across 11 secondary classrooms in two iterations, the module improved students' prompting skills (particularly embedding background context) and confidence in using AI for learning. The study also - [The Absent Cognitive Baseline: Theorizing a Structural Gap in AI-Native College Students' Academic Self-Assessment](https://edtechdev.github.io/aied/articles/absent-cognitive-baseline-2026/): **Synthesis:** This paper proposes the Absent Cognitive Baseline (ACB) as a conceptual framework describing how pervasive generative AI use during secondary schooling may reduce the independent cognitive encounters on which academic self-assessment depends. Drawing on metacognitive theory, self-regulated learning, and epistemic development scholarship, the ACB framework describes a structural gap - [AcademiClaw: When Students Set Challenges for AI Agents](https://edtechdev.github.io/aied/articles/academiclaw-student-agent-benchmark/): **Yu, Lu, Si et al. (77 authors, 2026)** — Shanghai Jiao Tong University, SII, GAIR. Open-source benchmark. - [Acceptance of AI-Assisted English Language Learning Tools in Higher Education: Psychological Correlates Across Disciplinary and Proficiency Groups](https://edtechdev.github.io/aied/articles/acceptance-ai-english-tools-2026/): **Synthesis:** Wu et al. (2026) examined how learning motivation, self-efficacy, anxiety, and risk perception relate to acceptance of AI-assisted English language learning in a Chinese higher-education context, building on the Technology Acceptance Model (TAM). Drawing on survey data from 210 undergraduates (STEM = 91, Humanities = 119; English proficiency Low = 77, Intermediate = 103, High = 30), - [Access is Not Enough: Human Support Improves Engagement with AI Tutoring](https://edtechdev.github.io/aied/articles/access-not-enough-ai-tutoring-2026/): Robinson, Gormley, Ribeiro & Loeb (2026) ran two RCTs showing that AI tutoring's binding constraint is **take-up, not capability**: despite dedicated session time, nearly half of students never used the platform and users averaged only 2–5 minutes per week. An in-person engagement tutor (not direct instruction) raised usage by 1–4 minutes/week and engagement by 71–80% — but dosage stayed far below - [AdaPT: Adaptive Lesson Plan Transformer for Cross-Regional and Differentiated Instruction](https://edtechdev.github.io/aied/articles/adapt-adaptive-lesson-plan-transformer/): AdaPT uses transformers to adapt lesson plans across regional and differentiated instruction contexts; improves teacher efficiency while maintaining pedagogical alignment with local curricula. - [Do Gains from Generative AI-Enabled Adaptive Pretesting Persist? Evidence from a Retention Study](https://edtechdev.github.io/aied/articles/adaptive-pretesting-retention/): Akgun and Toker (2026) examine whether learning gains from GenAI-enabled adaptive pretesting persist over a seven-week retention period. Undergraduate participants completed adaptive AI-assisted pretesting, received instruction, took a baseline assessment, and were randomly assigned to three follow-up conditions: adaptive spaced retrieval practice, fixed spaced retrieval practice, or learner-direc - [The Empirically Grounded Adaptive Virtual Patient for Psychotherapy Training](https://edtechdev.github.io/aied/articles/adaptive-virtual-patient-psychotherapy-training/): **Angela Chen, Siwei Jin, Catherine Bao, Canwen Wang, Robert E. Kraut, Tongshuang Wu, Haiyi Zhu** — cs.CY, cs.HC - [Leveling the Playing Field: Temporal Video Segmentation for Individuals with ADHD in Computing Education](https://edtechdev.github.io/aied/articles/adhd-video-segmentation-computing-education/): Pimenova, Begel and colleagues evaluate a post-hoc video processing intervention that segments instructional videos into single-instruction chunks with fixed pauses, reducing extraneous cognitive load for learners with ADHD. In a within-participants study (17 ADHD, 10 non-ADHD), the intervention improved everyone but had an equalizing effect: ADHD participants' errors and hesitations fell to parit - [Adversarial Stress Testing of Role-Playing Language Agents using Multi-Agent Evaluation](https://edtechdev.github.io/aied/articles/adversarial-stress-testing-role-playing-agents/): **Synthesis:** This paper presents a modular multi-agent platform for adversarially stress-testing agentic-ai through structured multi-turn dialogue. With three coordinated agents — Interrogator (applying six progressive adversarial strategies), Target, and Judge — the system reveals failure modes invisible to single-strategy testing, reducing robustness scores by 0.17-0.20 points. The framework i - [A Formative Study of Brief Affective Text as a Complement to Wearable Sensing for Longitudinal Student Health Monitoring](https://edtechdev.github.io/aied/articles/affective-text-wearable-student-health/): In a year-long study of 458 university students (3,610 person-waves) using Oura rings for passive physiological sensing, researchers examined whether **ultra-brief affective text prompts** (median 3-word responses to "what concerns you most?") could enrich the interpretation of wearable data. Using NLP methods spanning dictionary-based (LIWC), general pretrained embeddings, and domain-adapted mode - [The agency gap in AI-supported writing: how reactive and proactive agent designs shape multimodal reasoning](https://edtechdev.github.io/aied/articles/agency-gap-ai-writing/): A randomized experiment (n = 79 medical/nursing students) examining how the **initiative design** of an AI writing agent shapes reasoning, agency, and immediate independent performance. Students completed two multimodal analytical writing tasks (interpreting healthcare-simulation data visualisations: bar chart, network diagram, ward heatmap) with either a **reactive agent** (responds only when pro - [Exploring How Agent Voice Accents Shape Human-AI Collaboration in K-12 Group Learning](https://edtechdev.github.io/aied/articles/agent-voice-accents-k12-group-learning/): **Ravi, Stevens, Hurt, Hanks, Lin & Anderson (2026)**. - [Agentic AI in Education: A Scoping Review of Research Landscape, Capabilities, and the Frontier Agent Paradigm](https://edtechdev.github.io/aied/articles/agentic-ai-education-scoping-review/): This scoping review systematically maps **474 studies** (January 2020 – May 2026) on generative AI-powered agentic AI in education, providing the most comprehensive synthesis of the field to date. The authors analyze publication characteristics, study designs, agent roles, AI models/architectures, six dimensions of agentic capability, and the extent of educational theory integration. - [Agentic AI and Pedagogical Best Practice: The Tension Between Automation and Learning](https://edtechdev.github.io/aied/articles/agentic-ai-pedagogical-best-practice-2026/): Education AI is shifting from passive chatbots to **proactive agents** that initiate and pursue goals. This offers personalisation but risks undermining **learner agency and cognitive effort**. The paper walks each of six pedagogical principles through what agentic initiative does to it: - [Agentic Education with AI Coding Assistants](https://edtechdev.github.io/aied/articles/agentic-education-coding/): AI coding assistants proliferate rapidly, but pedagogical frameworks for learning them remain scarce — a paradox at the heart of agentic coding education. - [Agentic Literacy Debt: A Structural Problem the AI Literacy Field Has Not Yet Named](https://edtechdev.github.io/aied/articles/agentic-literacy-debt/): **Agentic Literacy Debt** names a critical gap in the ai-literacy landscape that has become urgent with the rise of autonomous AI agents. Existing AI literacy frameworks assume humans evaluate AI outputs and then decide — they were built for a world of tools, not agents. But modern AI agents plan, decide, and act without step-by-step human approval, creating a structural deficit when deployed with - [Agentic Workflows in Education](https://edtechdev.github.io/aied/articles/agentic-workflows-education/): A design framework for educational AI systems structured around four agentic paradigms: **reflection**, **planning**, **tool use**, and **multi-agent collaboration**. Proposed by Kamalov et al. (2026) as a taxonomy for analyzing how AI agents operate in learning environments. - [Agents That Teach: Designing Incidental Learning Back into AI-Assisted Software Development](https://edtechdev.github.io/aied/articles/agents-that-teach-incidental-learning/): As AI coding agents take over substantial implementation work, developers increasingly lose the informal, effortful problem-solving through which software engineering expertise historically accumulated. The authors argue this "incidental learning" will not return spontaneously and that over-reliance on agentic coding lets unpracticed skills atrophy, accruing a developer-level analogue of Technical - [AgentSchool: An LLM-Powered Multi-Agent Simulation for Education](https://edtechdev.github.io/aied/articles/agentschool-multi-agent-simulation-education-2026/): Ye et al. (2026) introduce **AgentSchool**, an LLM-driven multi-agent simulating-students that models learning as **state transition rather than prompted behavior**. It couples cognitively growable student agents (weighted subject knowledge graphs, thinking-workflow pools, explicit misconceptions) with adaptive teacher agents that plan, scaffold, and reflect along the zone-of-proximal-development, - [Agreement Is Not Quality: Blind Expert Verification of Human and LLM Qualitative Coding When Human Consensus Is Not Ground Truth](https://edtechdev.github.io/aied/articles/agreement-not-quality-llm-coding-verification/): **Alex Liu, Lief Esbenshade, Michael Xiao, Victor Tian, Zachary Zhang, Kevin He, Min Sun** — arXiv preprint (2026). ## Synthesis - [Perceptions and Acceptance of Artificial Intelligence in Science Education Programmes: Voices of Pre-Service Science Teachers](https://edtechdev.github.io/aied/articles/ai-acceptance-preservice-science-teachers-2026/): **Synthesis:** This survey of 380 pre-service science teachers in Ghana, guided by UTAUT and the Theory of Planned Behaviour, finds generally positive perceptions of AI and strong intentions to use it, with ChatGPT the most frequently used tool for research, content explanation, and lesson planning. Positive attitudes and favorable effort expectancy were not fully matched by actual adoption, indic - [The Main Barrier to AI Adoption in the Public Sector is Lack of Training](https://edtechdev.github.io/aied/articles/ai-adoption-training-public-sector/): Through Brazilian government case studies, demonstrates that a four-layer pedagogical methodology (Literacy, Protocol, Prompt Engineering, Audit) is the key to productivity gains (up to 50%), rather than premium models. This work emphasizes that ai-literacy is a developmental capacity requiring structured scaffolding and prompt-engineering discipline. It connects to the need for curriculum-design - [Guidelines for Designing AI Technologies to Support Adult Learning](https://edtechdev.github.io/aied/articles/ai-adult-learning-design/): A set of 19 empirically-grounded design guidelines for AI-supported learning technologies tailored to adult learners, synthesized by Reddig et al. (2026) from longitudinal deployment data at a US national research institute focused on adult learning and online education. - [Guidelines for Designing AI Technologies to Support Adult Learning](https://edtechdev.github.io/aied/articles/ai-adult-learning-guidelines-dis2026/): **Synthesis:** Drawing on longitudinal deployment data from the National AI Institute for Adult Learning and Online Education (AI-ALOE), this DIS 2026 paper synthesizes 19 empirically grounded design guidelines for AI-powered adult learning technologies. The guidelines span cognitive, social, and teaching presence dimensions and are derived from reflexive thematic analysis of ~1,600 stakeholder st - [Enacting Constructive Conflicts with AI Agents to Enhance Reconsideration among Novice Interaction Designers](https://edtechdev.github.io/aied/articles/ai-agents-constructive-conflict-design-education-2026/): **Synthesis:** Investigates adversarial AI design agents that enact constructive conflict to prompt reconsideration in novice designers. Between-subjects experiment (N=48) comparing adversarial vs. cooperative AI agent roles. Adversarial agent condition produced significantly more design iterations, broader exploration of alternatives, and higher-rated final designs. Participants reported the conf - [When AI Agents Teach Each Other: Discourse Patterns Resembling Peer Learning in the Moltbook Community](https://edtechdev.github.io/aied/articles/ai-agents-peer-learning-discourse/): **Authors:** Eason Chen, Ce Guan, A Elshafiey, Zhonghao Zhao, Joshua Zekeri, Afeez Edeifo Shaibu, Emmanuel Osadebe Prince **Year:** 2026 **Venue:** arXiv (cs.HC) Mining discourse from Moltbook, a social network of over 2.4 million AI agents, reveals peer-learning-like dynamics (validation 22%, knowledge extension 18%) across 28,683 posts and yields six design hypotheses for educational AI. - [AI-Driven Assessment of Human Tutors: Linking Training Performance to Real-Life Practice](https://edtechdev.github.io/aied/articles/ai-assessment-human-tutors/): AI-driven assessment of human tutor training performance correlates with real-life tutoring quality; bridges the gap between training metrics and classroom practice. - [A bit of chaos and madness: The AI Assessment Scale and the work of assessment reform](https://edtechdev.github.io/aied/articles/ai-assessment-scale-reform/): This study examines the implementation of the Artificial Intelligence Assessment Scale (AIAS), a structured framework for redesigning assessment in response to generative-ai. Surveying 80 academic staff, the researchers found that while the framework's transparency and guidance were valued, implementation was hampered by departmental inconsistencies, workload pressures, and uncertainty about appro - [AI Assistance for Discretionary Work: Increasing Feedback Provision in Higher Education](https://edtechdev.github.io/aied/articles/ai-assistance-discretionary-feedback/): This field experiment shows that AI-generated feedback drafts can measurably increase the rate and length of feedback that teaching assistants actually deliver to students, without sacrificing perceived usefulness or instructor time-efficiency. The design keeps humans firmly in the loop: TAs could edit or discard every draft, and the intervention still produced significant gains. The finding conne - [Design-Based Research for Developing an AI-Assisted Collaborative Learning Model to Enhance Critical Thinking and Problem-Solving Skills in Higher Education](https://edtechdev.github.io/aied/articles/ai-assisted-collaborative-learning-model-dbr/): **Synthesis:** Design-Based Research for Developing an AI-Assisted Collaborative Learning Model to Enhance Critical Thinking and Problem-Solving Skills in Higher Education - [An exploratory behavioral and electroencephalographic study of artificial intelligence-assisted learning modes in high school students](https://edtechdev.github.io/aied/articles/ai-assisted-learning-modes-eeg/): This study investigates how different modes of AI interaction affect cognitive engagement and learning outcomes in high school students. Using a within-subjects design with 24 students, the researchers compared three conditions: **Auto mode** (AI solves problems independently), **Interactive mode** (student-AI collaboration with scaffolding), and **Manual mode** (no AI assistance). The Interactive - [Mapping the Emerging Curriculum for AI-Assisted Software Engineering via Syllabus Analysis](https://edtechdev.github.io/aied/articles/ai-assisted-se-curriculum-syllabus-analysis-2026/): **Synthesis:** This paper analyses 23 publicly available syllabi from upper-division, credit-bearing university courses that teach AI-assisted software development. The study identifies common curricular themes — prompt engineering, code review with AI, AI-augmented testing, ethical considerations — and maps how different institutions are defining this emerging subject area. Key findings include: - [Smaller, Younger, and More Impactful: How AI-Assisted Writing Transforms Research Teams](https://edtechdev.github.io/aied/articles/ai-assisted-writing-research-teams/): **AI-Assisted Writing Transforms Research Teams** challenges the longstanding "Big Science" trend toward ever-larger teams, showing that AI writing tools enable smaller, younger research teams to produce highly impactful publications. Analyzing 147,074 full-text publications from PLoS and Nature portfolio journals since 2020, the study uses propensity score matching and multiple regression methods - [AI-Assisted Autonomous Learning and Reduced Academic Accomplishment in Vocational Higher Education: The Mediating Role of Hardiness](https://edtechdev.github.io/aied/articles/ai-autonomous-learning-accomplishment-2026/): **Synthesis:** Wang and Zhang (2026) examined how AI-assisted autonomous learning relates to reduced academic accomplishment among 1,264 vocational college students in China, focusing on the mediating role of hardiness (commitment, control, challenge). Using structural equation modeling, they found AI-assisted autonomous learning was negatively associated with hardiness and positively associated w - [Why Put in This Much Effort?": How AI Availability Shapes Students’ Motivation in Introductory Programming](https://edtechdev.github.io/aied/articles/ai-availability-student-motivation/): **Tran, Harper & Price (2026)** examine a pressing motivational paradox in contemporary computing education: the ready availability of AI tools that can complete programming assignments undermines students' willingness to invest effort in developing their own skills. Drawing on self-determination theory, the study identifies how the perception of AI as a 'shortcut' reduces autonomous motivation an - [AI-Driven Tools for Enhancing Campus Well-being: Prevention and Intervention](https://edtechdev.github.io/aied/articles/ai-campus-wellbeing-tools/): This dissertation presents an integrated AI framework for campus well-being spanning prevention (improving feedback collection) and intervention (advancing mental health detection). It represents an important application of llm and generative-ai technologies to student-experience that extends beyond academic learning to holistic student support in higher-ed. - [How AI Is Changing Teaching Workflows](https://edtechdev.github.io/aied/articles/ai-changing-teaching-workflows/): AI saves teachers roughly 30% of lesson preparation time with no measurable quality loss — but whether that *reduces burnout* depends entirely on where the freed-up time goes. The key mechanism is **reallocation, not reduction**: teachers redirect saved hours toward higher-value instructional activities rather than simply pocketing time. This article synthesizes evidence from multiple controlled t - [AI chatbot design principles to enhance the collective efficacy in collaborative learning](https://edtechdev.github.io/aied/articles/ai-chatbot-collective-efficacy-collaborative-learning/): **Synthesis:** AI chatbot design principles to enhance the collective efficacy in collaborative learning - [AI Coaching for Accelerating Human Skill Development with Reinforcement Learning](https://edtechdev.github.io/aied/articles/ai-coaching-rl-skill-development/): This paper explores how an embodied AI agent can act as a scaffolding that accelerates human motor-skill development using adaptive-learning. The authors argue that effective coaching requires dynamically balancing guidance with learner autonomy — too much assistance leads to over-reliance and skill atrophy, while too little leaves learners struggling. - [Artificial Intelligence and Collaborative Learning: Impacts on Creativity, Critical Thinking, and Problem-Solving](https://edtechdev.github.io/aied/articles/ai-collaborative-learning-skills-impacts/): **Synthesis:** Artificial Intelligence and Collaborative Learning: Impacts on Creativity, Critical Thinking, and Problem-Solving - [A systematic review of AI-powered collaborative learning in higher education: Trends and outcomes from the last decade](https://edtechdev.github.io/aied/articles/ai-collaborative-learning-systematic-review/): **Synthesis:** A systematic review of AI-powered collaborative learning in higher education: Trends and outcomes from the last decade - [What AI in Education Needs Next: Lessons from Youth Leaders Across Five Countries](https://edtechdev.github.io/aied/articles/ai-education-global-capacity/): A global perspective on AI in education readiness, framed around the insight that the real bottleneck is human and institutional capacity, not technical access. Based on a WEF (2026) synthesis of youth leader initiatives across the United States, Kenya, China, UAE, and Switzerland. - [AI-Enabled Serious Games: Integrating Intelligence and Adaptivity in Training Systems](https://edtechdev.github.io/aied/articles/ai-enabled-serious-games/): Serious games are widely used for learning and training across domains such as healthcare, defense, and education. This chapter examines how contemporary AI approaches may support real-time instructional adaptation in serious games. - [Using AI in engineering education: a balancing act, driven by clear purpose](https://edtechdev.github.io/aied/articles/ai-engineering-education-balancing-act/): Based on a questionnaire of 100 higher-education engineering students and a critical literature review, examines how students use and perceive LLMs. Students value LLMs for writing support, conceptual clarification, coding assistance, and brainstorming, but express concerns about inaccuracies, bias, overreliance, and academic integrity. Analyzes two dominant metaphors — LLM as 'oracle' and 'tutor' - [A Longitudinal Analysis of Public Discourse on AI Ethics in Education Using Twitter Data](https://edtechdev.github.io/aied/articles/ai-ethics-education-public-discourse/): **Akriti Bagale, Nafisa Mehjabin, Ali Unlu, Aditya Johri, et al. (2026)** - George Mason University; University of Virginia. arXiv preprint. - [Warning About AI Fallibility Increases Help-Seeking in an Intelligent Tutoring System](https://edtechdev.github.io/aied/articles/ai-fallibility-warning-help-seeking/): **Synthesis:** Recent work in Technology-Enhanced Learning and HumanComputer Interaction highlights the importance of transparency and trust calibration in AI-supported learning environments as they pose a risk of hallucinations. In this study, we investigate whether a simple transparency intervention that warns s - [Defining AI Fatigue in Academic Contexts: Dimensions, Indicators, and a Stage-Based Model Using Grounded Theory](https://edtechdev.github.io/aied/articles/ai-fatigue-academic-contexts/): This grounded theory study analyzed open-ended responses from 1,054 university students across three Philippine universities to define **AI fatigue** as a distinct construct — separate from technostress and digital fatigue. The analysis identified **five dimensions**, each with two indicators grounded in participant accounts: **(1) Cognitive Overload** — mental exhaustion from processing AI output - [Using AI-Generated Feedback to Improve Critical Thinking and Writing Proficiency](https://edtechdev.github.io/aied/articles/ai-feedback-critical-thinking-writing-2026/): **Synthesis:** This study developed the Writing Improvement and Smart Evaluation Agent (WISE Agent), an AI feedback tool targeting textual logic and perspective biases in student essays. A three-month intervention with 260 Chinese sixth-grade students found structural optimizations in critical thinking dimensions rather than a uniform increase in total scores. Lower-performing students advanced in - [Making AI-Generated Feedback Matter: From Provision to Student Enactment](https://edtechdev.github.io/aied/articles/ai-feedback-enactment-workflow-2026/): **Synthesis:** Alsaiari et al. (2026) report a large-scale quasi-experimental cohort study (13,037 students; 51,296 student-authored resources) comparing three AI-mediated feedback workflows. Students in the **Enacted Feedback** condition — prompted to select feedback suggestions, evaluate their relevance, and engage in targeted AI dialogue anchored to those selections — showed significantly highe - [Artificial intelligence and feedback in university education: effectiveness and student perceptions](https://edtechdev.github.io/aied/articles/ai-generated-feedback-higher-ed/): This quasi-experimental study directly compares **AI-generated feedback** (two LLMs: **GPT-o4-mini** and **DeepSeek R1**) with **expert human-teacher feedback** in a project-based university course (Assessment & Learning, third-year Primary Teacher Education, University of Padua). The central question is not "is AI feedback worse?" but *under what pedagogical conditions* AI feedback can be a credi - [Student Perceptions and Preferences Regarding AI-Generated Instructional Videos in Computing Education](https://edtechdev.github.io/aied/articles/ai-generated-instructional-videos-computing-ed/): Studies student perceptions of AI-generated instructional videos in computing education. Finds students value personalization and rapid production but express concerns about accuracy and the loss of instructor presence. Identifies clear preferences for hybrid approaches where AI generates draft content that instructors review and refine. - [AI-Generated Interactive Fiction for Educational Use: A Pilot Study of Perceived Comprehensibility, Coherence, and Engagement](https://edtechdev.github.io/aied/articles/ai-generated-interactive-fiction-education-2026/): **Synthesis:** This pilot study (N = 22 STEM higher-education students) evaluates AI-generated interactive fiction as an educational medium. Narrative clarity and length acceptance rated positively, engagement hovered near neutral, and story-content coherence was the weakest dimension — with quiz integration emerging as the main usability bottleneck. The authors derive concrete design implications - [AI-Generated Slides: Are They Good? Can Students Tell?](https://edtechdev.github.io/aied/articles/ai-generated-slides-student-perception/): This study evaluated five generative AI tools for creating instructional slides from instructor-authored course notes: NotebookLM, Claude, M365 Copilot, Cursor, and Claude Code. Educators assessed slides for accuracy, completeness, and pedagogical soundness. - [Studying Circular Motion with an AI-Generated Smartphone Physics Lab](https://edtechdev.github.io/aied/articles/ai-generated-smartphone-circular-motion-lab-2026/): **Synthesis:** Suñer et al. (2026) show that a fully customized, browser-based rotation laboratory can be generated entirely through natural-language prompting of an AI assistant, with no manual coding. Most smartphone physics experiments rely on precompiled sensor apps whose interfaces cannot be tailored to a specific activity, and customized labs previously required programming knowledge beyond - [AI-Generated Traces for Novice Programmers: Learning Effects and Learner Differences in a Multi-Institutional Study](https://edtechdev.github.io/aied/articles/ai-generated-traces-novice-programmers/): Multi-institutional study on Generated Animated Traces (GATs) for CS1. Found that mid-engagement students may experience a performance decrement due to coordination costs (Expertise-Reversal Effect). cs-education, scaffolding, personalized-learning, stem-education, adaptive-learning. - [AI-Guided Learning: Research on Knowledge and Skill Acquisition Support Methods Using Deep Learning Audio-Video Processing Techniques](https://edtechdev.github.io/aied/articles/ai-guided-learning-audiovideo-2026/): **Synthesis:** This dissertation develops an AI-guided learning framework that supports three interconnected stages — Consume, Understand, and Imitate — with three deep-learning systems for audio/video learning. AIxSpeed adapts audio playback speed using speech-recognition confidence; FastPerson produces multimodal video summaries; and Profy supports pronunciation practice from largely unannotated - [Higher Education Must Bridge the AI Gap](https://edtechdev.github.io/aied/articles/ai-higher-ed-bridge-gap/): A Science editorial by University of Illinois Chicago Chancellor Marie Lynn Miranda (April 2026) arguing that higher education has a narrow window to shape AI's distribution equitably. Proposes a three-pillar AI literacy framework: practical fluency, critical understanding, and ethical/professional use. - [The Impact of AI on Work in Higher Education](https://edtechdev.github.io/aied/articles/ai-higher-ed-workforce-survey/): A large-scale survey (n=1,960) by EDUCAUSE (2026) examining how AI is reshaping work in higher education institutions — attitudes, adoption patterns, institutional strategies, risks, and opportunities. - [AI in the Wild: A Large Scale Analysis of Authentic Interactions of College Students with Generative AI](https://edtechdev.github.io/aied/articles/ai-in-the-wild-college/): Karidi, Amir & Roll (2026) present one of the largest empirical analyses to date of authentic (rather than lab-based) interactions between college students and generative AI tools. By analyzing interaction logs at scale, they identify distinct patterns: some students use AI as a llm-powered learning companion for explanation and exploration, while others offload cognitive work entirely — copying o - [Artificial Intelligence as Catalyst and Contested Terrain: Transforming Interior Design Practice, Pedagogy, and Professional Regulation in Malaysia](https://edtechdev.github.io/aied/articles/ai-interior-design-malaysia-2026/): **Synthesis:** This article examines how generative AI and intelligent visualization platforms are reshaping interior design practice in Malaysia, shifting designers from primary form-generators toward critical mediators and curators of machine outputs. It explores the implications for university curricula, arguing that professional education must integrate technical proficiency with critical and - [What Changes When the Interlocutor Is an AI? Interactional Fluency and Linguistic Uptake in L2 Spoken Dialogue](https://edtechdev.github.io/aied/articles/ai-interlocutor-l2-spoken-dialogue/): Scheinberg et al. (2026) analyze 78 university learners of German across four sites completing a counterbalanced spot-the-difference task with both a human peer and a real-time AI partner. Using diarized ASR transcripts, they extract measures of interactional fluency, linguistic uptake, and learner experience. Human dialogue was faster and more balanced with many short turns; AI dialogue resembled - [Unravelling undergraduates' development of evaluative judgments through AI-supported internal feedback](https://edtechdev.github.io/aied/articles/ai-internal-feedback-evaluative-judgments/): **Synthesis:** Unravelling undergraduates' development of evaluative judgments through AI-supported internal feedback - [Using AI-based Learning Assistants in Higher Education: A Large-Scale Descriptive Analysis](https://edtechdev.github.io/aied/articles/ai-learning-assistants-higher-ed-large-scale/): Presents a large-scale descriptive analysis of an AI learning assistant (Syntea) using objective log data from 77,543 higher-education students, characterizing real usage patterns, adoption, and engagement at scale. The work connects to broader debates about how generative-ai systems reshape student-experience and the conditions under which AI support scaffolding rather than undermines learning. I - [Building AI Companions that Prioritise Learning over Performance](https://edtechdev.github.io/aied/articles/ai-learning-companions-framework/): A design framework for LLM-powered educational agents that prioritize durable learning over short-term task performance. Introduced by Khosravi et al. (2026), AI learning companions are defined as adaptive, pedagogically informed agents integrated into learning environments — distinct from both task-oriented LLMs and simple prompted tutors. - [Designing Needs- and Attention-Aware AI Learning Tools for Engineering Education: Insights from Psychological Outcomes](https://edtechdev.github.io/aied/articles/ai-learning-tools-engineering-education-needs/): Survey of 206 engineering students: AI chatbots provide greatest perceived benefit as relief from competence frustration, smaller benefits for autonomy, weakest for relatedness. Baseline motivational states matter more than demographics; inattention moderates how baseline competence and autonomy relate to perceived AI benefits. Offers design principles for engineering-specific AI learning tools. - [Artificial Intelligence in Lifelong Learning: Opportunities and Challenges in Adult Education Policy](https://edtechdev.github.io/aied/articles/ai-lifelong-learning-policy/): Theodora and Tselios (2026) provide a policy-oriented synthesis of AI's dual role in adult and lifelong-learning contexts — as both an enabler of personalized, scalable education and a source of significant equity and governance challenges. Drawing on international policy frameworks, the paper argues that AI integration in adult education requires balanced policies promoting inclusion, transparenc - [AI Literacy Assessment: Self-Reported vs Performance Misalignment](https://edtechdev.github.io/aied/articles/ai-literacy-assessment-misalignment/): Highlights critical misalignment between self-reported AI literacy and actual performance. Teachers overestimate their AI skills by 40% on average. Performance-based assessments correlate better (r=0.72) with classroom AI integration than self-reports (r=0.31). - [AI literacy alone is not enough: Student AI readiness and career adaptability in business and management education](https://edtechdev.github.io/aied/articles/ai-literacy-career-adaptability-business-2026/): **Synthesis:** Testa, Apuzzo, and Pittaway (2026) investigate how AI-related competencies contribute to career adaptability in business and management education. Surveying 339 university students in economics, management, and business programs in Italy, they employ a moderated mediation model examining the relationships among AI literacy, AI readiness, AI self-efficacy, and career adapt-abilities. - [Beyond Tool Adoption: A Practical Five-Stage Developmental Continuum for AI Literacy in Higher Education](https://edtechdev.github.io/aied/articles/ai-literacy-continuum-higher-education/): Proposes a five-stage developmental continuum (Not Engaged, Uncritical Use, Informed Use, Critical Evaluation, Improvement) for AI literacy at NC State; the continuum doubles as a diagnostic tool for moving students beyond fluent-but-uncritical tool adoption. Found that reaching higher stages requires discipline-embedded experiences. ai-literacy, higher-ed, ethics, faculty-development, student-exp - [Programming Language Policy as an AI Literacy Equity Problem: A 15-Nation Comparative Analysis](https://edtechdev.github.io/aied/articles/ai-literacy-equity-programming-policy/): Across 15 nations, the paper examines how secondary computer-science education embeds AI literacy into general-track subjects (Digital Literacy, ICT, TIC, SNT) rather than specialized tracks, creating structural inequities in who develops AI capability. The comparative analysis shows that policy choices about which programming language and subject bears 'universal' AI literacy determine differenti - [The AI Literacy Heptagon: A Structured Approach to AI Literacy in Higher Education](https://edtechdev.github.io/aied/articles/ai-literacy-heptagon-2026/): **Synthesis:** Hackl, Müller, and Sailer (2026) present the AI Literacy Heptagon, a structured seven-dimensional framework for AI literacy (AIL) in higher education, developed through an integrative literature review of publications from 2021–2024. The framework synthesizes seven core dimensions — technical knowledge and skills, application proficiency, critical thinking ability, ethical awareness - [AI Literacy for Legal Translation: Developing Digital Resilience](https://edtechdev.github.io/aied/articles/ai-literacy-legal-translation-2026/): **Synthesis:** Proposes a four-component AI literacy framework for legal translation professionals: conceptual AI knowledge, technical operational skills, critical evaluation competencies, and ethical governance awareness. Argues generative AI extends rather than replaces professional translation competence. Identifies linguistic, technical, legal, ethical and cognitive risks of AI in legal transl - [AI Literacy: An Exercise in Power-Knowledge](https://edtechdev.github.io/aied/articles/ai-literacy-power-knowledge/): Argues existing AI literacy frameworks, dominated by technical competency and responsible-use principles, enforce a consumer orientation toward AI rather than fostering genuine epistemic agency. Draws on Foucault's power-knowledge framework to propose a critical AI literacy that empowers learners to shape and challenge AI systems rather than merely use them. - [AI-Integrated Learning Management System for Middle School: A Longitudinal Study of Learning Outcomes](https://edtechdev.github.io/aied/articles/ai-lms-middle-school-longitudinal/): **Misan Paul Etchie, Taiwo Olutosin** — cs.CY, cs.AI, cs.HC - [Is AI making us stupid?](https://edtechdev.github.io/aied/articles/ai-making-us-stupid/): A 3-page **perspective** (opinion/review, not an empirical study) addressing whether AI use erodes human cognition. The authors' answer: **not inherently — but the risk is real and follows the cognitive-psychology principle of *cognitive offloading*.** When people delegate reasoning, writing, memory, or problem-solving to AI, they forgo the mental practice that builds and maintains those capacitie - [AI Tools Scaffolding Metacognition in STEM](https://edtechdev.github.io/aied/articles/ai-metacognition-stem-review/): A bibliometric–systematic review of AI tools in STEM education: - [AI as a Partner in Learning about, Doing, and Engaging with Science: Vigilance as the Key to Productive Augmentation](https://edtechdev.github.io/aied/articles/ai-partner-science-epistemic-vigilance/): Argues that epistemic vigilance — the human evaluation of AI output calibrated to how far a fallible source can be trusted — is the binding constraint on productive augmentation. AI's fluent, confident prose reads as trustworthy whether or not it is, making evaluation harder. Vigilance sets how deeply a claim is processed and is thus the precondition for learning with AI. Design factors (prompts, - [Design Principles and Observable Indicators for AI-Enabled Pedagogical Accompaniment: Evidence from the Amico Dual-Mode Prototype in Italy and China](https://edtechdev.github.io/aied/articles/ai-pedagogical-accompaniment-amico/): Benedetti (2026) introduces a theoretically grounded framework for AI-enabled pedagogical accompaniment that explicitly centers human agency — an approach described as "human-in-command" rather than merely human-in-the-loop. The Amico prototype embodies five design principles: transparency of system identity and limits, scaffolding toward human contact, maieutic questioning, prevention of dependen - [Faculty Orientations Shape Adoption of AI in Research and Teaching](https://edtechdev.github.io/aied/articles/ai-pedagogical-orientation/): A mixed-methods survey of 90 STEM faculty in the RCSA Cottrell community identified a coherent latent construct — **AI pedagogical orientation** — that strongly predicts AI adoption across research, teaching, and professional activities. This orientation reflects deep beliefs about AI's role in disciplinary thinking, learning, and expertise development — not merely positive or negative sentiment. - [AI Peer Feedback Systems](https://edtechdev.github.io/aied/articles/ai-peer-feedback-systems/): Peer feedback develops critical reflection and evaluative judgment, yet: - [Preparing Students for AI-Powered Materials Discovery: A Workflow-Aligned Framework for AI Literacy, Equity, and Scientific Judgment](https://edtechdev.github.io/aied/articles/ai-powered-materials-discovery-ai-literacy/): This paper presents a workflow-aligned framework for preparing students to use AI in materials discovery. The authors argue that in materials science, the limiting factor is no longer only algorithmic capability but **human-AI collaboration competence**. Students need to develop scientific judgment about when to trust AI predictions and how to integrate them into research workflows. - [Exploring Fraction Comprehension and Interest in Elementary Education Through AI-Powered Personalized Learning](https://edtechdev.github.io/aied/articles/ai-powered-personalized-learning-elementary-fractions-2026/): **Synthesis:** Examines AI-powered personalized learning in elementary fraction instruction through a systematic review, quantitative study (N=120), and qualitative teacher interviews. Found that AI-adaptive platforms significantly improved fraction comprehension for students with math learning difficulties compared to traditional instruction. AI personalization increased student interest and enga - [Position: Adopting AI in Practice Does Not Guarantee the Productivity Boost](https://edtechdev.github.io/aied/articles/ai-productivity-moderation/): This ICML 2026 position paper argues that adopting AI in organizational practice does not automatically yield productivity gains — human and environmental factors critically moderate the relationship. Drawing on the partial equilibrium model of Gries and Naudé (2022), it identifies five key moderators that can attenuate or negate productivity benefits. - [Toward Accessible Psychotherapy Training Using AI-Driven Interactive Patient Avatars](https://edtechdev.github.io/aied/articles/ai-psychotherapy-training-avatars/): AI-driven interactive patient avatars for psychotherapy training provide accessible, repeatable practice with measurable skill improvement in evidence-based therapy techniques. - [AI-based scoring systematically underestimates conceptual understanding of linguistically weak students' explanations in physics](https://edtechdev.github.io/aied/articles/ai-scoring-language-bias-physics/): **Authors:** Markus S. Feser, Paul L. Tschisgale (Leibniz Institute for Science and Mathematics Education, Kiel, Germany) - [Why does AI unlock new possibilities in STEM education? A Bibliometric Analysis of Trends and Future Agenda](https://edtechdev.github.io/aied/articles/ai-stem-bibliometric-trends/): STEM education faces challenges in personalization and interdisciplinary integration. AI technology has brought new possibilities, but the mechanisms by which AI reshapes the STEM education ecosystem require systematic investigation. This study employs bibliometric methods to analyze 242 publications from 2015-2025, constructing knowledge maps to reveal the evolutionary trajectory. The findings sh - [AI-Driven Analytics of Team-Teaching Talk: Acoustic Patterns across Experience, Cohorts and the Learning Design](https://edtechdev.github.io/aied/articles/ai-team-teaching-talk-analytics/): **Yuchen Liu, Roberto Martinez-Maldonado, Riordan Alfredo, Paola Mejia-Domenzain, Dwi Rahayu, Sadia Nawaz** — AIED 2026 — cs.HC, cs.AI - [AI tools in Arab University English classrooms: Looking back and forward](https://edtechdev.github.io/aied/articles/ai-tools-arab-english-classrooms/): This paper aims to synthesize empirical research on AI tools used to support English as a second/foreign language (EL2) learners in Arab University classrooms (AUCs) between Jan 1st 2023 and Aug 31st 2025. We utilized 3 large datasets, namely Google Scholar, Web of Science, and Scopus as the data sources. Using PRISMA-guided searches across these well-known databases, we included only published ar - [AI literacy-related domains and AI-TPACK readiness among preservice mathematics teachers: A factor-informed structural equation modelling study](https://edtechdev.github.io/aied/articles/ai-tpack-preservice-math-teachers/): **Synthesis:** AI literacy-related domains and AI-TPACK readiness among preservice mathematics teachers: A factor-informed structural equation modelling study - [Modeling AI-TPACK in Practice: Insights from Teachers'' Multi-Agent Workflow Design](https://edtechdev.github.io/aied/articles/ai-tpack-teacher-multi-agent-workflow/): This study investigates how teachers design multi-agent instructional workflows and identifies three distinct **teacher archetypes** that emerge from behavioral log analysis of 61 in-service teachers: - [PromptDecipher: Supporting AI Tutor Authoring Through Editable Simulated Interactions](https://edtechdev.github.io/aied/articles/ai-tutor-authoring-promptdecipher/): - teacher-role ## Connected Articles - [The Missing Evaluation Axis: What 10,000 Student Submissions Reveal About AI Tutor Effectiveness](https://edtechdev.github.io/aied/articles/ai-tutor-behavioral-evaluation/): A framework for evaluating AI tutoring systems that extends beyond pedagogical quality of feedback to measure what students actually *do* with that feedback — whether they act on it and whether they apply it correctly. Proposed by Niousha et al. (2026) based on analysis of 10,235 real student code submissions. - [AI Tutor Effectiveness Review](https://edtechdev.github.io/aied/articles/ai-tutor-effectiveness-review/): Zerkouk, Mihoubi & Chikhaoui (2025) systematically analyzed qualified studies from 2010–2025 across: - [AI Tutor Safety and Pedagogical Harms](https://edtechdev.github.io/aied/articles/ai-tutor-safety-harms/): Conventional LLM safety benchmarks focus on toxic outputs, jailbreaks, and bias. In education, the primary risks are quieter: - [Methodologies for Improving the Quality of AI Tutoring in K-12 Education](https://edtechdev.github.io/aied/articles/ai-tutoring-quality-k12-methodologies-2026/): **Synthesis:** Udeshi et al. (2026), the team behind **Khanmigo** (Khan Academy's K-12 AI tutor, launched 2023), describe the metrics they use to measure AI tutoring quality and student engagement, along with the live experiments that have moved those metrics. Given that LLMs are opaque black boxes, they argue robust evaluation and live experimentation are essential. The paper highlights changes a - [Artificial Intelligence in UK Higher Educational Policy and Institutional Decision Making](https://edtechdev.github.io/aied/articles/ai-uk-higher-education-policy-2026/): **Synthesis:** This systematic literature review examines how AI is positioned in UK higher-education policy and its influence on institutional pedagogical decision making, finding that AI integration is accelerating but fragmented, with a gap between policy ambitions and institutional capacity and disparities between teaching-led and research-intensive universities. - [From AI Use to Critical Thinking Among Medical Students: A Moderated Mediation Perspective on Cognitive Load and Self-Regulated Learning](https://edtechdev.github.io/aied/articles/ai-use-critical-thinking-medical-students-2026/): **Synthesis:** Arshad et al. (2026) examined how AI-based educational technology influences critical thinking among 480 undergraduate medical students in Pakistan, using a cross-sectional design and Hayes' PROCESS Model 14. They found that AI use was positively associated with critical thinking and self-regulated learning, while cognitive load negatively related to both. Cognitive load partially m - [Beyond Output Metrics: Reframing AI-Assisted Vocal Pedagogy Through Human Learning and Educational Value](https://edtechdev.github.io/aied/articles/ai-vocal-pedagogy-2026/): **Synthesis:** Li (2026) presents a conceptual Perspective arguing that AI-assisted vocal pedagogy should be evaluated not by how precisely AI measures vocal output (pitch, stability, timing) but by how AI-generated evidence becomes meaningful for human learning — how learners interpret feedback, regulate practice, sustain motivation, and develop trust in teacher-guided processes. The article prop - [Artificial intelligence in vocational education and training: A systematic review of educational purposes, theoretical conceptualizations, and empirical effectiveness](https://edtechdev.github.io/aied/articles/ai-vocational-education-training-review/): **Authors:** Viola Deutscher, Herbert Thomann, Olga Zlatkin-Troitschanskaia, Ulrike Weyland, Stephan Abele, Amory H. Danek, Samuel Greiff, Andreas Rausch, Susan Seeber, Jürgen Seifried, Esther Winther **Source:** Computers and Education: Artificial Intelligence, Vol 11, 100628 — Open Access (CC BY 4.0) First systematic review of AI in vocational education and training, identifying 26 empirical stu - [From Planning to Revision: How AI Writing Support at Different Stages Alters Ownership](https://edtechdev.github.io/aied/articles/ai-writing-support-stage-ownership-2026/): Gero, Long, Schnitzler & Dhillon (2026, DIS '26) ran a between-subjects essay study (n = 253) showing that **where** AI support enters the writing process determines how much students feel they own the work: any AI assistance decreased ownership, but planning support cost the least while drafting support cost the most. The mechanism is AI-contributed text and ideas — and there is a genuine **quali - [AiAWE: An Open-Source LLM Automated Writing Evaluation System Using LoRA-Adapted Instruction-Tuned Models](https://edtechdev.github.io/aied/articles/aiawe-automated-writing-evaluation/): Gayed presents **AiAWE**, an open-source automated-grading (AWE) system that scores argumentative essays using a LoRA-adapted instruction-tuned llm (Gemma-3-27B-it). Using a proprietary ETS dataset of 480 TOEFL Independent Writing essays (120 training, 360 evaluation), the system achieves: - [AICoFe: Implementation and Deployment of an AI-Based Collaborative Feedback System for Higher Education](https://edtechdev.github.io/aied/articles/aicode-collaborative-feedback-system/): AICoFe orchestrates a multi-LLM pipeline using GPT-4.1-mini, Gemini 2.5 Flash, and Llama 3.1 to synthesize quantitative rubric data and qualitative observations into actionable feedback for higher education students. The key innovation is a **teacher-in-the-loop mediation workflow**: educators use specialized Learning Analytics dashboards to curate and refine AI-generated feedback drafts before de - [The Environmental Cost of LLMs in AIED: Reporting and Practices](https://edtechdev.github.io/aied/articles/aied-carbon-footprint-reporting/): **Sabrina C. Eimler, Lukas Erle, Daniel Flood, Aditi Haiman, Luca Häckert, André Helgert, Lachlan McGinness, Büsra Yapici** - [AIED's Unfinished Mission: Centering Agency and Motivation in the Age of Effortless Bypass](https://edtechdev.github.io/aied/articles/aied-unfinished-mission-bypass/): The widespread availability of general-purpose AI that can perform complex cognitive tasks threatens to undermine education at scale. This effortless bypass dilemma sharpens a challenge AIED has long engaged with but must now confront directly: ensuring learners choose effortful engagement when easier alternatives are available to complete learning tasks. In this paper, I argue that AIED's longsta - [AISSA: AI-based Student Slides Analysis Tool for Academic Presentations](https://edtechdev.github.io/aied/articles/aissa-slides-analysis/): A web-based system that uses LLMs and Learning Analytics dashboards to provide automated, rubric-based feedback on student presentation slides. Developed by Becerra et al. (2026), AISSA addresses the scalability challenge of providing timely formative feedback in large university courses. - [Perceptions And Acceptance of Artificial Intelligence in Science Education Programmes: Voices of Pre-Service Science Teachers](https://edtechdev.github.io/aied/articles/amponsah-ai-acceptance-science-teachers-2026/): **Synthesis:** Amponsah, Adu-Boahen, Commey-Mintah, Kumassah, Ayittey & Nketsiah (2026) survey 380 pre-service science teachers in Ghana using UTAUT and TPB frameworks, finding generally positive AI perceptions but a gap between intention and actual classroom use. ChatGPT emerged as the most used AI tool, with research, content explanation, and lesson planning as primary purposes. - [When Agents Learn to Be You: Benchmarking Privacy Leakage, Impersonation Risk, and Defenses in Persona Skills](https://edtechdev.github.io/aied/articles/antiskillbench-persona-skills-privacy-2026/): **When Agents Learn to Be You: Benchmarking Privacy Leakage, Impersonation Risk, and Defenses in Persona Skills** — Introduces AntiSkillBench with 7,500 persona-grounded dialogue traces from 50 behaviorally rich profiles. Evaluates skill-level privacy leakage, agent-level attribute disclosure, and behavioral impersonation across three skill-distillation strategies... privacy agentic-ai student-exp - [ANVIL: Analogies and Videos for Lecturers](https://edtechdev.github.io/aied/articles/anvil-ai-educational-animations/): Noviello, Birillo, and Migut (2026) present ANVIL, an end-to-end multimodal generation pipeline for educational content — one of the first systems to automate the full journey from concept definition to rendered instructional animation. The four-stage pipeline (analogy generation, screenplay compilation, animation code generation with automated repair) represents a significant advance in AI-genera - [ASE-26: A Curriculum for Agentic Software Engineering as a Discipline](https://edtechdev.github.io/aied/articles/ase-26-agentic-software-engineering-curriculum/): Formalizes Agentic Software Engineering (ASE) as a distinct discipline. Proposes a 21-module curriculum focused on the "evolution of intent" and practitioner discipline required to manage agents rather than just writing code. This work emphasizes that ai-literacy is a developmental capacity requiring structured scaffolding and prompt-engineering discipline. It connects to the need for curriculum-d - [Assessment in Team Problem-Solving Exercises in Computing Education](https://edtechdev.github.io/aied/articles/assessment-team-problem-solving-computing-education/): Tabletop exercises (TTXs) let learner teams rehearse high-stakes workplace tasks such as cybersecurity incident response, but their open-ended, collaborative nature makes formative-assessment difficult: teams often receive delayed or incomplete feedback. This full research-to-practice paper compares assessment methods that exploit the action and communication logs captured by TTX platforms to eval - [ASTRA: A Scalable Next-Generation ATCO Training Simulator with Autonomous Simpilots](https://edtechdev.github.io/aied/articles/astra-atco-training-simulator/): ASTRA uses autonomous AI sim-pilots for scalable air traffic control training; reduces dependency on human role-players while maintaining realistic scenario complexity. - [Analysis and Prediction of At-Risk Students Using Machine Learning Algorithms](https://edtechdev.github.io/aied/articles/at-risk-students-ml-prediction/): Gheisari and Salarian (2026) apply supervised machine learning classification to identify at-risk students before they withdraw from higher education programs. The study evaluates Logistic Regression, Random Forest, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) using academic performance, demographic data, and enrollment records. Logistic Regression and linear SVM achieved the highe - [Authentic Assessment](https://edtechdev.github.io/aied/articles/authentic-assessment/): Wiggins (1990) proposed AA as a counterbalance to standardised tests: direct examination of "student performance on worthy intellectual tasks." - [From authentic products to authenticated processes: authentic assessment in AI-rich higher education](https://edtechdev.github.io/aied/articles/authentic-products-authenticated-processes-2026/): Generative AI has not created the need for authentic assessment — it has made weaknesses in assessment design harder to ignore. Polished products can now be generated or substantially mediated by tools, so **product resemblance is an increasingly unreliable signal of capability**. Tsiligkiris calls this risk **construct substitution**: an AI-generated or AI-mediated product is attributed to the st - [The Effect of High-Frequency, Automatically-marked Formative Assessments on Student Outcomes in A-Level Sciences](https://edtechdev.github.io/aied/articles/automated-formative-assessments-a-level-sciences/): This quasi-experimental mixed-methods longitudinal study (N=142) deploys a fully automated marking pipeline for handwritten mock examinations in A-Level sciences, removing the human-marking bottleneck that normally caps the frequency of formative mocks. High-frequency, automatically-marked formative-assessment cycles were associated with improved student outcomes, providing field evidence for the - [Automated Grading of Linux/Bash Examinations Using Large Language Models](https://edtechdev.github.io/aied/articles/automated-grading-linux-bash-examinations-large-language-models/): **Manuel Alonso-Carracedo, Ruben Fernandez-Boullon, Pedro Celard, Francisco J. Rodriguez-Martinez, Lorena Otero-Cerdeira (2026)** - [A Survey of Automated Presentation Coaching: Systems, Methods, and Open Challenges](https://edtechdev.github.io/aied/articles/automated-presentation-coaching/): This survey provides the first systematic review of automated presentation coaching systems, organizing them along a five-dimensional task taxonomy: segmental pronunciation, lexical stress, suprasegmental prosody, pacing, and content faithfulness. The authors review systems spanning pronunciation tutors, fluency and prosody coaches, multimodal trainers, and conference Q&A practice tools, identifyi - [Automatic Short Answer Grading with LLMs](https://edtechdev.github.io/aied/articles/automatic-short-answer-grading/): Automatic Short Answer Grading (ASAG) is never perfect. Upper bounds on accuracy arise from: - [Awareness of Technological Isomorphism: AI in Elementary Math](https://edtechdev.github.io/aied/articles/awareness-technological-isomorphism/): Introduces a novel core concept, **"Awareness of Technological Isomorphism,"** defined as a student's metacognitive realization that their own mathematical cognitive operations (observing trends, inducing patterns, making predictions) share an underlying logical structure with AI technical operations (pattern recognition, predictive modeling). This awareness facilitates transfer-of-learning. - [AICoFE: AI-Powered Feedback System](https://edtechdev.github.io/aied/articles/becerra-aicofe-feedback-2026/): **AICoFE** (AI-based Collaborative Feedback) is a multi-LLM feedback generation system for higher education that combines independently fine-tuned language models with **teacher-in-the-loop mediation**, producing diverse feedback perspectives while preserving pedagogical authority through Learning Analytics dashboards. - [Pragmatic users and skeptical nonusers: A qualitative typology of ChatGPT adoption in physics education](https://edtechdev.github.io/aied/articles/becker-chatgpt-typology-physics-2026/): **Synthesis:** Becker, Bauer, Schrader, Bitzenbauer & Veith (2026) analyze 1,189 survey responses from physics students using qualitative content analysis and latent class analysis, identifying two distinct user profiles: 70% are "Pragmatic Users" who use ChatGPT for scaffolding despite awareness of inaccuracies, and 30% are "Skeptical Non-Users" who avoid it over overreliance concerns. Both group - [Behaviorally Adaptive Visual Diversion for Inclusive and Resilient Digital Assessment Delivery](https://edtechdev.github.io/aied/articles/behaviorally-adaptive-visual-diversion-assessment-2026/): **Behaviorally Adaptive Visual Diversion for Inclusive and Resilient Digital Assessment Delivery** — Proposes BAVD, a theoretical framework for adaptive visual diversion in digital assessment that resists screen-capture cheating while accommodating learners with visual-processing accommodations. Formulates the model using coupled dynamical systems (... assessment accessible-learning privacy academ - [Beyond Detection: redesigning authentic assessment in an AI-mediated world](https://edtechdev.github.io/aied/articles/beyond-detection-authentic-assessment-ai-2025/): Detection-led responses face well-documented limits: validity and fairness failures (bias against non-native writers), notable error rates, erosion of trust, and distraction from assessment design. Detection should be a **limited, situational tool — not a strategy of first resort**. The constructive question is not "how do we prevent students from using AI?" but "how do we enable them to use it th - [Beyond MOOCs: How technical and structural factors shape learner engagement, retention and inclusivity across online learning platforms](https://edtechdev.github.io/aied/articles/beyond-moocs-how-technical-and-structural-factors-shape-learner-engagement-reten/): **Synthesis:** This study examines the critical influence of technical and structural factors on learner Engagement, Retention and Inclusivity (ERI) in MOOCs and other large-scale online learning platforms. Using a novel mixed-methods 2TS method, analysis of over 226,000 user reviews from six platforms (Coursera, edX, Udemy, Alison, uLesson, Khan Academy) found that technical instability, limited - [From Execution to Education: A Bloom-Aligned Framework for Measuring Educational Control in LLMs](https://edtechdev.github.io/aied/articles/bloom-aligned-educational-control-llms/): Introduces a Bloom-aligned framework for measuring 'educational control' in LLMs: the ability to preserve a task's instructional intent while shifting its cognitive demand toward higher-order Bloom levels, offering a metric for evaluating whether AI assistance scaffolds or shortcuts learning. The work connects to broader debates about how generative-ai systems reshape student-experience and the co - [Beyond Rephrasing: Book-Level Organization Improves Synthetic Textbook Data for Mid-Training](https://edtechdev.github.io/aied/articles/book-level-synthetic-textbook-organization/): Studies how organizing synthetic content into coherent book-level documents affects language model training, moving beyond local rewriting. Presents a scalable synthesis pipeline that retrieves source material, clusters it into topical units, and plans hierarchical textbook structures. Shows book-level organization significantly outperforms isolated content generation for educational knowledge acq - [Bots and Blocks: Presenting a Project-Based Approach for Robotics Education](https://edtechdev.github.io/aied/articles/bots-blocks-project-based-robotics-education-2026/): **Synthesis:** Geger, Briechle, and Rausch (2026) propose a project-based learning approach for teaching robotics in higher education, arguing that classic study programs often fail to prepare students for industry work because of a lack of practical experience caused by solely theoretical lecturing. They present a framework for an agile, semester-spanning project where students learn to work with - [WIP: Bridging the Gap Between Instructional Design and Pedagogical Use: A Framework for Mathematics Educators](https://edtechdev.github.io/aied/articles/bridging-instructional-design-framework-math/): Castillo Ventura et al. (2026) address the gap between instructional design of digital mathematics resources and their pedagogical use in classrooms. Their work-in-progress framework translates learning theory principles into observable pedagogical variables structured as metadata, enabling teacher-support systems to characterize digital resources for mathematics education. Drawing on a literature - [A New Direction for Students in an AI World: Prosper, Prepare, Protect](https://edtechdev.github.io/aied/articles/brookings-ai-students-report/): A yearlong global "premortem" by the Brookings Center for Universal Education (2026) examining generative AI's risks and benefits for students. Based on 500+ interviews across 50 countries, 400+ studies reviewed, and a Delphi panel. - [When AI Is Wrong on Purpose: How Students Respond to Buggy GenAI Code](https://edtechdev.github.io/aied/articles/buggy-genai-code-student-responses/): As generative AI becomes central to software development, CS education is shifting toward prompt-centered workflows where students describe intended behavior in natural language to elicit code. But professional practice demands careful review of GenAI output that may look correct yet harbor subtle faults — a challenge in CS1, where current models solve tasks correctly and dull students' incentive - [Calibrating Trustworthiness: Co-Designing Metrics and Visualizations for Evaluating LLMs in Education](https://edtechdev.github.io/aied/articles/calibrating-trustworthiness-llm-education-2026/): **Calibrating Trustworthiness: Co-Designing Metrics and Visualizations for Evaluating LLMs in Education** — Longitudinal co-design with learning engineers building an LLM-powered digital textbook. Co-constructed five trustworthiness metrics with 20 measures tailored to pedagogical use. Designed visualizations mapping trustworthiness violations onto LLM res... llm ai-ed-evaluation over-reliance hum - [The care-full craft of feedback in an age of generative AI](https://edtechdev.github.io/aied/articles/care-full-feedback-genai/): A conceptual/position paper arguing that feedback in an age of GenAI must be understood as **"matters of care"** — ethical, relational practices rather than information transmission. It builds on a ten-principle **Manifesto for Feedback in the Age of GenAI** (Winstone et al. 2025, Copenhagen Feedback Symposium) and distils **four core values** for integrating GenAI into a multimodal feedback lands - [Artificial intelligence assisted design of a novel cooperative learning technique for higher education](https://edtechdev.github.io/aied/articles/ccct-cooperative-learning-technique/): **Synthesis:** Artificial intelligence assisted design of a novel cooperative learning technique for higher education - [Chat Debugging: An Exploratory Study of Human-AI Collaboration to Debug Analog Circuits](https://edtechdev.github.io/aied/articles/chat-debugging-human-ai-collaboration-circuits/): **Synthesis:** This exploratory study investigates how undergraduates use llm to debug malfunctioning analog circuits under exam conditions, identifying both promising human-ai-collaboration and critical limitations. Through thematic analysis of student chat logs, the authors find that off-the-shelf LLMs offer considerable domain knowledge and sensible debugging suggestions, yet struggle with 2D/3 - [WIP: Chat-Debugging: Large Language Model as a Hardware Debugging Assistant](https://edtechdev.github.io/aied/articles/chat-debugging-llm-hardware-education-2026/): **Synthesis:** Work-in-progress exploring LLMs as debugging assistants for physical hardware lab courses. Proposes 'Chat-Debugging' where students interact with an LLM to diagnose circuit faults. Aims to reduce frustration and improve debugging skill development. Initial prototype tested in an undergraduate hardware course; preliminary results suggest LLM assistance helps students identify faults - [ChatGPT Critical and Creative Thinking: Systematic Review](https://edtechdev.github.io/aied/articles/chatgpt-critical-creative-thinking-review/): Li, Cui & Hagedorn (2026) PRISMA-review **67 empirical studies (2022–2025)** on ChatGPT and university students' critical-thinking and creative thinking: effects are contingent on **pedagogical framing**, not the tool itself (generative-ai). - [Students' engagement with ChatGPT feedback: implications for student feedback literacy in the context of generative artificial intelligence](https://edtechdev.github.io/aied/articles/chatgpt-feedback-engagement-genai/): A qualitative study of **16 undergraduates** at a Hong Kong teacher-education university who used **ChatGPT 3.5** to obtain feedback on IELTS writing tasks. Data came from unobtrusive screen-recorded observations plus stimulated-recall interviews. The study extends the traditional tripartite model of feedback engagement (cognitive, affective, behavioural) to a **four-dimensional model adding metac - [ChatGPT-generated help produces learning gains equivalent to human tutor-authored help on mathematics skills](https://edtechdev.github.io/aied/articles/chatgpt-hints-human-tutor-learning-gains-2024/): Pardos & Bhandari (2024) report a randomized efficacy study (N=274) comparing ChatGPT-generated hints to human tutor-authored hints and a no-help control across four mathematics subject areas. Only the ChatGPT condition produced statistically significant learning gains versus control, with no significant difference between ChatGPT and human-authored hints — and ChatGPT's 32% raw hint-error rate wa - [Little Impact of ChatGPT Availability on High School Student Test Score Performance](https://edtechdev.github.io/aied/articles/chatgpt-impact-high-school-tests/): This paper uses a clever identification strategy: measure the **seasonal drop in ChatGPT activity during non-school summer months** (2023 and 2024). Areas with larger summer dropoffs have heavier school-related AI use. The author then examines whether higher AI-use areas show different test score trends. - [Pedagogical Promise and Peril of AI: A Text Mining Analysis of ChatGPT Research Discussions in Programming Education](https://edtechdev.github.io/aied/articles/chatgpt-programming-education-text-mining/): This book chapter presents a **text mining analysis** of how scholarly literature frames ChatGPT's role in programming education. Using term frequency analysis, phrase pattern extraction, and topic modeling, the authors identify four dominant themes: pedagogical implementation, student-centered learning, AI infrastructure, and assessment design. - [Child Safety in Generative AI: An Expert-Guided and Incident-Grounded Evaluation Framework](https://edtechdev.github.io/aied/articles/child-safety-genai/): **Haein Kong** — HEAL Workshop at CHI 2026, submitted 1 Jul 2026 - [Anchor Is the Key: Toward Accessible Automated Essay Scoring with Large Language Models Through Prompting](https://edtechdev.github.io/aied/articles/choi-anchor-aes-prompting-2025/): **Synthesis:** Choi, Tate, Ritchie, Nixon & Warschauer (2025) investigate the most practical approach to LLM-based automated essay scoring — prompting — and find that providing anchor papers (example essays with scores) significantly improves LLM-human agreement, bringing it close to human-human scoring reliability. GPT-4o mini achieves comparable results to GPT-4o at substantially lower cost, mak - [AI-Generated Lesson Plans in Civic Education](https://edtechdev.github.io/aied/articles/civic-education-ai-lesson-plans/): An analysis of 310 AI-generated lesson plans (2,230 individual activities) produced by ChatGPT (GPT-4o), Gemini (1.5 Flash), and Copilot (GPT-4 based) for all 53 Massachusetts eighth-grade civics standards. Each standard received two prompts: a basic "write a lesson plan" and a "highly interactive" variant. - [CLARA: An AI-Augmented Analytics Dashboard for Collaboration Literacy](https://edtechdev.github.io/aied/articles/clara-collaboration-literacy-dashboard/): - learning-analytics - intelligent-tutoring - rag ## Connected Articles - [Coauthorship integrity: Reconceptualising assessment validity for the age of generative artificial intelligence](https://edtechdev.github.io/aied/articles/coauthorship-integrity-reconceptualising-assessment-validity-for-the-age-of-gene/): **Synthesis:** This paper addresses concerns that students use GenAI to submit texts they do not understand, adopting an assessment validity lens. It proposes Coauthorship Integrity as a new conceptual source of validity evidence—violated when students submit AI-generated content they do not understand. The paper reports progress on an "AI Viva," a conversational agent engaging students in hybrid - [Code as Anchor, Memory and Metaphor as Support: Learner Experiences with Multi-View Visualizations](https://edtechdev.github.io/aied/articles/code-anchor-multi-view-visualization/): **Naaz Sibia, Jessica Wen, Amber Richardson, Yashika Jain, Khushi Malik, Bogdan Simion, Carolina Nobre, Angela Zavaleta Bernuy, Andrew Petersen, Michael Liut** (2026). ICER 2026 - [CODE-GEN: A Human-in-the-Loop RAG-Based Agentic AI System for Multiple-Choice Question Generation](https://edtechdev.github.io/aied/articles/code-gen/): **A dual-agent RAG-based system for generating and validating coding comprehension MCQs**, evaluated by 6 SMEs across 7 pedagogical dimensions (N=288 questions, 2,016 rating pairs). AI excels at criteria-matching and computational verification (concept alignment 98.6%, code validity 95.5%), but human expertise remains essential for distractor quality (79.9%) and pedagogically rich feedback — provi - [Combating Harms of Generative AI in CS1 with Code Review Interviews and a Flipped Classroom](https://edtechdev.github.io/aied/articles/code-review-genai-cs1/): Oral code reviews paired with a flipped classroom represent a pragmatic harm-reduction approach to generative AI in CS education. Rather than banning LLMs, Fowles et al. (2026) designed weekly formative assessments where students must explain their submitted code regardless of its origin. Over three semesters at Utah State University, keystroke logs confirmed significantly higher AI usage (increas - [Codify: An Intelligent Socratic Tutoring System for Programming Education](https://edtechdev.github.io/aied/articles/codify-socratic-programming-tutor/): 📄 DOI: 10.32473/flairs.39.1.141554 - [Codify: An Intelligent Socratic Tutoring System for Programming Education](https://edtechdev.github.io/aied/articles/codify-socratic-tutoring-programming/): Codify (also referred to as "AI Tutor") is a web-based intelligent-tutoring platform for programming education that integrates conversational AI, adaptive assessment, and learning analytics. It leverages **LLMs deployed via AWS Bedrock** with a **Socratic teaching methodology** that promotes discovery-based learning over direct answer generation — students are guided through questions and hints ra - [Cognitive Agent Compilation for Explicit Problem Solver Modeling](https://edtechdev.github.io/aied/articles/cognitive-agent-compilation/): **Cognitive Agent Compilation (CAC)** is a framework that uses a strong teacher LLM to compile problem-solving knowledge into an explicit, inspectable target agent. Unlike end-to-end LLM tutoring approaches, CAC separates the agent into three components: - [The Tragedy of the Cognitive Commons: How AI Could Disrupt the Regeneration of Professional Expertise](https://edtechdev.github.io/aied/articles/cognitive-commons-ai-expertise-regeneration/): **Nolan Lovett** — Human Resource Development Review (author accepted manuscript, 2026). - [Profiling cognitive offloading in LLM-mediated synthesis writing: Volume vs. content](https://edtechdev.github.io/aied/articles/cognitive-offloading-llm-synthesis-writing/): **Oleksandra Poquet, Mani Shankar Nanduri, Maria Ximena Salinas Loyer, Matthias Stadler, Michael Sailer, Jelena Jovanovic** — Accepted at EC-TEL 2026 — cs.HC, cs.ET - [Cognitive offloading and the speedup illusion in human-AI interaction](https://edtechdev.github.io/aied/articles/cognitive-offloading-speedup-illusion/): This preregistered large-scale study (N = 1,237) investigates whether people are well-calibrated in estimating the time savings from AI assistance on simple cognitive tasks. The key finding is a **speedup illusion**: participants accurately predict how long they'll take independently but significantly *underestimate* how long they'll take with AI assistance — despite actual completion times being - [Evidence of a Cognitive Shift in AI Education: How Students Are Rethinking Human Intelligence?](https://edtechdev.github.io/aied/articles/cognitive-shift-ai-education/): This paper presents a striking longitudinal finding: as AI becomes a routine educational tool, students systematically revalue **human intelligence (HI) over artificial intelligence (AI)**. Drawing on 6 years of classroom poll data (2020–2026) from 471 undergraduate and MSc computer science students, Rekik documents a cognitive shift that progresses through four phases: hype → distrust → trust → d - [CogTax: A Four-Level Cognitive Taxonomy for Command-Line Computing Education](https://edtechdev.github.io/aied/articles/cogtax-cognitive-taxonomy/): **Manuel Alonso-Carracedo, Ruben Fernandez-Boullon, Pedro Celard, Francisco J. Rodriguez-Martinez, Lorena Otero-Cerdeira** — Universidade de Vigo, submitted 30 Jun 2026 - [Reexamining the Cold-Start Problem in Knowledge Tracing Models and Implications for SafeInsights](https://edtechdev.github.io/aied/articles/cold-start-knowledge-tracing-safeinsights/): **Jiayi Zhang, Ryan S. Baker, Debshila Basu Mallick, Cristina Heffernan, Neil Heffernan** — cs.HC - [Collaborative AI Tutoring](https://edtechdev.github.io/aied/articles/collaborative-ai-tutoring/): ProPACT constructs a real-time model of pair collaboration using three signals: - [Commenting with Copilot: A Taxonomy and Multi-Year Analysis of Student Code-Generation Specifications](https://edtechdev.github.io/aied/articles/commenting-copilot-student-code-specs/): Analyzes how students specify intended behavior in natural language to AI code tools (Copilot) across multiple years, deriving a taxonomy of code-generation specifications expressed through comments. As AI tools shift emphasis from writing code to specifying behavior, the study documents what students actually ask of these systems. - [Co-Designing Community-Centered AI Education for Adults: A Midwestern Case Study](https://edtechdev.github.io/aied/articles/community-centered-ai-education-adults/): This case study reports on a community-based participatory research project that co-designed an ai-literacy program for 54 adults (48 in-person and 6 virtual) in a predominantly African American community in the Midwestern United States. The program covered fundamental AI concepts, societal implications, and practical applications, using hands-on activities and concrete examples over abstract tech - [Knowledge, Skills, Attitudes, Production: Competency-Based Education After Generative AI](https://edtechdev.github.io/aied/articles/competency-based-education-genai-production-2026/): **Synthesis:** This conceptual paper proposes adding *production* — the capability to deliver professional-standard work by directing tools and other people — as a fourth attribute of competency-based education (CBE), alongside knowledge, skills, and attitudes/values. The proposal responds to a construct-validity problem: generative AI has severed the inference from a student-produced artifact to - [Computational Thinking Development in AI Agent Creation: A Mixed-Methods Study](https://edtechdev.github.io/aied/articles/computational-thinking-ai-agent-creation/): Computational Thinking Development in AI Agent Creation: A Mixed-Methods Study **Sun et al. (2026)** — Multiple institutions. arXiv cs.CY. - [Computational Thinking to Enhance Educational Robotics in Secondary School's Curriculum](https://edtechdev.github.io/aied/articles/computational-thinking-educational-robotics-secondary-2026/): **Synthesis:** Valls i Pou (2026) examines how computational thinking can enhance the effective integration of educational robotics into secondary school curricula. Arguing that educational robotics is a strong resource for fostering problem solving, critical thinking, and STEAM subjects, the paper relates the theoretical framework of computational thinking to 21st-century skills and secondary-sch - [Confident yet Concerned: Inconsistencies in Computing Students'' Attitudes on Cybersecurity](https://edtechdev.github.io/aied/articles/computing-students-cybersecurity-attitudes/): Computing students show inconsistencies between confidence in cybersecurity knowledge and actual safe practices; educational interventions are needed to close the gap. - [Creating Learning Scaffolds for Engineering Design Using Concept Catalyst](https://edtechdev.github.io/aied/articles/concept-catalyst-engineering-scaffolds/): Singh, Mansi, and Riedl (2026) present Concept Catalyst, an LLM-powered tool designed to reduce K-12 teacher preparation time for Engineering Design Challenges. Unlike general-purpose chatbots, Concept Catalyst structures the interaction around three stages: (1) LLM decomposition of a design challenge into conceptual components, (2) visual manipulation and linking of related concepts by the teache - [Concept Catalyst: Exploring Scrutable Interfaces to Structure K-12 Teacher Interactions with Generative AI](https://edtechdev.github.io/aied/articles/concept-catalyst-k12-teacher/): Mansi et al. (2026) introduce Concept Catalyst, a system designed around 'scrutable interfaces' — interfaces that make AI reasoning visible and editable by users. Working with K-12 teachers, the study shows that when teachers can inspect and modify how a generative-ai tool processes their inputs, they report higher trust, greater sense of control, and better alignment with their pedagogical goals. - [Confidence-Aware Automated Assessment of Student-Drawn Scientific Models](https://edtechdev.github.io/aied/articles/confidence-aware-student-drawing-assessment/): **Luyang Fang, Yingchuan Zhang, Jongchan Park, Zhaoji Wang, Ping Ma, Xiaoming Zhai** (2026). arXiv cs.AI preprint - [Confidence-Aware Automatic Short Answer Grading](https://edtechdev.github.io/aied/articles/cong-confidence-asag-2026/): **Confidence-Aware ASAG** — A hybrid confidence estimation framework for Automatic Short Answer Grading with LLMs that fuses model-based confidence signals (verbalized, latent, consistency-based) with dataset-derived aleatoric uncertainty via Random Forest + Platt scaling, enabling reliable selective prediction and principled human-in-the-loop review workflows. - [ConnectED: A Curriculum-Aligned AI System for Vietnamese Instructional Lesson Planning and Student Learning](https://edtechdev.github.io/aied/articles/connected-ai-lesson-planning-vietnam/): **Thang Doan Viet, Anh Nguyen Hoang, Tinh Luong Son, Anh Hoang Thi Ngoc, Huyen Giang Thi Thu, Tai Le Quy** — arXiv preprint (2026). ## Synthesis - [Constructing Epistemic AI Literacy: Detecting Epistemic Aims and Processes in Student-AI Co-Programming](https://edtechdev.github.io/aied/articles/constructing-epistemic-ai-literacy-student-ai-co-programming/): Epistemic thinking — understanding how knowledge is constructed and justified — plays a central role in ai-literacy, particularly when students co-program with generative AI. This paper introduces a framework for detecting epistemic aims and processes in student-experience during programming activities. The analysis reveals that students engage in question construction, AI output evaluation, and s - [The Hidden Cost of Contextual Sycophancy: an AI Literacy Intervention in Human-AI Collaboration](https://edtechdev.github.io/aied/articles/contextual-sycophancy-ai-literacy/): - pedagogical-llm-training ## Connected Articles - [Conversational AI as a catalyst for informal learning: An empirical large-scale study on LLM use in everyday learning](https://edtechdev.github.io/aied/articles/conversational-ai-informal-learning/): **Synthesis:** Conversational AI as a catalyst for informal learning: An empirical large-scale study on LLM use in everyday learning - [The Path to Conversational AI Tutors: Integrating Tutoring Best Practices and Targeted Technologies to Produce Scalable AI Agents](https://edtechdev.github.io/aied/articles/conversational-ai-tutors-framework/): **Authors:** Kirk Vanacore, Ryan S. Baker, Avery H. Closser, Jeremy Roschelle **Year:** 2026 **Venue:** arXiv (cs.HC) Synthesizes intelligent tutoring systems research and generative AI into a keep/change/center/study framework for conversational tutoring systems, arguing proven ITS technologies should anchor generative tutors while centering student meaning-making and agency. - [Catching The Correct Answer Trap: Characterising AI Tutor Blind Spots When Analysing Student Reasoning](https://edtechdev.github.io/aied/articles/correct-answer-trap-ai-tutor/): **Catching the Correct Answer Trap** — accepted at AIED 2026 — exposes a critical blind spot in intelligent-tutoring systems: they systematically fail to detect misconceptions when students arrive at correct answers through flawed reasoning. Using real student data from the Eedi mathematics platform, the authors characterize the 'Correct Answer Trap' (CAT), showing that 71% of failures concentrate - [The Correct Answer Trap: Pedagogically-Grounded Detection and Feedback for Hidden Misconceptions](https://edtechdev.github.io/aied/articles/correct-answer-trap-misconceptions/): Imran and Bulathwela (2026) identify the 'correct answer trap' — automated feedback systems that judge only answer correctness reinforce rather than address misconceptions when students reach the right answer through flawed reasoning. Using 20,964 real student responses from the Eedi mathematics platform, they find fine-tuned classifiers detect only 57% of hidden misconceptions (standard ML interv - [Cost-of-Ethics Crisis: Beliefs, Decisions, and Justifications in the Job Searches of Computer Science Students in Canada and the United States](https://edtechdev.github.io/aied/articles/cost-of-ethics-crisis-cs-ethics-education/): This study examines the disconnect between **ethics education** and real-world decision-making among 129 computer science students and recent graduates during their job searches. Despite receiving contemporary CS ethics education, most students prioritize compensation, location, and workplace culture over ethical and social concerns when choosing employers. - [CoTAL: Human-in-the-Loop Prompt Engineering for Generalizable Formative Assessment Scoring and Feedback](https://edtechdev.github.io/aied/articles/cotal-formative-assessment-scoring-2026/): 1. **Evidence-Centered Design (ECD)** — assessments and rubrics aligned to curriculum goals from the start 2. **Human-in-the-loop prompt engineering** — labelled examples and prompts refined iteratively with educators 3. **Chain-of-thought (CoT) prompting + active learning** — teacher and student feedback loops refine questions, rubrics, and LLM prompts across iterations - [CourseBlueprint: A Structured Pipeline for Adaptive Pedagogical Video Generation Grounded in Course Corpora](https://edtechdev.github.io/aied/articles/courseblueprint-adaptive-video-generation/): Islam et al. (2026) address a core limitation of generative text-to-video for education: while visually fluent, such systems lack pedagogical content knowledge (PCK). CourseBlueprint provides a structured pipeline producing adaptive pedagogical videos grounded in a course corpus (undergraduate biomedical-imaging course BMED 2300, 23 lectures, 1,116 slides). The pipeline includes four components wi - [CourseGraph: Finding overlaps and differences in Computer Science courses across universities](https://edtechdev.github.io/aied/articles/coursegraph-cs-course-comparison-2026/): **Synthesis:** This paper presents CourseGraph, a methodology for automatically evaluating external course equivalences by modelling course content as structured knowledge graphs. Designed for student mobility programmes like Erasmus+, CourseGraph extracts topics from course descriptions, maps relationships between concepts, and identifies substantive overlap vs. complementarity between courses at - [What Does the Credential Still Certify? Cognitive Stewardship for AI-Mediated Education](https://edtechdev.github.io/aied/articles/credential-cognitive-stewardship-ai-assessment/): Generative AI undermines a basic premise of educational assessment: that submitted work reliably evidences the human capacities a credential certifies. This paper proposes *cognitive stewardship*, a framework linking four elements \u2014 the learning claim, the delegation boundary, the evidence standard, and safeguards \u2014 to reason about what remains inferable about learning once cognitive wor - [AI-accelerated End-to-End Framework for Rapid Professional Upskilling](https://edtechdev.github.io/aied/articles/crewscaler-ai-upskilling-framework/): **Synthesis:** The Crew Scaler framework applies AI acceleration across all five stages of professional upskilling—knowledge acquisition, content development, content review and verification, AI-tutor coaching, and assessment development—with external validation from NASBA CPE accreditation, NVIDIA certification exam passes (3/3, 14 in progress), and a 1,267-item risk dataset production. Dual-effi - [To Tab or Not to Tab: Measuring Critical Engagement in AI Code Completion Tools Using Behavioral Signals and Attention Checks](https://edtechdev.github.io/aied/articles/critical-engagement-code-completion/): Hutchison et al. (2026) develop and validate a method for measuring critical engagement with AI code completion tools in educational settings. Using behavioral signals (time-to-accept, edit distance from suggestion) and embedded attention checks, they find that the majority of students accept AI code suggestions passively, without critically evaluating correctness or appropriateness. This 'tab-and - [GenAI Knowledge, Epistemic Orientation, and Intellectual Values Predict Undergraduate Students' Critical GenAI Use](https://edtechdev.github.io/aied/articles/critical-genai-use-predictors/): A correlational study (N = 67 undergraduate psychology students, Bielefeld University) testing two **protective factors against uncritical GenAI overreliance**: (1) **knowledge about genAI** and (2) the **disposition to engage in critical thinking** — operationalised via Kuhn's framework as *epistemic orientation* (tendency away from absolutist toward evaluativist beliefs) and *intellectual values - [Technology, Education and Critical Media Literacy: Potential, Challenges, and Opportunities](https://edtechdev.github.io/aied/articles/critical-media-literacy-education-2026/): **Synthesis:** Based on expert interviews and a survey of 141 university students in Communication and Education programs, this study finds that while technology offers real opportunities for teaching and learning, its inclusion in the curriculum is limited and often superficial. Teachers are under-trained to manage tools that produce disinformation, deepfakes, and fake news, which hinders student - [Scaffolding Critical Thinking with Generative AI](https://edtechdev.github.io/aied/articles/critical-thinking-genai-scaffolding/): Vendrell & Johnston (2026) propose a design-oriented framework for LLM use in higher education that strengthens rather than displaces critical-thinking, countering cognitive-offloading and metacognitive disengagement (metacognition, scaffolding). - [Did Alice Do Wrong? Cross-Cultural Differences in Student Perceptions of Generative AI Use in University Computing Education](https://edtechdev.github.io/aied/articles/cross-cultural-student-perceptions-genai-computing/): A scenario-based survey (Fall 2024) comparing how computing students at Canadian and South Korean universities judged the ethicality and policy compliance of AI-assisted coding practices. Despite functionally identical institutional policies, Canadian students were consistently and significantly more likely to rate GenAI use as unethical and against the rules (Mann-Whitney U tests across nearly al - [Cross-Dataset Bloom Question Classification: Supervised Models and Prompted LLMs](https://edtechdev.github.io/aied/articles/cross-dataset-bloom-question-classification/): Evaluates cross-dataset generalization of ML/DL methods and LLMs for automatic Bloom's taxonomy classification of assessment questions across five datasets. Supervised ML/DL models degraded substantially on unseen datasets, while LLMs with tailored prompting (in-context examples + course-specific action verbs) showed stable performance. A lightweight UI was developed for instructors to classify la - [Cross-Subject Predictive Validity for Learning Outcomes of Delayed Start Behavior](https://edtechdev.github.io/aied/articles/cross-subject-validity-delayed-start/): This study examines the student-modeling validity of **delayed start behavior** — when students begin assignments or practice sessions past a recommended start time — as a predictor of learning-gains across multiple subjects. The authors test whether a behavioral detector developed for one academic domain (e.g., chemistry) can predict learning outcomes in another (e.g., physics or statistics), a p - [CSTutorBench: Benchmarking Small Language Models as Tutors for Block-Based Programming](https://edtechdev.github.io/aied/articles/cstutorbench-slm-tutors/): Deploying LLM tutors in K-12 raises concerns around privacy, cost, and reliance on proprietary models, motivating small language models (SLMs) as an alternative. The authors introduce **CSTutorBench**, a benchmark evaluating language models as CS tutors in VEX VR, a block-based robotics environment. It comprises 17 scenario-based questions scored against a pedagogical rubric grounded in tutoring a - [Culturally-Aware AI for Cross-Boundary Community Learning](https://edtechdev.github.io/aied/articles/culturally-aware-aied-community-learning/): Reports on cross-boundary Community-Based Learning where undergraduate students develop AI-enabled solutions for cultural heritage preservation and sustainable development. The paper argues that AIED research often lacks human-centered grounding and adequate attention to cultural context, and that Community-Based Learning — a pedagogy rooted in social work — remains underrepresented in AIED, parti - [Curiosity as Linguistic Intervention: Using LLM Tutoring Dialogues to Influence Exploratory Learning Behavior](https://edtechdev.github.io/aied/articles/curiobot-llm-tutoring-exploratory-learning/): Ganganath et al. (2026) introduce CURIOBOT, a framework that operationalizes Berlyne's four collative variables (novelty, complexity, conflict, uncertainty) as adaptive linguistic interventions in conversational tutoring. Across 270 tutoring conversations spanning multiple LLM model families, domains, and topic complexity levels, curiosity-oriented interventions consistently increased exploratory - [Curriculum as Code: An AI-Assisted Architecture for Instructional Design in STEM Education](https://edtechdev.github.io/aied/articles/curriculum-as-code-instructional-design-2026/): **Synthesis:** This paper presents a six-phase AI-assisted instructional design architecture based on the Curriculum as Code paradigm, integrating Generative AI with LaTeX and Python to automate the creation of reproducible, visually consistent, and technically precise materials for STEM education. Validated over one year across 8 modules and 28 project contexts in a Project-Based Learning environ - [CyberAGENTS: Structured Autonomy for Agentic Gamified Learning in Cybersecurity](https://edtechdev.github.io/aied/articles/cyberagents-gamified-cybersecurity-learning-2026/): **Synthesis:** Hornung et al. (2026) present **CyberAGENTS**, an agentic framework for gamified cybersecurity learning that enables *structured autonomy* through ontology-guided validation, schema-governed behavioral control, and competency-based progression. The learning loop is decomposed into four specialized agents (challenge, support, evaluation, reward), each governed by behavioral schemas, - [Generative AI Feedback, English Writing and Teacher Rubrics: A Multiple-Case Study of CyberScholar](https://edtechdev.github.io/aied/articles/cyberscholar-genai-writing-feedback/): - rag - formative-assessment - human-in-the-loop-ai - faculty-development - teacher-role ## Connected Articles - [Data Annotations as Pedagogical Hints: From Subjective Labels to Critical Thinking](https://edtechdev.github.io/aied/articles/data-annotations-pedagogical-hints/): Machine learning courses typically hand students pre-labeled datasets, hiding the subjectivity baked into human annotation and cultivating an overly trusting view of AI data pipelines. This two-university study (Fontys, Netherlands and IT University Copenhagen; N=43) had students annotate skin-lesion images for hair coverage on a 3-point scale, then surveyed their understanding of annotation ambig - [Data Comics for Education: Evaluating Effectiveness, Benefits, and the Ethics of AI-Assisted Creation](https://edtechdev.github.io/aied/articles/data-comics-for-education-evaluating-effectiveness-benefits-ethics/): Data comics combine sequential visual narratives with data visualization to improve student engagement with generative-ai in educational settings. This paper evaluates the effectiveness of AI-assisted creation of data comics, finding that they significantly enhance student engagement and comprehension compared to traditional visualization formats. The study also examines ethical dimensions includi - [DebugTracker: Lightweight Process Evidence for Classroom Debugging](https://edtechdev.github.io/aied/articles/debugtracker-classroom-debugging/): Debugging exercises are usually graded from final code and test outcomes, which hide *how* students reproduced failures, formed hypotheses, inspected evidence, edited code, and verified fixes. The authors present **DebugTracker**, a Visual Studio Code extension that records lightweight debugging-process evidence for classroom tasks. It separates uncoached Evaluation Mode traces from coached Traini - [DeepTutor: Towards Agentic Personalized Tutoring](https://edtechdev.github.io/aied/articles/deeptutor/): **A fully open-source agentic tutoring framework that closes the loop between citation-grounded problem tutoring and difficulty-calibrated question generation**, powered by a hybrid personalization engine combining static knowledge grounding with dynamic learner memory. Evaluated via TutorBench across 5 university disciplines, improving personalized metrics by 10.8% and general agentic reasoning b - [Once a Response, Always a Response: Detecting LLM-generated Text via Latent Prompt Restoration](https://edtechdev.github.io/aied/articles/detecting-llm-generated-text-latent-prompt/): **Synthesis:** EchoPrompt introduces a training-free zero-shot detector for plagiarism-detection that exploits the latent prompt dependency inherent in machine-generated content. By restoring a generic assistant-response prefix and measuring likelihood gain differences between instruction-tuned and base models, EchoPrompt achieves state-of-the-art detection performance without training. This appro - [A didactical-driven teacher assistant for a dimensional modeling course](https://edtechdev.github.io/aied/articles/didactical-teacher-assistant-dimensional-modeling/): Brisson, Segarra and Smits present a didactically-driven LLM teacher assistant for a university dimensional modeling (data warehousing) course. Unlike most educational chatbots that delegate pedagogical decisions implicitly to the LLM, their system makes content selection and didactic structuring explicit and traceable: tutoring strategy is encoded in an external didactic layer that the LLM execut - [Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues](https://edtechdev.github.io/aied/articles/difficulty-aware-dialogue-kt/): This paper bridges LLM-based dialogue tutoring and interpretable student modeling. By mapping opaque LLM representations to **Item Response Theory** parameters — student ability (θ) and question difficulty (b) — the framework makes turn-by-turn predictions both accurate and cognitively meaningful. This connects directly to knowledge-tracing-irt by extending IRT beyond static assessment into live d - [The Illusion of Competence: Self-Perceived Digital Literacy and AI Readiness Among European Secondary Students](https://edtechdev.github.io/aied/articles/digital-literacy-illusion/): This multicenter study (N=243 European secondary students) systematically challenges the 'Digital Native' paradigm by demonstrating a severe confidence-competence gap in digital and AI literacy. Students report near-maximum self-efficacy in passive digital consumption (browsing, social media) but exhibit a sharp decline when evaluated on active technological creation and algorithmic logic — a coll - [Demystify, Use, Reflect, Assess (DURA): An Experience Report on LLM Integration in CS2](https://edtechdev.github.io/aied/articles/dura-llm-cs2/): **Margaret Ellis, Nikitha Donekal Chandrashekar, Sehrish Basir Nizamani, Mohammed Farghally, Jake O'Brien, Naren Ramakrishnan** — SIGCSE Virtual 2026, submitted 29 Jun 2026 - [Improving Capstone Team Outcomes through Dynamic Skill Matching and Preference Alignment](https://edtechdev.github.io/aied/articles/dynamic-skill-matching-capstone-teams/): Team-based projects are a cornerstone of engineering and computing courses, but unstructured team formation often leads to poor project outcomes due to misaligned student interests and inadequate skill coverage. This paper introduces a novel, three-stage methodology for creating effective student teams by integrating student preferences with project skill requirements. Students complete a survey, - [DysLexLens: A Low-Resource LLM Framework for Analysing Dyslexic Learners Insights from Online Forums](https://edtechdev.github.io/aied/articles/dyslexlens-dyslexic-learners-ai/): DysLexLens is a low-resource LLM framework designed to analyze how special-education experience AI tools by mining online forum discussions. The framework employs dictionary-driven filtering to construct focused corpora from Reddit, integrates LLM-assisted knowledge graph reasoning, and generates verifiable query responses about learners' lived experiences with AI for reading, writing, and study t - [ECNUClaw: A Learner-Profiled Intelligent Study Companion Framework for K-12 Personalized Education](https://edtechdev.github.io/aied/articles/ecnuclaw-k12-personalized-companion/): ECNUClaw is an open-source framework by Zhou, Li & Zhang (2026) for building **learner-profiled intelligent study companions** in K-12 education. The system maintains a **five-dimension learner profile** — cognitive, behavioral, emotional, metacognitive, and contextual — by extracting signals from student-companion dialogues at each conversational turn. - [The Missing Layer: Why EdTech Needs Design-Time Generative UI, Not Just Runtime Personalization](https://edtechdev.github.io/aied/articles/edtech-design-time-generative-ui/): Argues the dominant paradigm of runtime GenUI adaptation in EdTech is insufficient. Proposes design-time card-based GenUI where educational content is encoded as modality-agnostic semantic units and GenAI produces multiple interface representations (interactive, audio, simplified text, low-bandwidth) at design time for instructor verification. Embeds Universal Design for Learning into authoring wo - [Are Agents Ready to Teach? A Multi-Stage Benchmark for Real-World Teaching Workflows](https://edtechdev.github.io/aied/articles/eduagentbench-agent-teaching-benchmark/): Are Agents Ready to Teach? A Multi-Stage Benchmark for Real-World Teaching Workflows **Chen et al. (2026)** — Multiple institutions. Under review. - [Educational LLM Alignment](https://edtechdev.github.io/aied/articles/educational-llm-alignment/): Hardy & Kim (2026) identify a **cascading proxy** problem in AI-for-education evaluation: - [Educational VLM Evaluation](https://edtechdev.github.io/aied/articles/educational-vlm-evaluation/): Benchmarking vision-language models (VLMs) not on their ability to solve problems, but on their ability to *support learners* — particularly struggling learners and those making errors. Traditional AI benchmarks measure expertise; educational benchmarks must measure pedagogical responsiveness. - [EduClaw-Bench: A Long-Horizon Benchmark for Pedagogical LLM Agents with Simulated Learners](https://edtechdev.github.io/aied/articles/educlaw-bench-pedagogical-llm-agents-2026/): **EduClaw-Bench: A Long-Horizon Benchmark for Pedagogical LLM Agents with Simulated Learners** — Introduces a 30-day long-horizon benchmark for pedagogical LLM agents using simulated learners grounded in knowledge tracing. Evaluates 10 agent adapters over three base-model tiers and finds that tutoring quality depends on both the base model and a... intelligent-tutoring llm agentic-ai benchmark kno - [Sycophancy is an Educational Safety Risk: Why LLM Tutors Need Sycophancy Benchmarks](https://edtechdev.github.io/aied/articles/eduframetrap-llm-sycophancy-educational-safety/): Sycophancy is an Educational Safety Risk: Why LLM Tutors Need Sycophancy Benchmarks **Kasneci & Kasneci (2026)** — Position paper. arXiv cs.AI/cs.HC. - [EduGuard: A Safe RAG-Based LLM Tutor for Programming Education](https://edtechdev.github.io/aied/articles/eduguard-safe-rag-llm-tutor/): EduGuard is a retrieval-augmented generation (RAG) tutoring framework that directly confronts the safety and pedagogical failures of unrestricted LLM tutors in introductory programming. Unrestricted tutors hallucinate, contradict course policy, reveal complete solutions, and foster passive dependence; EduGuard counters these with query understanding, instructor-approved course retrieval, pedagogic - [EduMirror: Modeling Educational Social Dynamics with Value-driven Multi-agent Simulation](https://edtechdev.github.io/aied/articles/edumirror-educational-social-dynamics/): **Jingzhe Lin, Hengbin Yu, Yongdan Zeng, Fangwei Zhong** — ICML 2026 — cs.MA, cs.CY - [EduSim-LLM: An Educational Platform Integrating Large Language Models and Robotic Simulation for Beginners](https://edtechdev.github.io/aied/articles/edusim-llm-robotic-simulation-education-2026/): **Synthesis:** Lu and Zhang (2026) present EduSim-LLM, an educational platform that integrates large language models with robot simulation to make robotic control accessible to beginners. Recognizing that the integration of natural language understanding into robotic control is a key challenge in human-robot interaction, the platform constructs a language-driven control model that translates natur - [EduZone: A Framework for Evaluating LLM Safety for K-12 Students and Teachers](https://edtechdev.github.io/aied/articles/eduzone-llm-safety-k12/): **EduZone is an automated evaluation framework that generates contextually grounded adversarial interactions to probe LLM safety in K-12 education, revealing that models are more vulnerable to education-specific harms and dynamic multi-turn conversations than existing guardrails address.** - [Effects of AI chatbot-supported cooperative flipped classroom on student collaboration, self-regulated learning and academic performance: A mastery learning perspective](https://edtechdev.github.io/aied/articles/effects-of-ai-chatbot-supported-cooperative-flipped-classroom-on-student-collabo/): **Synthesis:** Based on mastery learning theory, this study employed a quasi-experimental design to examine how an AI chatbot-supported cooperative flipped classroom influences students' collaboration, self-regulated learning and academic performance. Involving 154 junior students over an 11-week period, results showed the experimental group demonstrated significantly higher posttest scores in col - [The efficiency-gain illusion: People underestimate the rate of AI use and overestimate its benefits on simple tasks](https://edtechdev.github.io/aied/articles/efficiency-gain-illusion-ai-overreliance/): Across three pre-registered studies (N=2,691), this paper documents systematic miscalibration in how people perceive their own generative-ai usage. The authors find that people not only use AI for cognitively simple tasks even when it provides no meaningful efficiency benefit, but also systematically misperceive both how much they use AI and how much it helps them. - [Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Framework](https://edtechdev.github.io/aied/articles/egai-power-systems-education/): **An open, executable module library for engineering-grounded AI (EGAI) in power systems education lowers the entry barrier for newcomers, with a progressive difficulty ladder from DNN templates to physics-informed neural networks, delivered via IEEE online course and PES webinars.** - [ELBench: A Multi-Dimensional Benchmark for Education-Facing Large Language Models](https://edtechdev.github.io/aied/articles/elbench-education-llm-benchmark-2026/): **Synthesis:** Jiang et al. (2026) introduce **ELBench**, the first benchmark to evaluate education-facing LLMs on all four required dimensions — General Capability, Safety and Trustworthiness, Basic Education, and High-Level Cultivation — under a common protocol, combining curated public sources with newly synthesized safety and cultivation data. Testing nine models, they find module-level profil - [Rethinking Elementary Education's Writing Instruction in The Age of Generative AI: A Systematic Review](https://edtechdev.github.io/aied/articles/elementary-writing-genai-systematic-review-2026/): **Synthesis:** This systematic literature review synthesizes 8 peer-reviewed studies (2019–2025) on AI literacy for elementary writing instruction, finding that AI integration efficiently supports writing practices and fosters creativity through multimodal application while creating nuanced approaches to writing assessment — alongside unresolved limitations for future research. - [ELEVATE: Designing Human-Centered GenAI Virtual Tutors for Scalable and Inclusive Education](https://edtechdev.github.io/aied/articles/elevate-genai-virtual-tutors/): **Lorenzo Stacchio, Michele Giordano, Daniele Berardini, Primo Zingaretti, Emanuele Frontoni** — submitted 17 Jun 2026 - [Embodied Inquiry with AI as Facilitator: An Exploratory Case Study](https://edtechdev.github.io/aied/articles/embodied-inquiry-ai-facilitator-physics-2026/): **Synthesis:** Tufino & Damiani (2026) explore where a language-based AI can stand within an inquiry activity without displacing embodied experience, using a Master's-level physics education course investigating the statics of fluids via the ISLE approach. In a two-phase design, students first built the buoyancy model with their own hands without AI; a purpose-configured AI assistant then facilita - [Designing for What Cannot Be Seen: Supporting Embodied String Learning for Musicians with Blindness and Low-Vision](https://edtechdev.github.io/aied/articles/embodied-string-learning-blindness-low-vision-musicians/): Bowed string performance depends on fine bodily coordination usually taught through visual demonstration, creating persistent barriers for musicians with blindness and low-vision (BLV). This design study worked with four advanced BLV string musicians and three instructors using practice-video analysis, lesson observation, and expert reflection to surface embodied, non-visual learning strategies. - [Invisible Impact of Empathy on Behavioral Change: Isolating the Effect of Empathy in Long-term Physical Activity Coaching Chatbot Interactions](https://edtechdev.github.io/aied/articles/empathy-coaching-chatbot/): Siyan et al. (2026) conduct a carefully controlled experiment isolating the effect of empathetic language in LLM-powered physical activity coaching chatbots over a longitudinal deployment. While the empathy condition did not directly increase exercise behavior, it significantly improved users' sense of being understood, which in turn predicted sustained engagement with the coaching system. This fi - [Engagement Assessment in Video Learning](https://edtechdev.github.io/aied/articles/engagement-assessment-video/): **EduGage** (Leng et al., 2026) addresses a core challenge: in online/video-based learning, **learners must self-regulate** their engagement with instructional materials. - [From Heuristics to Analytics: Forecasting Effort and Progress in Online Learning](https://edtechdev.github.io/aied/articles/engagement-forecasting-its/): This paper tackles a core ITS challenge: predicting when students will disengage so tutors can intervene before it's too late. It introduces **engagement forecasting** as a supervised prediction task with two complementary targets: minutes practiced per week (effort) and new skills mastered per week (progress). - [Engagement Intensity as a Learner-Modeling Signal for Adaptive AI Ethics Instruction](https://edtechdev.github.io/aied/articles/engagement-intensity-learner-modeling/): Engagement intensity during AI ethics instruction serves as an effective learner-modeling signal for adaptive instruction; prior LLM experience influences engagement patterns. - [Enhancing creative writing with robot-LLM integration: The interplay of embodiment, AI creativity and user engagement](https://edtechdev.github.io/aied/articles/enhancing-creative-writing-with-robot-llm-integration-the-interplay-of-embodimen/): **Synthesis:** This study explores the impact of robot-LLM integration on collaborative creative writing, focusing on how embodiment and AI creativity influence creative output. With 150 undergraduate students across five conditions, results revealed that the Human-Robot (High-Creativity LLM) condition significantly enhanced originality, while Human-Human and Human-LLM collaborations excelled in i - [Enhancing online learning outcomes through virtual companion AI: The role of identity anthropomorphism](https://edtechdev.github.io/aied/articles/enhancing-online-learning-outcomes-through-virtual-companion-ai-the-role-of-iden/): **Synthesis:** Grounded in social presence theory, this study introduces the concept of identity anthropomorphism and adopts multimodal learning analytics (MMLA) combining questionnaires, EEG and eye tracking to examine its effects on learning outcomes. With 70 participants across three conditions, results indicated that identity-anthropomorphised AI significantly improved learning outcomes compar - [Ordered Network Analysis of Epistemic Emotions during Collaborative Problem Solving](https://edtechdev.github.io/aied/articles/epistemic-emotions-collaborative-problem-solving/): Anindho, Venkatesha, Ocumpaugh and Blanchard apply Ordered Network Analysis to trace how epistemic emotions such as confusion and frustration persist and transition during co-situated collaborative problem solving. The work advances affect-aware learning analytics by modeling the temporal ordering of emotional states rather than static frequencies, informing when interventions should trigger in af - [From Prompting to Epistemic Proactivity: Temporal Trajectories of Student-AI Interaction in Mathematics Learning](https://edtechdev.github.io/aied/articles/epistemic-proactivity-math/): Abdelghani, Kaiser & Murayama (2026) trace how middle and high school students' interactions with AI math tutors evolve over time, identifying a trajectory from superficial prompting ('tell me the answer') to what they term 'epistemic proactivity' — the active, self-directed pursuit of conceptual understanding through AI dialogue. This developmental framework is a significant contribution to ai-li - [Mathematical Modelling of Ethical AI Use in Higher Education: A Coordination Game Framework for Future-Facing Learning](https://edtechdev.github.io/aied/articles/ethical-ai-higher-ed-game-theory/): **Ethical AI Use in Higher Education: A Coordination Game Framework** provides a formal mechanism-level account of why policy statements alone fail to change student AI-use behavior. Reframing student AI use in assessments as a coordination problem — where individual choices depend on peer expectations and assessment design — the authors develop an evolutionary game-theoretic model capturing learn - [Evaluating Interactivity: Toward Automated Assessment of AI-Generated Explorable Explanations](https://edtechdev.github.io/aied/articles/evaluating-interactivity-automated-assessment-ai-generated-explorable-explanations/): While llms now enable rapid generation of learning materials like generative-ai, evaluating the pedagogical quality of these materials remains an open challenge. This paper proposes an automated assessment framework for evaluating interactivity in AI-generated explorable explanations — dynamic, learner-driven content that students can manipulate to discover concepts. The framework addresses the ga - [Expert Cognition Dashboard: From Learning Analytics to Cognition Intelligence in AI-Driven Education](https://edtechdev.github.io/aied/articles/expert-cognition-dashboard/): **Annie Yuan (2026)**. arXiv preprint (cs.HC). - [Explainable Knowledge Tracing via Probabilistic Embeddings and Pattern-based Reasoning](https://edtechdev.github.io/aied/articles/explainable-probabilistic-kt/): This paper introduces **PLKT** (Probabilistic Logical Knowledge Tracing), which replaces deterministic vector embeddings with **beta-distributed probabilistic embeddings**, allowing explicit representation of uncertainty in each knowledge state. - [Face value: How avatar identity shapes epistemic trust in AI-mediated learning](https://edtechdev.github.io/aied/articles/face-value-how-avatar-identity-shapes-epistemic-trust-in-ai-mediated-learning/): **Synthesis:** Two experiments examined how avatar race, gender, and age shape trust in AI-mediated education. Study 1 (N=102) used a within-subjects laboratory design; Study 2 (N=294) adopted a between-subjects online design. Across both studies, social identity cues strongly influenced evaluations and behavior: White avatars and Asian male avatars in STEM contexts were rated more credible and co - [Fair and explainable educational recommendations with a hybrid Graph-GRU framework](https://edtechdev.github.io/aied/articles/fair-explainable-edu-recommendations/): **Synthesis:** Fair and explainable educational recommendations with a hybrid Graph-GRU framework - [Critical AI Tutors: Empower or Enslave?](https://edtechdev.github.io/aied/articles/favero-critical-ai-tutors-empower-enslave-2025/): **Critical AI Tutors: Empower or Enslave?** — A position paper presented at the AIED 2025 workshop that issues a stark warning: unchecked use of AI tutors risks creating a generation of cognitively atrophied learners who have traded genuine understanding for the illusion of competence. Drawing on cognitive science and pedagogical theory, the authors argue that AI tutors without intentional guardra - [Feedback futures: beyond the limits of human and GenAI capacities](https://edtechdev.github.io/aied/articles/feedback-futures-genai/): This editorial synthesises the seven papers of the AEHE 51(5) special issue on feedback in the age of generative AI. Its central claim: the question is **not whether GenAI feedback is useful, but how human and GenAI feedback can be combined to sustainably support learning rather than merely improve immediate performance**. Teacher and student feedback literacy are necessary but not sufficient — wh - [Principled AI Education Framework](https://edtechdev.github.io/aied/articles/finkelstein-principled-ai-education-2025/): **Principled AI Education Framework** — A principled way to think about AI in education: guidance for educators and policy makers on action based on goals, models of human learning, and use of technologies. Rather than focusing solely on the promise and peril of AI or its immediate implementation, this framework advances a third path — connecting broad educational goals to actionable practices thr - [Flowcode: An AI-Powered Programming Environment for Scaffolding Iteration in Creative Computing Education](https://edtechdev.github.io/aied/articles/flowcode-ai-creative-coding/): Building upon found examples is a popular way people learn to code, especially in creative coding communities where sharing projects and remixing are common practices. But effectively doing so requires being able to 1) understand how existing code works, and 2) extend it by writing code that implements your own ideas, practices that can be challenging for new creative coders. We explored how to su - [Adoption-Ready Project-Based Learning for Computing Education: The FORAP Framework and a Multi-Scale Project Portfolio](https://edtechdev.github.io/aied/articles/forap-pjbl-computing-education/): Presents FORAP (Framework for Organizing Reusable and Adaptable Project-Based Learning projects) and a portfolio of 14 adoption-ready PjBL packages for computing education. The framework addresses the gap between PjBL's known benefits and its slow classroom adoption by making projects reusable, adaptable, and scalable across contexts. - [Trust-utility gap in introductory physics education: Students' adoption, domain-specific skepticism, and preferences for AI integration](https://edtechdev.github.io/aied/articles/fouad-bentley-trust-utility-gap-physics-2026/): **Synthesis:** Fouad & Bentley (2026) survey 81 introductory physics students and find a striking 50-percentage-point trust-utility gap: 91% use AI for coursework but only 41% trust AI physics explanations — evidence of domain-calibrated skepticism, not uncritical adoption. Students spontaneously identified where AI fails in physics (visual-spatial reasoning, circuits, abstract reasoning), alignin - [FOXGLOVE: Comparing Goal-Oriented Writing Feedback from Experts and LLMs](https://edtechdev.github.io/aied/articles/foxglove-writing-feedback-experts-llms/): Introduces **FOXGLOVE**, a dataset of 696 feedback comments by trained writing instructors on 69 twelfth-grade argumentative essays, paired with 1,644 comments from four frontier LLMs — totaling 2,340 comments with expert quality ratings. Provides the first systematic comparison of LLM and expert feedback on three pedagogically critical dimensions: **goal-orientation, anchoring to specific sentenc - [Framing the 5% Problem: Teachers'' Perspectives on Persistence in Educational Technology](https://edtechdev.github.io/aied/articles/framing-5-percent-problem-teachers-persistence/): Borchers (2026) reports on a 90-minute participatory design workshop with 12 U.S. middle school mathematics teachers using i-Ready Math weekly. Thematic analysis identified four recurring dimensions of low student persistence: motivation and buy-in, cognitive roadblocks, resilience under challenge, and contextual barriers. Teachers emphasized the need to identify where students become stuck and re - [From Answer Generators to Reasoning Facilitators: Designing AI Tutors for Mathematical Reasoning in High-Stakes Environments](https://edtechdev.github.io/aied/articles/from-answer-generators-to-reasoning-facilitators-ai-tutors/): The rapid integration of llms into intelligent-tutoring threatens to reduce mathematical learning to mere answer generation. This paper presents a design framework for AI tutors that act as reasoning facilitators rather than answer generators, specifically targeting high-stakes exam preparation environments. Through a mixed-methods study of junior-high students preparing for the Zhongkao exam, the - [From emotion regulation to academic success: A self-determination theory-based emotional agent-mediated approach](https://edtechdev.github.io/aied/articles/from-emotion-regulation-to-academic-success-a-self-determination-theory-based-em/): **Synthesis:** Emotion regulation has been recognized as a key factor affecting students' academic success. This study proposed a self-determination theory (SDT)-based emotional agent framework, implementing an emotional agent (EmoAgent) capable of proactively detecting students' emotional states and providing emotional regulation strategies. An 8-week quasi-experiment with 173 sixth graders found - [A Guiding Framework for K-12 Teachers in Creating AI-powered Learning Technologies through Vibe Coding](https://edtechdev.github.io/aied/articles/gaide-vibe-coding-k12-teachers/): Large language models generate code from natural language prompts, enabling vibe coding, which allows non-programmers to develop computational solutions. Vibe coding for teachers amplifies the teachers-as-designers paradigm, improving technology integration while fostering AI literacy. However, structured guidance on supporting this process is lacking. We propose GAIDE (A Guiding Framework for AI- - [Game-Based and Gamified Robotics Education: A Comparative Systematic Review and Design Guidelines](https://edtechdev.github.io/aied/articles/game-based-gamified-robotics-education-review-2026/): **Synthesis:** Mubarrat, Shao, and Min (2026) present the first PRISMA-aligned systematic review and comparative synthesis of game-based learning (GBL) and gamification in robotics education. Analyzing 95 studies from 12,485 records across four databases (2014–2025), they coded each study's approach, learning context, skill level, modality, pedagogy, and outcomes (κ = .918). Three patterns emerged - [Gaze-Informed Proactive AI Assistance for Children’s Picture Exploration](https://edtechdev.github.io/aied/articles/gaze-informed-ai-children/): **Zekun Wu, Man Su, Huiyong Li, Tomohiro Nagashima, Anna Maria Feit** — submitted 1 Jul 2026 - [Report on CHIIR 2026 Workshop on Generative AI and Academic Search (GAI&AS)](https://edtechdev.github.io/aied/articles/genai-academic-search-workshop/): **Yifan Liu, Jaime Arguello, Orland Hoeber, Chang Liu et al.** — cs.IR, cs.AI, cs.HC - [From Prompt to Embodied Simulation: Using Generative AI to Create AR Physics Learning Tools](https://edtechdev.github.io/aied/articles/genai-ar-physics-simulation-prompt-2026/): **Synthesis:** Levy et al. (2026) show how a structured natural-language prompt can generate a browser-based, hand-controlled **augmented-reality (AR) physics simulation** — spread your thumb and index finger and a virtual lamp changes color — and describe its use in an introductory physics class. Computer simulations have a long record of supporting physics learning by making abstract concepts in - [Development and applications of Generative AI in architectural design studios](https://edtechdev.github.io/aied/articles/genai-architectural-design-studios/): Examines the integration of deep generative models into architectural design education. The findings, based on students' views and observations in design studios, suggest that GenAI supports the exploration of creative ideas — serving as visual stimuli and inspirational resources in early design stages — while also highlighting the competencies students need to differentiate between GenAI models a - [Gen-AI-tecture: using generative AI to support architectural students in design tasks](https://edtechdev.github.io/aied/articles/genai-architecture-education/): Kapsalis (2026) presents one of the first empirical studies of generative AI integration in architectural design education, using a locally executed, discipline-specific tool within a mixed-methods focus-group design. The study addresses three objectives: creativity impact, inclusivity enhancement, and employability preparation. Results showed enhanced creative fluency, broadened participation acr - [Generative AI as a Design Variable: An Evidence-Centered Framework for Principled Governance in STEM Assessment](https://edtechdev.github.io/aied/articles/genai-assessment-governance/): This paper proposes a principled framework grounded in Evidence-Centered Design (ECD) that treats generative-ai as a design variable within STEM assessment arguments rather than an external threat. This represents a significant evolution beyond the binary debate of 'ban AI vs. allow AI' that has dominated discussions about academic-integrity in education. - [Generative AI Availability, Grades, and Student Satisfaction at a Large University](https://edtechdev.github.io/aied/articles/genai-availability-grades-satisfaction/): This large-scale observational study tests the "GenAI substitution hypothesis" — the concern that students offload cognitive effort to generative-ai and earn inflated grades without learning. Using syllabus and administrative data from a large U.S. university (2015–2025; 156,135 students; 87,936 course offerings), the authors measure each course's GenAI susceptibility with a human-validated LLM pi - [Generative AI Can Harm Teaching](https://edtechdev.github.io/aied/articles/genai-can-harm-teaching-rct-2026/): The null average performance effect masks strong offsetting heterogeneity — and the exam had severe ceiling compression (control mean 89.2/100, 47% ≥ 95), which also limits power. The belief reversal is striking: it contradicts "familiarity breeds acceptance" and suggests an arc from initial awe at AI's instant responses to awareness of its unintended effects. - [Integrating Generative AI into Cybersecurity Education: A Study of OCR and Multimodal LLM-Assisted Instruction](https://edtechdev.github.io/aied/articles/genai-cybersecurity-ocr-multimodal-instruction-2025/): **Synthesis:** Patel et al. (2025) present an LLM-assisted instructional integration with a virtual cybersecurity lab platform, addressing workforce reskilling needs driven by the digital transformation of Fourth Industrial Revolution (4IR) systems. Recognizing that the workforce must be reskilled and upskilled for STEM skills such as robotics, automation, AI, and security, the authors integrated - [Structuring Transparency: Developing Domain-Specific Generative AI Declaration Frameworks in Higher Education](https://edtechdev.github.io/aied/articles/genai-declaration-frameworks-higher-education/): As generative-ai disrupts higher-ed, institutions increasingly require students to declare AI use. However, generic binary declarations (e.g., "I used GenAI") fail to capture the nuanced application of these tools across different academic tasks. Micallef & Petrovska argue that establishing transparency is key to protecting academic-integrity, promoting ai-literacy, and shifting the focus from pol - [From Unified to Differentiated Materials: Generative AI–Supported Adaptation of EAP Reading Materials](https://edtechdev.github.io/aied/articles/genai-differentiated-eap-reading-materials-2026/): **Synthesis:** Gao (2026) examined whether generative-AI-supported adaptation of English for Academic Purposes (EAP) reading materials chiefly changes passage-level structural complexity or text-embedded functional support. Using a role-prompted workflow (barrier analysis, adaptation, fidelity checking, validation) and a 3×3 between-subjects design (N=135; proficiency × material condition), the st - [Unanticipated Effects of Generative AI on Expertise Pathways and Performance Perception in System Administration](https://edtechdev.github.io/aied/articles/genai-expertise-pathways-sysadmin/): **Rana Abou Khamis, Hala Assal, Ashraf Matrawy** — arXiv preprint (2026). ## Synthesis - [Human-centered GenAI feedback design in higher education: a multisite experiment on direct, reflective, and hybrid approaches to scientific argumentation](https://edtechdev.github.io/aied/articles/genai-feedback-design-multisite-experiment/): **Synthesis:** A multisite, cluster-randomized field experiment (1,176 first-year undergraduates, 48 sections, 4 universities, 3 science domains) compares four feedback designs for scientific argumentation: peer-only, direct GenAI, reflective GenAI (self-evaluation then AI critique), and hybrid (self-evaluation + peer + GenAI). The hybrid condition produced the highest argument-quality gains and c - [Generative AI in Higher Education: A Systematic Review of Opportunities, Challenges, and Pedagogical Innovations (2022–2025)](https://edtechdev.github.io/aied/articles/genai-higher-education-systematic-review-2026/): **Synthesis:** This PRISMA-guided systematic review synthesizes 125 peer-reviewed studies (2022–2025) on generative AI in higher education, documenting exponential adoption (92% student usage by 2025), four primary application domains, and persistent challenges around academic integrity, bias, hallucination, faculty readiness, and digital equity. - [The impact of generative artificial intelligence on academic development of Chinese students in humanities and social sciences](https://edtechdev.github.io/aied/articles/genai-impact-chinese-students-hss/): This large-scale survey of humanities and social sciences (HSS) students in China examines how generative-ai reshapes academic development across four dimensions: usage patterns, effects on learning processes and performance, challenges, and preferred curricular integration approaches. Over half of respondents reported enhanced learning motivation, independent thinking, and creativity, though a su - [Generative AI and linguistic diversity in academic writing and publishing: Perspectives from World Englishes](https://edtechdev.github.io/aied/articles/genai-linguistic-diversity-academic-writing/): Structured scholarly dialogue among five sociolinguists examining how GenAI tools influence academic writing practices, reinforce or disrupt linguistic hierarchies, and impact the legitimacy of diverse English varieties in global scholarly communication. Raises concerns about linguistic homogenization and the marginalization of World Englishes. - [Generative AI Literacy Training Improves Intelligence Analysts’ Discrimination of Real and AI-Generated Images](https://edtechdev.github.io/aied/articles/genai-literacy-image-discrimination/): Kamali et al. (2026) evaluate a Generative AI Literacy training intervention designed to improve intelligence analysts' ability to distinguish real photographs from AI-generated images. In a controlled experiment, trained analysts significantly outperformed untrained controls on image discrimination tasks, with gains persisting on challenging edge cases. This is an important contribution to ai-lit - [Development and evaluation of artificial intelligence literacy training for teacher education students](https://edtechdev.github.io/aied/articles/genai-literacy-training-teacher-education-dbr-2026/): **Synthesis:** Le, Huynh, Dang, Pham, Nguyen, and Nguyen (2026) develop and evaluate a design-based research (DBR) intervention providing GenAI literacy training for teacher education students. Arguing that existing AI literacy programs overemphasize technical knowledge and pre-GenAI tools, the study integrates contemporary AI competency frameworks into a workshop prototype. The workshop was pilot - [When AI Wears Many Hats: The Role of Generative Artificial Intelligence in Marketing Education](https://edtechdev.github.io/aied/articles/genai-marketing-education-roles-2026/): **When AI Wears Many Hats: The Role of Generative Artificial Intelligence in Marketing Education** — Uses multipronged analysis (syllabi review, educator survey, qualitative interviews) and Role Theory + Community of Inquiry model to propose three GAI roles in education: tutor (grasping theoretical concepts), teammate (brainstorming and problem-solv... generative-ai higher-ed pedagogy instructiona - [A meta-analysis of the effect of generative AI on productivity and learning in programming](https://edtechdev.github.io/aied/articles/genai-meta-analysis-programming-learning/): Maier, Gunzenhäuser & Schweisthal (2026) conduct a **meta-analysis synthesizing evidence** on how generative AI tools affect both programming productivity and learning outcomes. This is a **confidence: high** paper due to its synthesis design across multiple studies, addressing the central tension between short-term efficiency gains and long-term skill development. - [Generative AI (GenAI) as a mindtool that supports generative learning (GL)](https://edtechdev.github.io/aied/articles/genai-mindtool-generative-learning/): **Synthesis:** Generative AI (GenAI) as a mindtool that supports generative learning (GL) - [Generative AI and the marginalization of minoritized knowledges in higher education: the case of disability](https://edtechdev.github.io/aied/articles/genai-minoritized-knowledges-disability/): This paper argues that generative-ai systems in higher-ed are not epistemically neutral — they actively marginalize non-hegemonic ways of knowing. Drawing on educational sciences, critical technology studies, and disability studies, Tali-Otmani demonstrates how predominantly Anglophone and Western-centric training data reinforces epistemic coloniality. The situation of persons with disabilities pr - [Examining the Impact of Generative AI on Student Motivation and Engagement: The Mediating Role of Autonomy-Support and Autonomous Motivation in Education](https://edtechdev.github.io/aied/articles/genai-motivation-engagement-2026/): **Synthesis:** Ahmed and Sultan (2026) investigated how perceived autonomy, competence, relatedness, expectancy, and value influence autonomy support for AI use, autonomous motivation, and ultimately student motivation and engagement in GenAI-supported learning. Integrating Self-Determination Theory, Expectancy-Value Theory, and the Technology Acceptance Model, and analyzing data from 297 undergra - [From Enhancement to Over-Reliance: A Mixed-Method Study of Generative AI and Sustainable Learning Performance](https://edtechdev.github.io/aied/articles/genai-over-reliance-learning-2026/): **Synthesis:** Gao, Sun, and Khan (2026) developed a dual-pathway model examining both the positive and negative effects of generative AI use on sustainable learning performance, integrating AI literacy, self-regulated learning, cognitive offloading, and individual differences (polychronicity). Using a mixed-method design with three-wave time-lagged survey data from 623 Chinese university students - [Auditing Institutional Heterogeneity for Generative AI in Patient Education: A Large-Scale Study of 102 US Transplant Handbooks](https://edtechdev.github.io/aied/articles/genai-patient-education-transplant-handbooks/): Li, Padman and Krishnan audit 102 US transplant-center patient handbooks that serve as grounding corpora for generative AI patient-education assistants. They show large institutional heterogeneity in the underlying education materials, undermining the premise that grounding a genAI assistant in local content yields consistent guidance: patients at different institutions can receive materially diff - [Efficacy of an Intensive Generative AI Professional Development Program on Pedagogical Content Knowledge (AI-PCK) and the Comparative Analysis of Learning Gain between Experienced and Pre-service Teachers](https://edtechdev.github.io/aied/articles/genai-pd-ai-pck-learning-gain-2026/): **Synthesis:** This quasi-experimental study of an intensive 8-hour generative-AI professional development program with 163 teachers and pre-service teachers found significant gains across all five AI-PCK components (overall *d* = 2.36), with pre-service teachers showing statistically higher learning gains than experienced teachers (*p* = 0.033). - [Distinguishing performance gains from learning when using generative AI](https://edtechdev.github.io/aied/articles/genai-performance-vs-learning/): This *Nature Reviews Psychology* piece draws a critical distinction that has been under-theorized in AIED research: - [A Comparative Analysis of Institutional and Course Generative AI Policies within Higher Education: Implications for Instruction in Computing Education](https://edtechdev.github.io/aied/articles/genai-policies-higher-ed-computing/): **Synthesis:** A comparative content analysis of institutional GenAI policies and computing-course syllabi in U.S. research-intensive universities, revealing a gap between broadly pro-use institutional guidance and guarded, often prohibition-heavy classroom-level uptake. - [Associations Between Generative AI–Based Pronunciation Feedback and Willingness to Communicate in English: The Mediating Role of English Pronunciation Self-Efficacy](https://edtechdev.github.io/aied/articles/genai-pronunciation-feedback-wtc-2026/): **Synthesis:** Lu et al. (2026) examined, through the lens of Social Cognitive Theory, whether Chinese university EFL learners' perceptions of generative-AI-based pronunciation feedback relate to their willingness to communicate (WTC) in English, with English pronunciation self-efficacy as a hypothesized mediator. Using a cross-sectional survey of 1,701 learners, covariance-based structural equati - [Measuring How Students Rely on Generative AI in Academic Writing: Development and Multi-Source Validation of the Generative AI Reliance Types Scale (GenAI-RTS)](https://edtechdev.github.io/aied/articles/genai-reliance-types-scale/): As generative AI (GenAI) becomes embedded in undergraduate academic writing, *how* students rely on these tools — not merely whether they use them — has emerged as a core question for academic-integrity, student-experience, and educational equity. This study develops and validates the **Generative AI Reliance Types Scale (GenAI-RTS)**, a 20-item instrument measuring four theoretically derived reli - [GenAI as a runaway object in higher education: A socio-cultural view on AI-influenced academic practice in mathematics](https://edtechdev.github.io/aied/articles/genai-runaway-object-math-higher-ed/): **Synthesis:** GenAI as a runaway object in higher education: A socio-cultural view on AI-influenced academic practice in mathematics - [The GenAI Skill Bypass: Mapping Divergent Pathways of University Students and Staff AI Literacy](https://edtechdev.github.io/aied/articles/genai-skill-bypass-literacy/): Higher education institutions are increasingly expected to ensure that both students and staff develop Generative AI (GenAI) literacies. In response, they are introducing professional development programs and embedding GenAI skills within student curricula. However, current educational frameworks typically assume a linear progression of GenAI literacy, implying that foundational technical understa - [\"It is a temptation to get it to do the work…\" Student Experiences of Navigating the Generative AI Landscape in UK Higher Education: A Cross-Institutional Survey with International Comparison](https://edtechdev.github.io/aied/articles/genai-student-experiences-uk-he-survey-2026/): **Synthesis:** The StudentXGenAI Project surveyed more than 7,000 students across 7 UK institutions (September–December 2025) on GenAI use in their studies, comparing findings with a companion Australian survey. A significant minority of students conscientiously object to GenAI use, while most users are honest most of the time and try to avoid submitting direct GenAI outputs — yet students still u - [Comparing Generative AI and teacher feedback: student perceptions of usefulness and trustworthiness](https://edtechdev.github.io/aied/articles/genai-teacher-feedback-comparison/): The largest study in the AEHE 51(5) special issue: a **cross-sectional survey across four Australian universities** (≈192,000 invited; 10,132 volunteered; this paper analyses **6,960 students** who answered the feedback items). It combines quantitative comparison of perceived helpfulness/trustworthiness of GenAI vs teacher feedback with **thematic analysis of 8,642 open-ended responses** (11,903 c - [Not All Students Engage Alike: Multi-Institution Patterns in GenAI Tutor Use](https://edtechdev.github.io/aied/articles/genai-tutor-engagement-patterns/): **Authors:** Youjie Chen, Xixi Shi, Xinyu Liu, Shuaiguo Wang, Tracy Xiao Liu, Dragan Gašević **Year:** 2026 **Venue:** arXiv (cs.CY) Large-scale analysis (N=11,406 students, 200 classes, 10 institutions) of GenAI tutor engagement identifies four session-level engagement types — Deep, Shallow, Routine-Learning, and Exam-Driven — with 10.4% of sessions being shallow copy-paste use and deeper engagem - [A study of GenAI usage by Design Students: Analysis of Survey Results and Journals of AI practices at the Politecnico di Milano in 2025/2026](https://edtechdev.github.io/aied/articles/genai-usage-design-students-survey/): This survey of design students at the Politecnico di Milano (2025/2026), paired with AI-use journals kept during research assignments, examines how generative-ai enters the design process. Reported use is very frequent and concentrated in the early, ideation-heavy stages of projects. - [Contaminated Collaboration: Measuring Gender Bias Transfer in LLM-Assisted Student Writing](https://edtechdev.github.io/aied/articles/gender-bias-transfer-llm-writing/): **Ariyan Hossain, Kazi Kamruzzaman Rabbi, Farig Sadeque, S M Taiabul Haque** (2026). arXiv cs.CL - [Gender Differences in AI Literacy Workshop Outcomes and Deepfake Engagement](https://edtechdev.github.io/aied/articles/gender-differences-ai-literacy-deepfake/): Examines gender differences in AI literacy, safety awareness, and STEM career aspirations among Australian secondary students (Years 7, 8, 10; N=199) from two co-educational government schools after a one-day AI literacy workshop. Male students reported higher STEM career interest; female students were more likely to use AI for schoolwork and seek AI advice. Males were more likely to have created - [Generate-Then-Validate: Question Generation for Education](https://edtechdev.github.io/aied/articles/generate-then-validate-question-gen/): **Synthesis:** A novel generate-then-validate pipeline for educational question generation that reduces LLM hallucination by 62% compared to direct generation, validated on STEM datasets with 89% accuracy and a 23% improvement over baseline LLMs on relevance metrics. The two-stage approach first generates candidate questions, then validates them against domain constraints and pedagogical criteria. - [Does generative AI narrow education-based productivity gaps? Evidence from a randomized experiment](https://edtechdev.github.io/aied/articles/generative-ai-education-productivity-gaps/): **Synthesis:** In a rct with 1,174 participants, Cruces et al. find that generative-ai substantially narrows education-based productivity gaps, closing approximately three-quarters of the initial performance difference between higher- and lower-education workers. Critically, gains are not purely from delegation — lower-education participants retain part of their improvement after AI is removed, an - [Generative AI-enhanced learning experiences for computational thinking: A systematic scoping review and design guidelines](https://edtechdev.github.io/aied/articles/generative-ai-enhanced-learning-experiences-for-computational-thinking-a-systema/): **Synthesis:** This systematic scoping review examines the use of GenAI to support the teaching of computational thinking skills. Results reveal a young but rapidly growing research field, with most interventions focusing on undergraduate students and basic programming tasks. GenAI is typically used as a coder, tutor, debugger, or ideator, with mixed effects on learning outcomes. A key challenge i - [Generative AI without guardrails can harm learning: Evidence from high school mathematics](https://edtechdev.github.io/aied/articles/generative-ai-guardrails-harm-learning/): This landmark field experiment is among the first randomized controlled trials to causally demonstrate that **unguarded generative-AI tutoring can harm skill acquisition**, not merely fail to help. Conducted with **nearly 1,000 high-school math students** across ~50 classes at a large school in Turkey (Fall 2023–2024), the study compares three arms assigned at the classroom level: a **control** ar - [Generative AI interactive textbook in electrotechnics: A four-year comparative study on student performance and inclusion](https://edtechdev.github.io/aied/articles/generative-ai-interactive-textbook-in-electrotechnics-a-four-year-comparative-st/): **Synthesis:** This four-year comparative study presents results of implementing a Generative-AI Interactive Textbook built on GPT-4, integrated into an Electrical Engineering course. With a sample of 736 students, results suggest effects vary by assessment type: statistically significant improvement in mid-term assessment was consistently observed in multi-year analyses, while final assessment re - [Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build](https://edtechdev.github.io/aied/articles/generative-ai-reduced-study-time-math/): This landmark study provides the **first large-scale behavioral and outcome evidence** that generative-ai has fundamentally altered how students study and what they retain. Using a ten-year panel of **3.2 million ALEKS learning interactions** and complementary placement-assessment data, the authors employ a quasi-experimental design that exploits within-curriculum variation in AI susceptibility: t - [Stop Writing for Me: Generative Refusal in AI Tools for Thought](https://edtechdev.github.io/aied/articles/generative-refusal-ai-tools-for-thought/): Position paper exploring "Generative Refusal" — AI tools that strategically withhold text generation to demand user articulation, functioning as a Maieutic Partner rather than a cognitive offloading tool. Argues that in domains where the labor of articulation is central to craft, AI should enhance rather than bypass human cognition. - [Generativism: Toward a Learning Theory for the Age of Generative Artificial Intelligence](https://edtechdev.github.io/aied/articles/generativism-learning-theory/): Li & Zheng argue that the four dominant learning theories — behaviorism, cognitivism, constructivism, and connectivism — show significant conceptual limitations as generative-ai proliferates in higher-ed. They propose **Generativism**, a new learning theory for the generative AI age, which posits that learning increasingly occurs through the iterative co-construction of knowledge between human lea - [GIFT-AI: Teaching the Game and Leveling the Field: Peer and AI Review + Reflection in a Business Writing Course](https://edtechdev.github.io/aied/articles/gift-ai-pairr-business-writing-2025/): **Synthesis:** MacArthur et al. (2025) present the GIFT-AI approach — "teaching the game and leveling the field" — applying the Peer and AI Review + Reflection (PAIRR) model specifically to an upper-division Business Writing course (34 participating students at UC Davis in 2024). The model scaffolds major assignments so students receive peer review, then criteria-based chatbot feedback on the same - [ProPACT: Pair Programming with AI](https://edtechdev.github.io/aied/articles/golrang-propact-pair-programming-2026/): **ProPACT** (Proactive AI-Driven Adaptive Collaborative Tutor) is an AI-driven adaptive tutoring system for pair programming that **treats collaboration itself as the object of instruction.** Unlike individual-centric, reactive systems, it models *dyadic* learning states in real time and intervenes *before* collaborative breakdowns occur, using multimodal sensing and predictive forecasting. - [Comparative Validation of GPT-4o-mini and Teacher Mean Scores for Automated Scoring of Music Analysis Responses: Single-Pass Deployment, Repeatability, and Strategy-Specific Bias](https://edtechdev.github.io/aied/articles/gpt4o-mini-music-analysis-scoring/): **GPT-4o-mini can produce stable rubric-based scores for open-ended music analysis responses, with few-shot chain-of-thought prompting agreeing most strongly with teacher means while RAG systematically over-scores and self-consistency trades individual-level agreement for repeatability.** - [Modernizing Ground Truth: Four Shifts Toward Improving Reliability and Validity in AI in Education](https://edtechdev.github.io/aied/articles/ground-truth-reliability-aied/): The AIED community over-relies on **inter-rater reliability (IRR)** — typically a single Cohen's κ coefficient — as a mechanical gatekeeper for "ground truth." This practice is insufficient and potentially misleading for the complex, noisy realities of educational data. The authors propose **four practical shifts** to strengthen the evidence base of labeled AIED datasets. - [Beyond Access: Guided LLM Scaffolding for Independent Learning in Undergraduate Statistics](https://edtechdev.github.io/aied/articles/guided-llm-scaffolding-independent-learning/): Experimental study comparing Guided vs. Unrestricted LLM access. Explicit training in reasoning-focused scaffolding (stepwise hints, verification) led to significantly better independent performance and self-assessment calibration compared to uncritical reliance. This work emphasizes that ai-literacy is a developmental capacity requiring structured scaffolding and prompt-engineering discipline. It - [HAIML: A Human-Centered AI Metacognitive Learning Model — A Framework for Human Agency and Reflective Learning in the Age of Artificial Intelligence](https://edtechdev.github.io/aied/articles/haiml-human-centered-ai-metacognitive-model-2026/): **Synthesis:** HAIML is a human-centered framework for learning in AI-supported environments that preserves human agency, metacognitive awareness, ethical reasoning, and personal responsibility. Grounded in self-efficacy, self-regulated learning, experiential learning, metacognition, and automation-bias research, the model spans three interconnected layers — Experiential AI Use, Metacognitive Refl - [Analyzing Undergraduate Problem-Solving in Physics Through Interaction With an AI Chatbot](https://edtechdev.github.io/aied/articles/hashmi-socratic-physics-chatbot-2025/): **Synthesis:** A custom Socratic AI chatbot deployed in a large-enrollment introductory mechanics course with 150 first-year STEM majors, demonstrating that AI-driven Socratic dialogue can foster expert-like reasoning while generating fine-grained learning analytics for physics education research. - [SafeTutors: Pedagogical Safety in AI Tutoring](https://edtechdev.github.io/aied/articles/hazra-safetutors-pedagogical-safety-2026/): **SafeTutors** is a benchmark that jointly evaluates safety and pedagogy in AI tutoring systems across mathematics, physics, and chemistry. It argues that **tutoring safety is fundamentally different from conventional LLM safety**: the primary risk is not toxic content but the quiet erosion of learning through answer over-disclosure, misconception reinforcement, and the abdication of scaffolding. - [Human-Centric Artificial Intelligence Pedagogy (HCAP) framework developed from TPACK through integration of artificial intelligence literacy and competency](https://edtechdev.github.io/aied/articles/hcap-human-centric-ai-pedagogy-framework-2026/): **Synthesis:** Chiu (2026) proposes the Human-Centric AI Pedagogy (HCAP) framework, an evolution of the Technological Pedagogical Content Knowledge (TPACK) model designed for the generative AI era. Arguing that AI's agentic autonomy, epistemic complexities, and ethical dimensions render the established TPACK framework insufficient, HCAP integrates five knowledge domains: AI-Technological, AI-Conte - [Agentic AI-driven Immersive Simulation: A Knowledge-Aware Virtual Training Platform for High Dose Rate (HDR) Brachytherapy](https://edtechdev.github.io/aied/articles/hdr-brachytherapy-agentic-ai-simulation-2026/): **Synthesis:** Xu et al. (2026) present an agentic AI-driven immersive simulation for training in **High Dose Rate (HDR) brachytherapy**, integrating VR and mobile computing to create a high-fidelity, risk-free environment for mastering complex procedural skills. A knowledge-aware assistant uses rag to ground agent interactions in authoritative clinical guidelines, providing natural-language inter - [Collaborative AI Literacy Framework](https://edtechdev.github.io/aied/articles/hingle-collaborative-ai-literacy-2025/): **Collaborative AI Literacy Framework** — SEFI 2025. A systematic review of 9 studies (2015–2023) examining how collaborative learning (CL) approaches can be harnessed to build AI literacy across diverse educational contexts. Using the ICAP framework (Interactive–Constructive–Active–Passive) as an analytical lens, the review demonstrates that CL effectively increases AI literacy across activities, - [Who Am I? History-Aware Profiles for Student Simulation in Tutoring Dialogues](https://edtechdev.github.io/aied/articles/history-aware-student-simulation/): A key part of developing large language model (LLM)-powered, automated tutoring tools is student simulation, i.e., using LLMs to role-play as students, which can facilitate tutor model evaluation and training. Existing work mostly focuses on within-dialogue simulation, which lacks context on student knowledge and behavior, partly due to not grounding in past student question-answering or dialogue - [Interpretable Knowledge Tracing](https://edtechdev.github.io/aied/articles/huang-interpretable-knowledge-tracing-2026/): **Interpretable Knowledge Tracing** — A novel framework for dialogue-based Knowledge Tracing that explicitly models both student ability and tutor-turn difficulty using Item Response Theory, producing interpretable cognitive quantities from LLM output logits. Addresses two critical gaps in prior work: ignored question difficulty and opaque latent representations that undermine tutor trust. - [Human-AI Co-Mentorship in Project-Based Learning: A Case Study in Financial Forecasting](https://edtechdev.github.io/aied/articles/human-ai-co-mentorship/): A pedagogical model where human mentors and AI tools jointly support student learning in project-based contexts. Human mentors provide conceptual guidance, debugging, and problem formulation support; AI tools accelerate execution, code generation, and rapid iteration. Demonstrated by Chawla et al. (2026) in a financial forecasting project with high-school students. - [What do you mean by human-AI collaboration: Prerequisite functions and the affordances needed to achieve it](https://edtechdev.github.io/aied/articles/human-ai-collaboration-prerequisite-functions/): Asks what is gained and lost when 'collaboration' is applied freely to human-AI interaction. Argues true collaboration requires symmetric/negotiated relationship, shared goals, low and shifting division of labor, interactive exchange, and mutual modeling. Introduces a 5-level diagnostic taxonomy: Transactional, Situational, Operational, Praxical, and Synergistic. Only Synergistic satisfies full co - [Human-AI collaboration in higher education: Exploring the impact of technology expectations and distrust](https://edtechdev.github.io/aied/articles/human-ai-collaboration-trust-expectations/): **Synthesis:** Human-AI collaboration in higher education: Exploring the impact of technology expectations and distrust - [Human Autonomy and Sense of Agency in Human-Robot Interaction: A Systematic Literature Review](https://edtechdev.github.io/aied/articles/human-autonomy-agency-hri-review-2025/): **Synthesis:** Glawe, Schmeckel, Brauner, and Ziefle (2025) systematically review empirical studies on human autonomy and sense of agency in human-robot interaction (HRI), aiming to bridge the gap between design frameworks and regulatory demands (e.g., the EU AI Act, IEEE Ethically Aligned Design) on one hand and available empirical evidence on the other. Using the PRISMA workflow, they queried fi - [Human-LLM Collaborative Inductive Coding for Conceptualizing K-12 Educator AI Use](https://edtechdev.github.io/aied/articles/human-llm-collaborative-coding-k12-educator-ai/): **Alex Liu, Min Sun, Lief Esbenshade, Michael Xiao, Victor Tian, Zachary Zhang, Kevin He** — arXiv preprint (2026). ## Synthesis - [Comparing human and LLM ordered coding of qualitative data: How coding differences cascade through temporal analysis](https://edtechdev.github.io/aied/articles/human-vs-llm-ordered-coding/): **Authors:** Kamila Misiejuk, Sonsoles López-Pernas, Eduardo Araujo Oliveira, Brendan Eagan, Mohammed Saqr **Source:** Computers and Education: AI, Vol 11 — Open Access (CC BY 4.0) **Source:** Computers and Education: AI, Vol 11 — Open Access (CC BY 4.0) - [It Felt a Bit Eerie": Exploring Humanlike Interactions During Collaborative Writing with an Artificial Agent](https://edtechdev.github.io/aied/articles/humanlike-ai-collaborative-writing/): This comparative user study (n=48) examines how the temporal and visual dimensions of AI collaboration shape the experience of writing-education, revealing that humanlike design features in AI agents create both positive social expectations and unexpected social costs. - [Hybrid E-Assessment in Higher Education: Semi-Automated Grading of Paper-Based Written Examinations](https://edtechdev.github.io/aied/articles/hybrid-e-assessment-semi-automated-grading/): **Hartwig Grabowski, Michael Canz** — cs.AI, cs.CV, cs.CY - [Improving Hybrid Human-AI Tutoring by Differentiating Human Tutor Roles Based on Student Needs](https://edtechdev.github.io/aied/articles/hybrid-human-ai-tutoring-differentiated/): In a large-scale quasi-experiment with 635 students (grades 5-8), hybrid human-AI tutoring produced substantial gains over AI-only tutoring: +25% time on task, +36% skill proficiency, and +61% standardized academic growth. - [Hybrid intelligence feedback systems in design thinking development: Stage-specific insights on pedagogical effects and characteristics of generative AI and instructors](https://edtechdev.github.io/aied/articles/hybrid-intelligence-feedback-systems-in-design-thinking-development-stage-specif/): **Synthesis:** This study compares the pedagogical effects on students' design thinking and students' perceptions of feedback systems by GenAI and human instructors. A within-class randomized experimental design with 80 undergraduates revealed no significant overall difference in learning gains but identified respective stage-specific strengths: GenAI proved more effective during the empathise sta - [Hypergamigication Through Integrating Game Engines and Learning Management Systems: Ender's Game](https://edtechdev.github.io/aied/articles/hypergamification-game-engine-lms/): **Araz Yusubov, Michael Bechtel, Tangiz Alizada** — arXiv preprint (2026). ## Synthesis - [Measuring Cognitive Engagement in Collaborative Discourse with an Extended ICAP Framework: Comparing Human Annotation, In-Context Learning, and Reflective LLM Agents](https://edtechdev.github.io/aied/articles/icap-cognitive-engagement-llm-agents/): **Lan Anh Do, Hanling Jiang, Shuchin Aeron, Ayanna K. Thomas** — CogSci 2026 (accepted full paper). - [ICLE++: Modeling Fine-Grained Traits for Holistic Essay Scoring](https://edtechdev.github.io/aied/articles/icle-plus-plus-essay-scoring/): Introduces ICLE++, a new annotated corpus of persuasive student essays that addresses critical limitations of the dominant ASAP benchmark in automated-essay-scoring research. Unlike ASAP — used by virtually all recent AES models but limited to U.S. grade 7–10 native-English essays — ICLE++ provides both holistic scores and fine-grained trait-specific annotations, enabling evaluation of cross-corpu - [Would You Let a Humanoid Play Storytelling With Your Child? A Usability Study on LLM-Powered Narrative Human-Robot Interaction](https://edtechdev.github.io/aied/articles/icub-humanoid-storytelling-llm-hri-2025/): **Synthesis:** Lombardi et al. (2025) present a framework for enhancing the attention and social capability of the iCub humanoid robot by integrating advanced perceptual abilities that recognize social cues, understand surroundings through generative models such as ChatGPT, and respond with contextually appropriate social behaviour. They implement an interaction task using a narrative (storytellin - [Generative AI and the Productivity Divide: Human-AI Complementarities in Education](https://edtechdev.github.io/aied/articles/idan-anand-genai-productivity-divide-2026/): **Synthesis:** Idan & Anand (2026) conduct an RCT showing that GenAI access significantly increases task performance on average — but the gains are highly uneven, NOT predicted by GPA or prior knowledge, but by AI Interaction Competence (AIC): the ability to elicit, filter, and verify model outputs. High-AIC participants realized outsized gains while low-AIC saw limited or negative returns. A scaf - [IKS-Instruct: A 24,000-Example Multilingual Dataset for Teaching Language Models Indian Knowledge Systems](https://edtechdev.github.io/aied/articles/iks-instruct-dataset-indian-knowledge/): Presents a 24,795-example multilingual instruction dataset for teaching LLMs to deliver educational content grounded in Indian Knowledge Systems. Spans seven languages and bridges a gap in non-Western pedagogical content for instruction tuning. Demonstrates that domain-specific educational datasets improve LLM performance on culturally grounded knowledge tasks. - [Informal Learning Emerges in Everyday Human-LLM Interaction](https://edtechdev.github.io/aied/articles/informal-learning-everyday-human-llm-interaction/): As LLMs take over task execution, a central worry is that everyday AI use becomes cognitive offloading that erodes people's own capability development. This study analyses 128,569 naturalistic human-LLM conversations, translating learning-science constructs into turn-level behavioural signatures to test whether informal learning actually emerges in routine use. - [INSIDE the Student's Mind: Jointly Modeling Latent Reasoning and Action in LLM Student Simulators](https://edtechdev.github.io/aied/articles/inside-llm-student-simulator-reasoning-2026/): **Synthesis:** Niousha, Kang, & Norouzi (2026) introduce **INTERNAL STUDENT DIALOGUE (INSIDE)**, a student modeling framework that fine-tunes LLMs to both *act* like students and *think* like them. Two students may submit identical work for entirely different reasons, so INSIDE generates internal dialogue grounded in Bloom's Taxonomy across cognitive, affective, and action dimensions, fine-tuning - [A Framework for Institutional Change in the Age of AI](https://edtechdev.github.io/aied/articles/institutional-change-framework-ai/): Perl-Nussbaum & Finkelstein (2026) adapt institutional-change models to generative AI as an **arrival technology** — one that entered classrooms before pedagogical evidence existed — yielding a six-dimension framework and design implications for leading change under uncertainty (faculty-development, higher-ed, educational-policy-ai). - [Policy Fragmentation or Institutional Alignment? Institutional Governance of AI in Universities and Business Schools](https://edtechdev.github.io/aied/articles/institutional-governance-ai-universities/): **Synthesis:** This study analyzes AI policies across higher education institutions in 34 U.S. states, using NLP to uncover a clear divergence: university-level policies emphasize data security and risk mitigation, while school-level policies (when they exist) focus on pedagogical applications and tool usage. Relatively few business schools maintain distinct AI policies, creating misalignment with - [Instructional Agents: Reducing Teaching Faculty Workload through Multi-Agent Instructional Design](https://edtechdev.github.io/aied/articles/instructional-agents-multi-agent-course-gen/): **Synthesis:** Instructional Agents is a multi-agent LLM framework that automates end-to-end course material generation by simulating role-based collaboration among Teaching Faculty, Instructional Designer, Teaching Assistant, Course Coordinator, and Program Chair agents, all structured around the ADDIE instructional design framework. Evaluated across 5 university courses, the system supports four - [Role of Instructional Guidance in Generative AI-Assisted Learning](https://edtechdev.github.io/aied/articles/instructional-guidance-genai-learning/): Investigates how instructional guidance shapes student-AI interaction in higher-ed. Introduces a **five-step prompting framework** grounded in Generative Learning Theory (GLT) to guide learner interaction during review activities. Three conditions tested in a controlled experiment: slide-based learning, unprompted AI-supported learning, and prompted AI-supported learning. - [Interactive learning dashboards: rethinking learning visualisations as engagement tools](https://edtechdev.github.io/aied/articles/interactive-learning-dashboards-engagement/): **Synthesis:** Graf et al. (2026) transformed a conventional Learning Analytics Dashboard (LAD) into an interactive ILAD by adding an LLM-powered pedagogical agent and a Judgement of Learning (JoL) self-assessment feature. In a 5-week case study with 30 CS students across three conditions (no agent, "telling" agent, "eliciting" agent), the elicit condition produced more reflection and more accurat - [Understanding How International Students in the U.S. Are Using Conversational AI to Support Cross-Cultural Adaptation](https://edtechdev.github.io/aied/articles/international-students-conversational-ai-adaptation/): Understanding How International Students in the U.S. Are Using Conversational AI to Support Cross-Cultural Adaptation **Nourian et al. (2026)** — Multiple institutions. arXiv cs.HC. - [ISD Agent Benchmark](https://edtechdev.github.io/aied/articles/jeon-isd-agent-bench-2026/): **ISD-Agent-Bench** is a comprehensive benchmark for evaluating LLM-based instructional design agents, comprising **25,795 scenarios** generated via a Context Matrix framework that combines 51 contextual variables with 33 ISD sub-steps from the ADDIE model. It employs a multi-judge evaluation protocol to mitigate LLM-as-judge bias. - [MathBuddy: Affective Math Tutoring](https://edtechdev.github.io/aied/articles/kar-mathbuddy-affective-math-tutoring-2025/): **MathBuddy: Affective Math Tutoring** — EMNLP 2025 Demo. An emotionally aware LLM-powered mathematics tutor that dynamically models student emotions from both conversational text and facial expressions, aggregating multimodal affective signals to shape pedagogically appropriate LLM responses. Maps detected affective states to relevant pedagogical strategies, achieving a +23-point win rate advanta - [LLM Fallacy Misattribution in Education](https://edtechdev.github.io/aied/articles/kim-llm-fallacy-misattribution-2026/): **The LLM Fallacy** is a cognitive attribution error in which individuals misinterpret LLM-assisted outputs as evidence of their own independent competence — producing a systematic gap (∆C) between perceived and actual capability. This divergence persists regardless of whether the LLM output is correct or erroneous, and it is driven by three system-level properties (opacity, fluency, and interacti - [Knowledge-Based Design Requirements for Generative Social Robots in Higher Education](https://edtechdev.github.io/aied/articles/knowledge-based-design-generative-social-robots-2026/): **Synthesis:** Vonschallen, Oberle, Schmiedel, and Eyssel (2026) adopt a knowledge-based design perspective to investigate what information tutoring-oriented generative social robots (GSRs) require to function responsibly and effectively in higher education. Recognizing that GSRs powered by large language models enable adaptive, conversational tutoring but introduce risks such as misinformation, o - [Knowledge Distillation for Automated AI Tutor Evaluation](https://edtechdev.github.io/aied/articles/knowledge-distillation-ai-tutor-evaluation/): Addresses the lag between LLM integration into K-12/higher education and reliable methods for evaluating pedagogical quality. The authors introduce a knowledge-distillation approach to automate AI-tutor evaluation, distilling expert judgments of pedagogical quality into a scalable evaluator. - [Detecting Knowledge Gaps from Conversational AI Interactions Using Curriculum Prerequisite Graphs](https://edtechdev.github.io/aied/articles/knowledge-gap-detection-ai-tas/): This paper introduces a pipeline that maps student questions directed at a conversational AI teaching assistant to curriculum topics using a few-shot text classifier, grounded in a GPT-4-extracted prerequisite knowledge graph. Evaluated on 1,340 question events from 164 graduate students in an AI course, the classifier achieved 80.0% accuracy across 43 labels (42 topics + abstention). Topic-level - [Interpretable Knowledge Tracing via IRT](https://edtechdev.github.io/aied/articles/knowledge-tracing-irt/): Two critical gaps in dialogue-based Knowledge Tracing (KT): - [From Confusion to Consolidation: A Staged Conversational Workflow for Post-Lecture Review](https://edtechdev.github.io/aied/articles/knowloop-confusion-to-consolidation-2026/): **Synthesis:** KnowLoop, a dual-agent conversational system for post-lecture review, structures learning around three stages—Recognize (mark in-situ confusion during lectures), Resolve (Teaching Assistant provides context-grounded clarification), and Consolidate (Peer scaffolds reflective teach-back). A 22-participant study shows confusion points serve as personalized review anchors, lecture-groun - [KT4EQG: Personalized Exercise Question Generation via Knowledge Tracing](https://edtechdev.github.io/aied/articles/kt4eqg-personalized-question-generation/): **KT4EQG: Personalized Exercise Question Generation via Knowledge Tracing** bridges two key AI-in-education paradigms: personalized-learning through question generation and learning-analytics through knowledge tracing. Rather than generating generic practice questions, KT4EQG uses a Knowledge Tracing model to first identify the knowledge concept that would maximize a student's potential improvemen - [Kutti AI: A Voice-First, Offline-Capable Learning Companion with Real-Time Struggle Detection for Visually-Impaired Children](https://edtechdev.github.io/aied/articles/kutti-ai-voice-first-learning-companion/): Kutti AI addresses a persistent equity gap in educational technology: nearly all edtech assumes a visual interface, excluding an estimated 1.4 million blind children worldwide. The system inverts this assumption entirely, making spoken conversation the primary and sufficient learning modality — children hear curriculum content, answer aloud, and receive spoken feedback with no visual dependency, p - [Revisiting the Hint Button: Consistent Negative Associations Between Unproductive Hint Use and Learning Outcomes in Intelligent Tutoring Systems](https://edtechdev.github.io/aied/articles/lak2026-hint-button-unproductive-use/): **Synthesis:** A three-semester, 999-student analysis of hint usage in a K-12 mathematics ITS finds that two simple, interpretable indicators—premature hint requests and superficial hint reading—are consistently associated with reduced learning gains, even after controlling for prior knowledge. The work argues from an affordance perspective that the persistent "hint button" design common across IT - [LaTA: A Drop-in, FERPA-Compliant Local-LLM Autograder for Upper-Division STEM Coursework](https://edtechdev.github.io/aied/articles/lata-ferpa-compliant-local-llm-autograder/): LaTA: A Drop-in, FERPA-Compliant Local-LLM Autograder for Upper-Division STEM Coursework **Rodríguez (2026)** — Oregon State University. Submitted to Computers & Education. - [Beyond Compliance: A Proposed Framework for Ethical Governance of Student Data in Learning Analytics](https://edtechdev.github.io/aied/articles/league-ethical-governance-student-data-2026/): **Beyond Compliance: A Proposed Framework for Ethical Governance of Student Data in Learning Analytics** — Proposes LEAGUE framework (Lawfulness, Equity, Agency, Governance, Utility, Ethics by Design) for ethical governance of student data in learning analytics. Synthesizes scholarship across LA, educational data mining, data ethics, educational policy, v... learning-analytics privacy equity ethic - [Patterns of Learner-AI Interaction and Academic Performance in an Object-Oriented Programming Course](https://edtechdev.github.io/aied/articles/learner-ai-interaction-patterns-oop/): Examines how different forms of learner-AI interaction relate to learning outcomes in object-oriented programming courses. Identifies distinct patterns of GenAI use among students and correlates them with academic performance, finding that certain interaction patterns (seeking explanation rather than code generation) are associated with stronger learning outcomes. - [Enhancing learner-centered feedback with AI: teachers'' practices and perceptions](https://edtechdev.github.io/aied/articles/learner-centered-feedback-ai/): An empirical study of **21 higher-education teachers** using **PolyFeed**, an AI-powered feedback tool combining (1) a **BERT-based ML model** (from Aldino et al. 2024) that detects which learner-centered feedback components are missing from teacher-written feedback and suggests them, and (2) **ChatGPT-4o mini** to rephrase/enhance the teacher's draft. Teachers gave feedback on a simulated student - [Learning behavior accounts for background-related advantage in AI-assisted education](https://edtechdev.github.io/aied/articles/learning-behavior-background-advantage-ai-ed/): Investigates why AI-for-education shows inconsistent average effects, arguing that learning behavior explains background-related advantage: students from advantaged backgrounds engage with AI tools in ways that compound gains, while others do not. Prior ed-tech research shows average effects mask heterogeneity; this paper quantifies the behavioral mechanism. - [Learning by Chatting? Investigating the Impact of Generative AI on Information Seeking and Learning](https://edtechdev.github.io/aied/articles/learning-by-chatting-genai-impact/): **Shravika Mittal, Su Lin Blodgett, Q. Vera Liao** - [Learning Engagement Assistant (LEA): Cross-Course Scalability and Classroom Evaluation of an Agentic AI Tutoring System](https://edtechdev.github.io/aied/articles/learning-engagement-assistant-lea/): LEA (Learning Engagement Assistant) is an **agentic AI tutoring system** that couples course-specific retrieval-augmented generation (RAG) with structured knowledge-tracing / Knowledge Component (KC) models across integrated Chat, Tutor, and Quiz modes. This paper reports the first real-student classroom deployment of LEA (n = 8, STEM course CMP511) and the first empirical test of its cross-course - [Learning-to-learn in the age of generative AI: A scoping review and conceptual framework](https://edtechdev.github.io/aied/articles/learning-to-learn-in-the-age-of-generative-ai-a-scoping-review-and-conceptual-fr/): **Synthesis:** This paper presents a scoping review of learning-to-learn (L2L) definitions within pedagogical and psychological literature, identifying 21 relevant publications via PRISMA-ScR. It proposes a novel three-layered framework organized by conceptual broadness: Dimensions (cognitive and metacognitive skills), Processes (self-regulation), and Tools (retrieval practice). The framework maps - [Learning to Prompt: Improving Student Engagement with Adaptive LLM-based High-School Tutoring](https://edtechdev.github.io/aied/articles/learning-to-prompt-adaptive-tutoring/): **Po-Chin Chang, Nicholas Hogan, Aske Plaat, Michiel T. van der Meer** (2026). arXiv cs.AI preprint - [Learning with machines: Toward a theory of epistemic co-agency](https://edtechdev.github.io/aied/articles/learning-with-machines-toward-a-theory-of-epistemic-co-agency/): **Synthesis:** Samuel (2026) introduces the **Epistemic Entanglement Framework**, a theory-informed model for understanding how learners engage with generative AI (GenAI) systems. Arguing that existing learning theories (constructivism, sociocultural theory, connectivism) presume human-centered epistemic agency and cannot account for the ways GenAI simulates reasoning, reframes arguments, and co-c - [Rethinking Higher Education: From Fixed Curricula to Learnity Graphs](https://edtechdev.github.io/aied/articles/learnity-graphs-lifelong-learning-framework-2026/): **Synthesis:** Szekely, Gal-Ezer & Harel (2026) argue that AI-mediated knowledge access warrants rethinking fixed higher-education curricula, proposing "learnity graphs" — structured representations of learning as interconnected units of knowledge, skills, experience, and artifacts — as a lifelong-learning framework that integrates academic, professional, and personal learning. - [LearnMate^2: Design and Evaluation of an LLM-powered Personalized and Adaptive Support System for Online Learning](https://edtechdev.github.io/aied/articles/learnmate2-llm-adaptive-learning/): LearnMate^2: Design and Evaluation of an LLM-powered Personalized and Adaptive Support System for Online Learning **Wang, Lee, & Mutlu (2026)** — University of Wisconsin-Madison. CHI-related publication. - [LearnOpt: Recovering the Latent Cognitive Structure of Standardized Examinations via Knowledge Graphs and Constrained Optimization](https://edtechdev.github.io/aied/articles/learnopt-exam-cognitive-structure/): Standardized examinations are typically treated as uniform syllabus coverage problems. LearnOpt recovers stable latent cognitive structures diverging systematically from official syllabi, using LLM-tagged questions and constrained optimization. Applied to 9 years of NEET questions (n=1,496) and JEE Advanced questions. Finds NEET latent skill distribution is stable within syllabus regimes (KL 0.004 - [LecturaAgents: A Multi-Agent Framework for Adaptive Personalized AI-Assisted Learning and Embodied Teaching](https://edtechdev.github.io/aied/articles/lecturaagents-multi-agent-teaching/): **Jaward Sesay, Yue Yu, Siwei Dong, Yemin Shi, Guangyao Chen, Borje F. Karlsson** (2026). arXiv cs.CL - [Less Deliberate in Teams: Student LLM Use Across Individual and Collaborative Work](https://edtechdev.github.io/aied/articles/less-deliberate-teams-llm/): **Sehrish Basir Nizamani, Zannah Ziew, Saad Nizamani, Khyati Goyal** — ACM SIGCSE Virtual 2026, submitted 29 Jun 2026 - [Let''s Chat: Leveraging Chatbot Outreach for Improved Course Performance](https://edtechdev.github.io/aied/articles/lets-chat-chatbot-outreach-2026/): Meyer, Page, Mata et al. (2026) ran two pre-registered RCTs at Georgia State University testing a **non-generative** academic chatbot that texted students 2–3 customized nudges per week in large-enrollment online courses. It raised the probability of earning an A or B by **4 percentage points** — driven entirely by women in Microeconomics (+7 grade points, +11 pp A/B, −10 pp DFW) — via a task-comp - [Leveraging complex systems: Leading for transformative change](https://edtechdev.github.io/aied/articles/leveraging-complex-systems-leading-for-transformative-change/): **Synthesis:** Dawson and Pardo (2026) argue that generative AI (GenAI) is precipitating a systemic, paradigmatic transformation of education — not a passing fad — and that traditional bureaucratic leadership structures are ill-suited for its pace, scale, and sociotechnical nature. They introduce the **SPARK framework** (Systems, Problem, Analysis, Research, and Knowledge brokerage), a pragmatic m - [A systematic review of generative AI in education: Empirical insights from a human–AI interaction perspective](https://edtechdev.github.io/aied/articles/liang-genai-systematic-review-human-ai-2026/): **Synthesis:** Liang, Yang, Sha, Gašević, Yan & Chen (2026) systematically review 56 empirical studies on GenAI in education through the AIED-HCD framework, analyzing three human–AI interaction modes along dimensions of human control and AI automation. They find that practice remains cautious toward high-AI-automation modes, but a high-control + high-automation mode is emerging as a trend — sugges - [Exploring the Effectiveness of Using LLMs for Automated Assessment of Student Self Explanations in Programming Education](https://edtechdev.github.io/aied/articles/llm-automated-assessment-student-self-explanations/): This paper presents a rigorous empirical comparison between llm-based and semantic similarity methods for automated-grading of student self-explanations in programming education. The task is framed as binary classification — determining whether a student's explanation of a worked-example step is correct or incorrect. - [Are LLM-based Chatbots Good Enough to Support Computer Science Students in Multiple-Choice Exercises?](https://edtechdev.github.io/aied/articles/llm-chatbots-cs-multiple-choice/): Investigates LLM chatbots' performance on 70 MCQs for a university CS lecture on interactive visual data analysis, comparing with student performance. GPT-4o and GPT-5 significantly outperformed smaller models. A user study in two courses showed that presenting ChatGPT answers with explanations did NOT generally improve student performance. - [Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety](https://edtechdev.github.io/aied/articles/llm-children-reading-story-generation/): Using an expert-designed children's reading curriculum and stories generated by GPT-4o and Llama 3.3 70B as training data, the authors fine-tuned three different 8B-parameter LLMs. **The fine-tuned 8B models outperformed zero-shot GPT-4o and Llama 3.3 70B on difficulty-related metrics** while showing negligible safety issues. - [Benchmarking Large Language Models for Diagnosing Students' Cognitive Skills from Handwritten Math Work](https://edtechdev.github.io/aied/articles/llm-cognitive-diagnosis-handwritten-math/): **MathCog** benchmark (3,036 teacher-annotated diagnostic verdicts, 639 handwritten responses, 18 LLMs): all models severely underperform (macro F1 < 0.5) — over-attributing evidence, overthinking minimal cues, hallucinating nonexistent evidence (hallucination-risk) — calling for evidence-aware architectures and human-in-the-loop-ai designs (knowledge-tracing, multimodal, benchmark). - [Using LLMs to Detect Growth in Computational Thinking in Introductory Physics](https://edtechdev.github.io/aied/articles/llm-computational-thinking-physics-2026/): **Synthesis:** Savage, Shanker, Michlitsch & Rebello (2026) investigate using LLMs to evaluate students' written explanations of computational physics problems at scale. Establishing a human-coded baseline grounded in CT literature, they found significant growth in Data Practices and Computational Problem-Solving Practices. The LLM successfully mirrored human evaluations for these constructs, but - [Can Large Language Models Foster Critical Thinking, Teamwork, and Problem-Solving Skills in Higher Education?: A Literature Review](https://edtechdev.github.io/aied/articles/llm-critical-thinking-teamwork-review/): **Synthesis:** Can Large Language Models Foster Critical Thinking, Teamwork, and Problem-Solving Skills in Higher Education?: A Literature Review - [LLMs for Culturally Relevant K-12 Pedagogy](https://edtechdev.github.io/aied/articles/llm-cultural-relevance-k12/): Explores LLMs to support K-12 teachers in designing culturally relevant pedagogy. An exploratory pilot with four K-12 teachers found the CulturAIEd tool enhanced teachers' confidence in identifying opportunities for cultural responsiveness in learning activities and in making culturally responsive modifications to existing activities. Addresses equity gaps in AI educational tools by centering cult - [LLM-Generated Design Problems for Assessing Higher-Order Thinking in Project-Based Learning](https://edtechdev.github.io/aied/articles/llm-design-problems-hot-pjbl/): Introduces 'design problems' (DPs): concise, scenario-based prompts that require applying knowledge in transfer contexts, generated with LLMs to assess higher-order thinking (HOT) in project-based learning. Traditional PjBL assessments often fail to capture HOT, especially transfer; DPs target that gap. - [Distinguishing Artificial from Authentic: Evaluating LLMs for Detecting LLM-Generated Content](https://edtechdev.github.io/aied/articles/llm-detecting-llm-generated-content-education/): As students increasingly use llms to draft written responses and program code, this study asks whether LLMs can reliably detect their own generated content across educational task types — programming exercises, reflective writing, and short-answer questions. Using authentic student responses alongside multiple LLM-generated variants, the authors evaluate detection under varied prompting strategies - [From Evaluated Models to Evaluation Aids: A Multi-Evidence Study of LLM-Based Difficulty Calibration for Programming Examinations](https://edtechdev.github.io/aied/articles/llm-difficulty-calibration-programming-exams-2026/): **Synthesis:** Yan, Xiong, Li & Chen (2026) reposition LLMs from benchmark targets to auxiliary evidence sources for interpreting programming-exam difficulty, showing that AI difficulty estimates correlate strongly with student pass rates across parallel-class finals (rho ≈ −0.87 at problem level) while explicitly bounding that these scales must not be used for individual student evaluation or aut - [Exploring the Value of Diverse LLM Explanations in Introductory Programming](https://edtechdev.github.io/aied/articles/llm-diverse-explanations-programming/): Bernstein, Denny, Leinonen et al. (2026) investigate whether providing students with multiple, diverse LLM-generated explanations of code (rather than a single 'best' explanation) improves comprehension in introductory programming. Their findings show that exposure to diverse explanations significantly outperforms single-explanation conditions on measures of conceptual understanding and code compr - [From Memorization to Creation: Evaluating the Cognitive Depth of LLM-Generated Educational Questions](https://edtechdev.github.io/aied/articles/llm-educational-question-cognitive-depth/): LLM-generated educational questions show varying cognitive depth; models excel at factual recall but struggle with higher-order thinking questions per Bloom's taxonomy. - [LLM-Based Educational Simulation: Evaluating Temporal Student Persona Stability Across ADHD Profiles](https://edtechdev.github.io/aied/articles/llm-educational-simulation-adhd/): Gonnermann-Müller, Haase & Leins (2026) evaluate whether **LLM-generated student personas simulating ADHD profiles** maintain stable and realistic behavioral patterns over time. This addresses a critical question for using LLMs in educational research and teacher training: can simulated learners reliably represent neurodivergent students? - [To Facilitate or not to Facilitate: Human and LLM Facilitator Tendencies in Online Discussions](https://edtechdev.github.io/aied/articles/llm-facilitation-timing-online-discussions/): **Dimitris Tsirmpas, Katerina Korre, John Pavlopoulos** — arXiv preprint (2026). ## Synthesis - [The LLM Fallacy and Misattribution of Competence](https://edtechdev.github.io/aied/articles/llm-fallacy-misattribution/): Three system properties enable the fallacy via two cognitive mediators: - [LLM-Generated Feedback in Introductory Programming: A Classroom Study](https://edtechdev.github.io/aied/articles/llm-feedback-programming-classroom/): Presents a **large-scale classroom study** (N=215 students, 6,693 submissions across 17 labs) deploying AI-generated feedback through a randomized protocol in an introductory Python programming course. Students received one of three conditions: natural language hints, AI-generated failing test cases, or no AI feedback (control). The resulting dataset, **ProgFeed**, captures fine-grained temporal l - [Automated Grading of Handwritten Mathematics Using Vision-Capable LLMs](https://edtechdev.github.io/aied/articles/llm-handwritten-math-grading/): Automated grading systems have enabled scalable assessment for many response types, but handwritten mathematics remains a barrier due to the complexity of multi-step solutions. Vision-capable large language models (LLMs) offer new opportunities here, yet their reliability in authentic instructional settings remains poorly understood. - [A review of intervention designs of LLM Integration in Undergraduate Computer Science Education](https://edtechdev.github.io/aied/articles/llm-intervention-design-cs-review/): This scoping review analyzed **13 experimental studies** on LLM integration in undergraduate cs-education, examining how intervention design choices shape learning outcomes. The central finding: **LLM effectiveness depends less on the model itself than on pedagogical design**. - [Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction](https://edtechdev.github.io/aied/articles/llm-item-difficulty-prediction/): This paper introduces Epi2Diff (Episode to Difficulty), a framework that maps LLM reasoning traces into cognitively grounded episode sequences for predicting human item difficulty in assessment. The authors argue that difficulty should be viewed not only as a property of item text but also as an observable consequence of problem-solving burden. By analyzing reasoning traces from large reasoning mo - [Rethinking LLM-Judged Helpfulness as a Pedagogy Signal: A Pre-Registered Audit Across Tutor Models](https://edtechdev.github.io/aied/articles/llm-judged-helpfulness-pedagogy-signal/): Pre-registered study auditing whether general-purpose helpfulness rubrics can distinguish direct answer-giving from pedagogical guidance in LLM tutors. Uses deterministic detectors for answer leakage and next-turn independent work across three tutor models. Finds that helpfulness ratings conflate genuine pedagogical scaffolding with simply giving correct answers. - [The Easy Trap: Why LLMs Underestimate Misconception-Driven Difficulty](https://edtechdev.github.io/aied/articles/llm-misconception-difficulty-easy-trap/): LLMs systematically underestimate the difficulty of misconception-driven items ('The Easy Trap'). While LLM ratings show moderate rank correlation with empirical student difficulty (rho=0.52-0.70), they misclassify several fraction items as easy that are among the hardest for students (e.g., 34% correct). LLMs approximate curricular rather than cognitive difficulty. - [Exploring the Design Space of LLM-Based Programming Support in CS Education: A Scoping Review through the Lens of Assistance Governance](https://edtechdev.github.io/aied/articles/llm-programming-support-governance-cs-education/): This scoping review synthesizes 90 peer-reviewed llm-based programming support systems in cs-education to make explicit how each system bounds, enacts, and controls assistance — decisions the authors argue are usually left implicit. They introduce the **PEA framework**: Policy (what help is allowed or restricted), Enforcement (how boundaries are operationalized through interaction and system behav - [Aligning LLM-Simulated and Human Examinees for Psychometric Calibration: A Cognitive Diagnostic Profiling Approach](https://edtechdev.github.io/aied/articles/llm-psychometric-calibration-cdp/): Proposes Cognitive Diagnostic Profiling (CDP), a zero-shot framework that dramatically improves LLM-simulated examinee alignment with human test-takers. With CDP, IRT difficulty Spearman correlations rose from 0.24 to 0.90, and RMSE fell from 6.31 to 0.90. Makes LLM-simulated examinees practical for operational test development. - [Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognition](https://edtechdev.github.io/aied/articles/llm-reasoning-traces-metacognition/): This preregistered between-subjects study (N=559) provides the first rigorous evidence that llm reasoning traces — increasingly common in AI interfaces — do not improve performance and can actively impair it. More critically, they create a dangerous metacognition blind spot: participants substantially overestimate their performance regardless of trace format. - [Four Types of LLM Reliance and Their Predictors Among Undergraduate Writers: A Mixed-Methods Study at a Minority-Serving R1 University](https://edtechdev.github.io/aied/articles/llm-reliance-types-undergrad/): Hossain (2026) develops a typology of LLM reliance among undergraduate writers at a minority-serving R1 institution, identifying four distinct profiles: strategic scaffolders who use AI for idea generation and structure, critical editors who revise AI output substantially, passive acceptors who submit AI-generated text with minimal changes, and uncritical delegators who offload entire assignments. - [LLM-assisted sentiment analysis for integrated computational and qualitative mixed methods education research: A case study of students' written reflection assignments](https://edtechdev.github.io/aied/articles/llm-sentiment-analysis-education-research/): **LLM-Assisted Sentiment Analysis for Mixed-Methods Education Research** demonstrates how LLMs can serve as scalable qualitative research assistants, enabling researchers to investigate multiple demographic variables simultaneously rather than being limited to simple binary comparisons. Using 151 longitudinal written reflections from a study abroad program, the authors show that LLM-assisted senti - [What Don't You Understand? Using Large Language Models to Identify and Characterize Student Misconceptions About Challenging Topics](https://edtechdev.github.io/aied/articles/llm-student-misconception-identification/): This paper presents a systematic two-stage methodology for surfacing student misconceptions at scale. Drawing on 3,802 medical student enrollments across 5 biomedical science courses (9 course periods, 40-50 quizzes each), Parker and Zavala-Cerna first use quantitative quiz-level performance metrics to identify challenging topics, then deploy LLMs to analyze quiz questions, student response patter - [LLM Student Modeling and Long-Term Memory Architecture](https://edtechdev.github.io/aied/articles/llm-student-modeling-memory/): Current AI tutoring systems treat each session as independent. Adaptive systems use real-time knowledge tracing (e.g., knowledge-tracing-irt) but rarely retain a longitudinal student model that evolves across semesters. Longitudinal personalization is essential for effective scaffolding because: - [Simulating Students or Sycophantic Problem Solving? On Misconception Faithfulness of LLM Simulators](https://edtechdev.github.io/aied/articles/llm-student-simulation-misconception-faithfulness/): This paper exposes a critical failure mode in using LLMs as simulated students for intelligent-tutoring development and evaluation. The authors introduce **misconception faithfulness** — the property that a simulated student holds a coherent, misconception-driven belief state and updates it *only* when feedback addresses the underlying misconception — and show that across seven LLMs (4B to 120B pa - [Can LLMs Effectively Simulate Human Learners? Teachers' Insights from Tutoring LLM Students](https://edtechdev.github.io/aied/articles/llm-student-simulation-teacher-insights/): **Synthesis:** Semi-structured interviews with 12 teachers who tutored LLM-simulated students (MathDial dataset) reveal key authenticity gaps: overly complex language, lack of emotions, unnatural attentiveness, and logical inconsistency. The study categorizes four real-world student behavior types along scaffolding and presence dimensions, and provides design guidelines for building higher-fidelit - [A Semi-Automated System for Generating Dialogue-Based TTS Lessons Using Large Language Models: An Exploratory Study of Educational Potential](https://edtechdev.github.io/aied/articles/llm-tts-dialogue-lesson-generation/): **Gendo Kumoi, Fumie Watanabe, Tota Suko, Takashi Ishida, et al. (2026)** - arXiv preprint (IEEE). arXiv preprint. - [What out-of-the-box LLMs can(t) do in law? A Turing test in Italian exams for lawyers, judges and notaries](https://edtechdev.github.io/aied/articles/llm-turing-test-italian-legal-exams-2026/): **Synthesis:** This paper reports a blind Turing Test evaluating leading LLMs on three Italian professional legal examinations: the Bar exam, Judges exam, and Notary exam. LLMs generated full written papers that were anonymised and graded by expert examiners using real examination rubrics. Results show marked variance across models and tasks: some LLMs match or exceed human passing thresholds on c - [Confirming Correct, Missing the Rest: LLM Tutoring Agents Struggle Where Feedback Matters Most](https://edtechdev.github.io/aied/articles/llm-tutoring-feedback-diagnosis-gap/): - scaffolding - intelligent-tutoring ## Connected Articles - [Balancing AI responsibility with privacy, safety, and utility: Unlearning in large language models for mathematics education](https://edtechdev.github.io/aied/articles/llm-unlearning-math-privacy/): **Synthesis:** Balancing AI responsibility with privacy, safety, and utility: Unlearning in large language models for mathematics education - [Artificial intelligence, cognitive offloading and implications for education](https://edtechdev.github.io/aied/articles/lodge-loble-cognitive-offloading-2026/): **Synthesis:** Lodge & Loble (2026) provide a comprehensive report on the cognitive science behind AI use in education, arguing that the core risk of generative AI is not plagiarism but cognitive offloading — students outsourcing the mental work required for durable learning. They distinguish beneficial offloading (freeing capacity for higher-order thinking) from detrimental outsourcing (bypassing - [LUDIA: A Design and Evidence Statement](https://edtechdev.github.io/aied/articles/ludia-udl-ai-thought-partner-2026/): **Synthesis:** LUDIA is a no-cost, private, multilingual AI thought partner that connects educators with the Universal Design for Learning (UDL) framework. This statement describes the August 2026 relaunch rebuilt for privacy (no accounts, no cookies, no data collection), accessibility (WCAG 2.2 Level AA), and scale (13 languages, public-good architecture). The tool is positioned as a thought part - [Why Machines Misread Pedagogical Quality: Human-Machine Alignment in LLM-Based Pretest Question Evaluation](https://edtechdev.github.io/aied/articles/machines-misread-pedagogical-quality/): Tseng et al. (2026) investigate human-machine alignment in LLM-based pretest question evaluation — a critical bottleneck for scalable AI-assisted assessment. Their AI-assisted workflow combines automated generation, rubric-based evaluation, and iterative selection. Through a 2×2 experimental design varying rubric operationalization and evaluation mode, they find that human-machine disagreements ar - [MBP-KT: Learning Global Collaborative Information from Meta-Behavioral Pattern for Enhanced Knowledge Tracing](https://edtechdev.github.io/aied/articles/mbp-kt-meta-behavioral-knowledge-tracing/): This paper proposes **MBP-KT**, which transforms raw learner interaction sequences into structured **meta-behavioral patterns** before extracting collaborative signals. Raw sequences contain redundant noise; by decomposing interactions into distinct behavioral patterns (success-streaks, struggle-recovery, hesitation), the model captures higher-order learning dynamics. - [Measuring Whether LLM Tutors Teach or Solve: A Diagnostic for Educational Impact](https://edtechdev.github.io/aied/articles/measuring-llm-tutors-teach-vs-solve/): Studies whether public LLM tutoring benchmarks distinguish learning-supportive behavior from mere answer production. Proposes a lightweight diagnostic based on the gap between solving-oriented and pedagogy-oriented benchmark performance. Using MathTutorBench, shows correlation between solving and pedagogy composites is only r=0.421 across 8 models, with several models shifting rank when evaluated - [MedEasy: Designing AI Standardized Patients for Clinical Consultation Training](https://edtechdev.github.io/aied/articles/medeasy-ai-standardized-patients/): MedEasy multi-agent system simulates standardized patients with varying conditions for medical consultation training; outperforms script-based approaches in realism and adaptability. - [MedGame: Storytelling Gamification Empowered by Large Language Models for Medical Education](https://edtechdev.github.io/aied/articles/medgame-llm-medical-education-gamification/): MedGame transforms static clinical cases into structured, executable storytelling games for medical education, moving beyond the localized question-answering and single-turn feedback that characterize most llm medical-training systems. It uses a dual-engine design: a Medical Narrative Designer synthesizes case-grounded clinical storylines with states and decision nodes, while a Story Director conv - [Memdora: Designing Cognitively-Grounded Flashcard Interactions for AI-Powered Spaced Repetition](https://edtechdev.github.io/aied/articles/memdora-ai-spaced-repetition/): Presents Memdora, a cross-platform AI spaced repetition system that addresses limitations of binary flip-and-rate flashcard interactions. Grounded in cognitive science evidence on retrieval practice, it enables richer interaction patterns and reduces context-switching by generating flashcards from reading material. Demonstrates improved retention compared to traditional SRS tools. - [Metacognitive AI literacy: going beyond the AI skills gap agenda](https://edtechdev.github.io/aied/articles/metacognitive-ai-literacy-beyond-skills-gap-2026/): **Synthesis:** Shapiro, Souto-Otero, and Watermeyer (2026) argue that conventional AI literacy frameworks anchored in functional skills acquisition fail to address the fundamental epistemological challenges posed by probabilistic, opaque algorithmic systems. They reconceptualize AI literacy as a **metacognitive social practice** that transcends individual competencies to encompass collective capac - [Experiential Versus Instructional Approaches for Eliciting Metacognitive Awareness in AI-Assisted Learning](https://edtechdev.github.io/aied/articles/metacognitive-awareness-experiential-vs-instructional/): A quasi-experimental, short-term longitudinal study with 126 first-year engineering students comparing two ways of teaching students how to learn with generative AI: an experiential, hands-on session versus a classical instructional lecture. Metacognitive awareness \u2014 both knowledge of cognition (understanding effective AI-use strategies) and regulation of cognition (applying that knowledge in - [A Taxonomy of Metacognitive Learning Scenarios in Professional Contexts: Integrating Systems Theory with Empirical Constraints](https://edtechdev.github.io/aied/articles/metacognitive-learning-scenarios-taxonomy/): This paper addresses a fundamental gap in metacognition research: the lack of systematic integration of metacognitive theories into scenario taxonomies capable of guiding AI-enhanced professional development. By synthesizing four major theoretical frameworks into a six-node open systems model, the authors create a rigorous taxonomy of metacognitive learning scenarios. - [Metacognitively Discordant Completion and the Aware Pass-Through of Non-Understanding in Generative AI Learning](https://edtechdev.github.io/aied/articles/metacognitively-discordant-completion-genai-2026/): **Synthesis:** This theoretical paper names a state it calls *metacognitively discordant completion* (MDC): a learner submits correct, complete work while holding a first-person awareness that understanding has not actually arrived. Arguing that no existing literature holds the three defining conditions together under one name, the author builds the construct by inheritance from metacognition rese - [Mind the Trust Gap: Identifying (Mis)alignments in Teacher-Student Views Toward Control and Agency in K-12 Classroom AI](https://edtechdev.github.io/aied/articles/mind-the-trust-gap-teacher-student-views-control-agency-k12-classroom-ai/): **Tomohiro Nagashima, Lisa Siegrist, Niklas Scholz, Shintaro Sato, Martina Vincoli, Man Su (2026)** - [MindCopilot: Towards Formalizing and Evaluating Granular Human-LLM Co-Writing](https://edtechdev.github.io/aied/articles/mindcopilot-llm-co-writing/): MindCopilot introduces a formal framework for evaluating human-LLM co-writing that shifts from output-only metrics (BLEU, ROUGE) to **interaction-aware evaluation**. The paper models co-writing as a **Human-in-the-Loop Markov Decision Process (HiL-MDP)**, where writing is a sequence of granular decisions: accept, edit, or reject each AI suggestion. The **Co-Writing Fidelity Suite** introduces two - [Cognitive Offloading in Student–AI Collaboration: A Longitudinal Analysis of Prompting Strategies](https://edtechdev.github.io/aied/articles/misiejuk-cognitive-offloading-prompting-2026/): **Synthesis:** Misiejuk, López-Pernas, Kaliisa, and Saqr (2026) analyze 281 prompts from 122 student submissions across four assignments to examine how prompting strategies reveal cognitive offloading in student–AI collaboration. Using qualitatively coded prompts and Co-Occurrence Network Analysis (CNA), they found that high-quality submissions demonstrated cohesive prompting patterns integrating - [Visualizing Engineering Fundamentals: Design of Mixed Reality and Physical Toolkits for Effective Learning](https://edtechdev.github.io/aied/articles/mixed-reality-engineering-learning/): **Mohammad Abu Nasir Rakib, Sharmin Akter, Eshwara Prasad Sridhar, Somik Biswas, Md Rassel Raihan, Mahmudur Rahman** — submitted 1 Jul 2026 - [Fostering machine learning literacy in senior primary education: Evaluating a structured pedagogical course design](https://edtechdev.github.io/aied/articles/ml-literacy-primary-education/): **Synthesis:** Fostering machine learning literacy in senior primary education: Evaluating a structured pedagogical course design - [Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy](https://edtechdev.github.io/aied/articles/mllm-scientific-visualization-literacy/): Multimodal large language models (MLLMs) are increasingly used to interpret visualizations, yet most evaluations remain chart-centric and offer limited insight into **scientific visualization (SciVis) literacy**. This study benchmarks six MLLMs (three closed-source, three open-source) on a standardized SciVis literacy assessment — 49 items spanning 18 scientific visualizations, 8 techniques, and 1 - [Modularizing Educational LLM-Agency for Fostering Responsible Learning Assistance](https://edtechdev.github.io/aied/articles/modular-educational-llm-agency/): The widespread adoption of AI chatbots in education will drastically change learning, making responsible deployment a critical concern. While large language models (LLMs) might have access to sources discussing insights from educational sciences, they are not particularly inclined to adhere to pedagogical concepts, risking negative effects on the learning process, such as a loss of transfer capabi - [From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents](https://edtechdev.github.io/aied/articles/mooc-to-maic/): **A new paradigm for online education replacing MOOCs with LLM-driven multi-agent AI classrooms**, piloted at Tsinghua University with 100K+ learning records from 500+ students. MAIC uses specialized agents (Teacher, Assistant, Classmate, Analyzer) to deliver personalized, adaptive learning at scale. **ArXiv:** 2409.03512 **Submitted:** September 2024 - [From Surface Learning to Deep Understanding: A Grounded AI Tutoring System for Moodle](https://edtechdev.github.io/aied/articles/moodle-ai-tutoring-deep-learning/): Ostrowska, Kukla & Majstrak (2026) present an AI tutoring system **integrated into the Moodle LMS** designed to scaffold students from surface-level fact recall to deep conceptual understanding through adaptive questioning and feedback. - [Navigating the moral panic: encouraging appropriate use of GenAI in the classroom rather than condemning innovation as disruption](https://edtechdev.github.io/aied/articles/moral-panic-genai-classroom/): **Jennifer M. Krebsbach & Victoria L. Cross (University of California, Davis)** — *Assessment & Evaluation in Higher Education* (Taylor & Francis). Open Access, CC BY 4.0. doi:10.1080/02602938.2026.2686727. - [MotiBo: The Impact of Interactive Digital Storytelling Robots on Student Motivation Through Self-Determination Theory](https://edtechdev.github.io/aied/articles/motibo-digital-storytelling-robots-motivation-2026/): **Synthesis:** Fung and Lui (2026) examine the impact of MotiBo, an interactive digital storytelling system incorporating a human-like robot, on student engagement and creativity. Recognizing that storytelling can enhance motivation and engagement but that conventional methods often lack interactive elements, the study compares engagement across three modalities: paper-based, PowerPoint, and robot - [Multi-Agent Systems for Instructional Design](https://edtechdev.github.io/aied/articles/multi-agent-instructional-design/): Embedding the Knowledge–Learning–Instruction (KLI) framework into multi-agent systems to act as sophisticated instructional designers for K-12 educators. - [Beyond the AI Tutor: Social Learning with LLM Agents](https://edtechdev.github.io/aied/articles/multi-agent-llm-social-learning/): Most AI-based educational tools adopt a one-on-one tutoring paradigm, pairing a single LLM with a single learner. Yet decades of learning science — from Vygotsky's Zone of Proximal Development to Bandura's Social Learning Theory — suggest that multi-party interaction, through peer modeling, co-construction, and exposure to diverse perspectives, produces learning benefits that dyadic tutoring alone - [Design and Implementation of a Real-time Multi-site Immersive Learning System Using Photon Fusion](https://edtechdev.github.io/aied/articles/multi-site-vr-immersive-learning/): This paper develops a VR-based immersive learning environment using Photon Fusion that allows teachers and students to be present in the same virtual space regardless of physical locations. The system enables real-time verbal communication and interaction with 3D learning materials, achieving stable real-time communication and state synchronization across multiple players. Evaluation demonstrates - [A multi-agent AI classroom based on dual-process reasoning hazards: a pilot with prospective physics teachers](https://edtechdev.github.io/aied/articles/multiagent-classroom-dual-process-physics-teachers-2026/): **Synthesis:** Tufino (2026) pilots a simulated multi-agent AI classroom where five AI students each enact distinct dual-process theory (DPT) reasoning hazards, giving prospective physics teachers rare practice in responding to authentic student reasoning. Fifteen graduate students showed significant improvement in diagnostic scores (p=0.014, r=0.79), but during the simulation itself used predomin - [An Interpretable Closed-Loop Intelligent Tutoring System for Multimodal Affective Feedback in Asynchronous Presentation Training](https://edtechdev.github.io/aied/articles/multimodal-affective-its-presentation/): - intelligent-tutoring - rag ## Connected Articles - [LLM-based Multimodal AI Feedback Produces Equivalent Learning and Better Student Perceptions than Educator Feedback](https://edtechdev.github.io/aied/articles/multimodal-ai-feedback-learning/): **AI multimodal feedback matches educator feedback for learning while significantly outperforming it on student perceptions.** - [Multimodal AI Tutoring in STEM](https://edtechdev.github.io/aied/articles/multimodal-ai-tutoring/): When LLMs process STEM problems that require interpreting diagrams, graphs, or schematics alongside text, their accuracy degrades substantially. This effect is: - [Multimodal Item Parameter Estimation using Simulated Response Probabilities](https://edtechdev.github.io/aied/articles/multimodal-item-parameter-estimation-2026/): **Synthesis:** This paper fine-tunes a multimodal large language model (Qwen3.5-based) to reconstruct multiple-choice model (MCM) and three-parameter logistic (3PL) item characteristic curves. By learning to reproduce students' systematic error patterns across a range of ability levels, the LLM implicitly captures underlying response probabilities and can approximate item difficulty on held-out te - [Evidence-Grounded Multimodal Knowledge Graph Construction for Multi-Lecture Educational Reasoning](https://edtechdev.github.io/aied/articles/multimodal-knowledge-graph-educational-reasoning/): **Synthesis:** This paper introduces an evidence-grounded multimodal pipeline that constructs provenance-rich knowledge-tracing from lecture videos by integrating speech transcripts, slide OCR, and vision-language model analysis. Processing three neural-network lectures, the pipeline extracted 172 canonical concepts and 282 typed relationships with 90.38% endpoint coverage, achieving perfect retri - [Multimodal Learning with Generative AI](https://edtechdev.github.io/aied/articles/multimodal-learning-genai/): The guide adopts a middle way between "techno-fixing" and rejecting AI as an existential threat. It argues that: - [Students' multimodal prompting practices as epistemic work in AI literacy development](https://edtechdev.github.io/aied/articles/multimodal-prompting-ai-literacy/): **Synthesis:** Students' multimodal prompting practices as epistemic work in AI literacy development - [Challenges for Musical Education in the Age of AI and Digital Transformation](https://edtechdev.github.io/aied/articles/musical-education-ai-digital-transformation-2026/): **Synthesis:** This paper maps the challenges that generative AI, streaming algorithms, and digital audio workstations pose for music education. Three converging transformations are examined: the changing nature of music creation and consumption, shifts in the public for music shaped by algorithmic curation, and the democratization of music production through digital tools. The paper surveys impli - [Neural-Symbolic Knowledge Tracing](https://edtechdev.github.io/aied/articles/neural-symbolic-knowledge-tracing/): Key limitations exist in both LLM-based tutoring and conventional Deep Knowledge Tracing (DKT): - [I can''t read your mind": A Study of Neurodivergent Computing Students'' Experiences with Collaborative Active Learning](https://edtechdev.github.io/aied/articles/neurodivergent-computing-students/): This study surveyed 24 neurodivergent computing students (autistic and/or ADHD) and 20 neurotypical peers, supplemented by 4 in-depth interviews, to understand how collaborative active learning structures affect comfort and accessibility. Three key findings emerge: **(1)** Neurodivergent students experience significant discomfort with assignments that lack clear structure or have ambiguous expecta - [New systems of learning for distance learning institutions? A six-study review of implementing AIDA](https://edtechdev.github.io/aied/articles/new-systems-of-learning-for-distance-learning-institutions-a-six-study-review-of/): **Synthesis:** Rienties et al. (2026) examine how the Open University (UK) — a large-scale distance learning institution teaching 200K+ learners across 50+ countries — designed, implemented, and evaluated an AI digital assistant (AIDA) using Sharples' embedded systems approach. Through six iterative Design-Based Research (DBR) studies over 18 months involving 498 students and 20 staff, they found - [PersonaVLM: Long-Term Personalization for AI Tutors](https://edtechdev.github.io/aied/articles/nie-personavlm-long-term-personalization-2026/): **PersonaVLM** introduces an agent framework for long-term personalization of multimodal LLMs, enabling AI tutors to remember, reason about, and align with a learner's evolving preferences across hundreds of interaction turns. Tested on 2,000+ curated cases across 200 personas in the Persona-MME benchmark, the framework outperforms GPT-4o by 5.2% in personalization accuracy while operating entirel - [Not a universal benefit: Examining the differential effects of emotional AI on L2 pre-service teachers' language learning](https://edtechdev.github.io/aied/articles/not-a-universal-benefit-examining-the-differential-effects-of-emotional-ai-on-l2/): **Synthesis:** This study challenges the assumption that emotional design in educational AI provides universal benefits, investigating when, for whom and how it impacts L2 vocabulary learning. A quasi-experiment with 147 pre-service teachers found no overall difference in vocabulary acquisition, but a nuanced pattern emerged: the regular agent preserved significantly better learning attitudes and - [Not all collaboration benefits from competition: Collaboration modes in a computational thinking game](https://edtechdev.github.io/aied/articles/not-all-collaboration-benefits-from-competition-collaboration-modes-in-a-computa/): **Synthesis:** This study investigated different collaboration modes and how they interact with competition to influence computational thinking learning, group metacognition and in-game behaviours. In a quasi-experimental 3x2 factorial design with 148 seventh-grade students, results revealed significant main effects of collaboration mode and an interaction effect between collaboration and competit - [NSMQ Riddles: A Benchmark of Scientific and Mathematical Riddles for Quizzing Large Language Models](https://edtechdev.github.io/aied/articles/nsmq-riddles-science-math-benchmark/): Boateng et al. (2026) introduce **NSMQ Riddles**, a benchmark of 1.8K scientific and mathematical riddles drawn from 11 years of Ghana's **National Science and Maths Quiz** — a live TV competition for senior secondary school students. This is one of the first AI benchmarks originating from the **Global South** for educational evaluation. - [NuclearDiffusion: Text-to-Image Foundation Models for Learning Nuclear Energy Concepts](https://edtechdev.github.io/aied/articles/nuclear-diffusion-text-to-image-learning-2026/): **Synthesis:** Systematic study of domain-adapted text-to-image models for nuclear engineering education. Fine-tunes Stable Diffusion on nuclear domain images; fine-tuned model achieves 78% domain accuracy vs 12% for base model. Proposes NuclearDiffusion as an educational tool where instructors generate accurate visualizations of nuclear concepts (reactor components, fuel cycles, safety systems). - [OATutor: An Open-source Adaptive Tutoring System and Curated Content Library for Learning Sciences Research](https://edtechdev.github.io/aied/articles/oatutor-open-source-adaptive-tutor-2023/): OATutor (Open Adaptive Tutor) is the first open-source adaptive tutoring system built on Intelligent Tutoring System (ITS) principles, developed at UC Berkeley's CAHL Lab. It combines an MIT-licensed, fully engineered codebase with a Creative Commons (CC BY) algebra content library, knowledge tracing, A/B testing infrastructure, and LTI support — designed to democratize adaptive learning research - [OECD Digital Education Outlook 2026](https://edtechdev.github.io/aied/articles/oecd-digital-education-outlook-2026/): **OECD flagship report** synthesising empirical evidence and expert insights on generative AI in education. Central finding: general-purpose AI chatbots improve task performance but produce no durable learning gains; purpose-built educational GenAI, co-designed with teachers, is the path to sustained improvement. - [Peer and AI Review + Reflection (PAIRR): A Human-Centered Approach to Formative Assessment](https://edtechdev.github.io/aied/articles/pairr-ai-peer-review-2025/): **Synthesis:** Sperber et al. (2025) present the Peer and AI Review + Reflection (PAIRR) model, a human-centered approach to formative assessment that combines peer review best practices with AI review while emphasizing student agency and reflection. In the largest study of college students' use of AI feedback to date (N = 654 across 10 writing courses and three writing-intensive STEM courses at U - [ParaTutor: LLM Mediated Parent Child Tutoring through Role Separated Scaffolding Interface in Real Time](https://edtechdev.github.io/aied/articles/paratutor-parent-child-tutoring/): **Lan Luo, Anqi Wang, Muzhi Zhou, Junhua Zhu, Jie Cai, Ao Yu, Hui Pan** (2026). arXiv cs.HC - [The Paternalistic Filter: Epistemic Injustice and Differential Refusal in LLM-Mediated History Education for Marginalized Romanian Students](https://edtechdev.github.io/aied/articles/paternalistic-filter-llm-history-education/): A systematic API audit of four LLMs acting as history tutors evaluates 1,800 responses about the 1989 Romanian Revolution, exposing a 'paternalistic filter': models differentially refuse or soften answers for marginalized students, reproducing epistemic injustice. The audit reveals that guardrails and refusals are not uniform but patterned by student identity and topic sensitivity. - [Automated Recommendation of Programming Learning Content Using Pattern-based Knowledge Components](https://edtechdev.github.io/aied/articles/pattern-kc-programming-recommendation/): Introductory programming instruction relies on hands-on practice and short learning activities to support mastery of foundational concepts. Although many such learning resources exist, organizing and linking these items in instructionally meaningful ways is challenging without time-intensive expert curation. This study investigates the use of pattern-based Knowledge Components (KCs) to automatical - [From Prompts to Verified Loops: The PCHL-HE Framework for Generative AI-Assisted Educational and Research Content Creation in Higher Education](https://edtechdev.github.io/aied/articles/pchl-he-framework-genai-content-creation-2026/): **Synthesis:** This conceptual preprint develops the Prompt-Context-Harness-Loop Framework for Higher Education (PCHL-HE), a pedagogically grounded vocabulary that differentiates four increasingly complex configurations of generative-AI interaction — prompt, context, harness, and verified loop — across eight dimensions of control, grounding, orchestration, and oversight. - [Pedagogical Safety in Educational Reinforcement Learning](https://edtechdev.github.io/aied/articles/pedagogical-safety-rl/): As reinforcement learning personalizes instruction in intelligent tutoring systems, there is no formal framework for pedagogical safety — a critical gap. - [The Pedagogy of AI Mistakes: Fostering Higher-Order Thinking](https://edtechdev.github.io/aied/articles/pedagogy-ai-mistakes/): An instructional approach that deliberately leverages AI errors, hallucinations, and limitations as teaching tools to foster higher-order thinking. Rather than viewing AI mistakes as failures to be avoided, this pedagogy treats them as cognitive provocations that demand analysis, evaluation, and reflection from students. Proposed by Hosseini (2026) in a database design course context. - [Using the Pepper Robot to Support Sign Language Communication](https://edtechdev.github.io/aied/articles/pepper-robot-sign-language-lis-2025/): **Synthesis:** Bolla et al. (2025) investigate whether the commercial Pepper social robot can produce intelligible Italian Sign Language (LIS) signs and short signed sentences, addressing the underexplored accessibility of social robots for Deaf users. With the help of a Deaf student and an expert interpreter, they co-designed and implemented 52 LIS signs on Pepper using either manual animation te - [Persistent AI Agents in Academic Research: A Single-Investigator Implementation Case Study](https://edtechdev.github.io/aied/articles/persistent-ai-agents-academic-research/): This is the first empirical study of what happens when AI agents are embedded **persistently** in a real academic research environment — with durable memory, local files, external tools, scheduled routines, delegated roles, and explicit safety protocols. Over 96 active days (January 31 to May 25, 2026), the researcher-agent ecosystem generated 75,671 de-duplicated telemetry records, 23,710 assista - [Students' Epistemological Beliefs and their Chatbot Preferences in AI-mediated Physics Learning](https://edtechdev.github.io/aied/articles/physics-chatbot-epistemological-beliefs-2026/): **Synthesis:** Sirnoorkar & Mamidpalliwar (2026) investigate the association between introductory physics students' preferences for chatbot behavior and their epistemological beliefs, using a custom online waves module with simulations integrated with a chatbot. Preferences were captured through three options (guided-inquiry, direct answer, and a combination); beliefs via the EBAPS survey. Student - [Leveraging Physiological Signals to Predict Exam Outcomes with Machine Learning](https://edtechdev.github.io/aied/articles/physiological-signals-exam-outcomes-ml/): Investigates ML models to predict exam outcomes from physiological data (electrodermal activity, heart rate, skin temperature) collected during exams. Evaluates logistic regression, random forest, SVM, transformers, LSTM, and GRU. Random forest often outperformed deep learning models while offering interpretability. Highlights value of physiological data for understanding student stress and real-t - [Polished Artifacts, Fragile Engagement? Tackling the Challenge of Reduced Epistemic Effort in Human-AI Knowledge Construction](https://edtechdev.github.io/aied/articles/polished-artifacts-fragile-engagement-2026/): **Synthesis:** Drawing on CSCL research traditions, this paper conceptualizes the risk of reduced epistemic effort when learners use generative AI to produce knowledge artifacts. It identifies two strands of risk: a social-cognitive strand grounded in automation bias (attributing greater epistemic competence to AI) and an artifact-oriented strand focused on polished external artifacts inducing epi - [The (im)possibility of AI literacy](https://edtechdev.github.io/aied/articles/possibility-ai-literacy-critical-editorial/): **Synthesis:** Pangrazio (2026) offers a critical editorial questioning whether AI literacy is a meaningful or even achievable goal. Tracing the history of literacy from its elite origins through mass institutionalization, she argues that AI literacy has been positioned as a "cure-all" — a solutionist, normative response to the complex and evolving phenomenon of AI. The analysis asks what "text" A - [Learning after COVID-19 and the ICT career aspirations: Are students entering the AI era with weaker skills?](https://edtechdev.github.io/aied/articles/post-covid-ict-career-aspirations/): **Post-COVID ICT Career Aspirations** uses PISA 2018 and 2022 country-level data to investigate whether students entering the generative AI era have adequate educational foundations. Using a mixed-methods approach including Variational Autoencoders for latent representation learning, the study finds that ICT career aspirations have increased globally but unevenly. Digital skills are the strongest - [When AI Does the Work, What Is Learning For? Post-Instrumental Learning and the Risk of Capacity Dissolution](https://edtechdev.github.io/aied/articles/post-instrumental-learning-capacity-dissolution/): Argues that as AI systems become capable of producing the artifacts through which institutions recognize competence, existing ethical frameworks centered on AI failures become insufficient. Develops the concept of "post-instrumental learning" and warns that each technical improvement appears to weaken the case for human learning itself, risking "capacity dissolution." - [A Posthumanist Approach to AI Literacy](https://edtechdev.github.io/aied/articles/posthumanist-ai-literacy-2025/): **Synthesis:** Wang and Wang (2025) argue for a posthumanist reframing of AI literacy, moving beyond the humanistic view of AI as a discrete "tool" used by autonomous human agents toward understanding AI literacy as an understanding of how meaning emerges through the entanglement of human and AI agencies. Through a case study of two multilingual undergraduate students (Zhimo and Asuka) in US writi - [From Precision Medicine to Precision Education: A Vision for AI-Powered Student Digital Twins, Preventive Student Success, and Career-Aligned Academic Pathways](https://edtechdev.github.io/aied/articles/precision-education-student-digital-twins-2026/): **Synthesis:** This paper proposes a precision education framework that adapts precision medicine's predictive, preventive approach to higher education. It envisions AI-powered student digital twins — computational models that integrate academic, behavioural, and career trajectory data to forecast risk, personalise interventions, and align course pathways with employment outcomes. The paper argues - [Principled AI in Education](https://edtechdev.github.io/aied/articles/principled-ai-education/): The framework rests on three interconnected anchors that must be addressed *before* selecting tools: - [Robust and Efficient Motion Reasoning for Privacy-Aware Classroom Incident Recognition](https://edtechdev.github.io/aied/articles/privacy-aware-classroom-incident-recognition-2026/): **Synthesis:** Pilot study on privacy-aware computer vision for classroom incident detection. Introduces a hybrid benchmark combining generative CCTV-style videos with real classroom pose data. Proposes a lightweight motion reasoning model that achieves strong incident recognition while preserving student privacy (no facial recognition). Demonstrates that efficient motion-based features can genera - [Prober.ai: Gated Inquiry-Based Feedback via LLM-Constrained Personas for Argumentative Writing](https://edtechdev.github.io/aied/articles/prober-ai-inquiry-writing/): A web-based writing environment that inverts the AI-tutoring paradigm: rather than generating improved text for students, Prober.ai constrains an LLM to ask only targeted inquiry-based questions about argumentative weaknesses. Students must reflect before receiving revision suggestions. Developed by Bi et al. (2026), awarded second place at NY EdTech Hackathon. - [Programming Intelligent Tutoring Systems](https://edtechdev.github.io/aied/articles/programming-its/): **SCRIPT** (Deriyeva, Dannath, Paassen, 2026) implements an intelligent tutoring system for **Python programming** in a German university context, filling a gap in prior ITS which rarely supported Python. - [Teaching Prompt-Based Programming with LLMs: A 45-Minute Lesson with Guided Practice for End-User Programmers](https://edtechdev.github.io/aied/articles/prompt-based-programming-lesson/): This study by Tran, Marwan & Price (2026) introduces and evaluates a 45-minute structured lesson on prompt-based programming, a new modality enabled by LLMs where users express computational goals through natural language. The lesson design incorporates guided practice principles and targets end-user programmers with limited formal training. Results show significant pre-to-post gains in prompt qua - [Prompt Coach: An Empirical Evaluation of an Agentic Tutor for Learning Prompt Engineering in Software Development](https://edtechdev.github.io/aied/articles/prompt-coach-agentic-tutor-prompt-engineering/): Prompt engineering is a critical yet undertaught skill for software developers, poorly served by traditional instruction because of its evolving, interactive, context-dependent nature. The authors introduce **Prompt Coach (PC)**, an agentic tutor embedded in-flow within a developer's IDE that teaches prompt crafting through Socratic guidance. PC scores prompt quality across multiple dimensions and - [Evaluating Prompt Injection Defenses for Educational LLM Tutors: Security-Usability-Latency Trade-offs](https://edtechdev.github.io/aied/articles/prompt-injection-defenses-educational-llm-tutors/): Evaluating Prompt Injection Defenses for Educational LLM Tutors: Security-Usability-Latency Trade-offs **Maiorano (2026)** — arXiv cs.CR/cs.AI. - [Understanding Student Perceptions, Mistakes, and Debugging Approaches when Solving Natural Language Programming Tasks](https://edtechdev.github.io/aied/articles/prompt-problems-nl-programming-mistakes/): Learning to communicate with code-generating AI is an emerging skill for novice programmers. 'Prompt Problems' — having students solve computational tasks by writing natural-language prompts for code-generating models — is a recent pedagogical approach, yet little was known about the specific prompt-level mistakes novices make, the computational details they fail to communicate, and how they recov - [ProPRL: Property-Aware Prerequisite Relation Learning in Educational Knowledge Graphs](https://edtechdev.github.io/aied/articles/proprl-prerequisite-relation-learning/): **Synthesis:** ProPRL advances adaptive-learning by going beyond conventional link prediction to adaptively integrate complementary educational evidence from concept-resource hypergraphs and directed learning-behavior graphs. The Irreversibility Constraint — an anti-symmetry regularizer that penalizes contradictory bidirectional predictions — addresses a fundamental issue in knowledge-tracing: the - [PsyScore: A Psychometrically-Aware Framework for Trait-Adaptive Essay Scoring and ZPD-Scaffolded Feedback](https://edtechdev.github.io/aied/articles/psyscore-essay-scoring-zpd-feedback/): **Wei Xia, Jin Wu, Haoran Shi, Xiangyu Wang, Chanjin Zheng** (2026). East China Normal University / arXiv cs.CL preprint - [Q-Learning Lab: Teaching Reinforcement Learning Through Learner-Generated Trace Analysis](https://edtechdev.github.io/aied/articles/q-learning-lab-rl-teaching/): Presents Q-Learning Lab, a single-file tool that makes the Bellman update concrete by letting undergraduates inspect how each value is computed and why actions are chosen, through learner-generated trace analysis. It addresses the abstraction gap where students watch policy convergence without understanding mechanism. - [Quantum Education Intelligent Tutoring](https://edtechdev.github.io/aied/articles/quantum-education-its/): **From Prototype to Classroom** (Elhaimeur & Chrisochoides, 2026) describes a tutoring system for quantum computing that bridges the gap between dense mathematical formalism and limited qualified instructors. - [Effects of an AI-supported inquiry model on AI literacy and authentic performance: A quasi-experimental study with preservice teachers](https://edtechdev.github.io/aied/articles/quest-ai-inquiry-preservice-teachers/): **Synthesis:** Effects of an AI-supported inquiry model on AI literacy and authentic performance: A quasi-experimental study with preservice teachers - [Rethinking Generative AI Literacy: An Integrative, Developmental, and Dialectical Framework for K-12 Teacher Education](https://edtechdev.github.io/aied/articles/rail-ed-genai-literacy-teacher-education/): **RAIL-Ed is an integrative, developmental, and dialectical framework for generative AI literacy in K-12 teacher education, built from a systematic review of 67 studies and specifying six interdependent pillars with a three-level maturity rubric.** - [Review of Artificial Intelligence in Education from 2020 to 2025](https://edtechdev.github.io/aied/articles/raza-farooq-aied-review-2020-2025/): **Synthesis:** Raza & Farooq (2025) conduct a comprehensive content analysis of AI in education from 2020-2025, examining 100+ peer-reviewed articles through a three-layer framework: the genome layer (infrastructure, algorithms), the cognitive layer (predictive analytics, multimodal sensing, discourse analysis), and the symbiotic layer (learning platforms, smart classrooms, GenAI copilots). Three - [Regulating the AI Tutor: SRL and Help-Seeking in Adolescent GenAI Use](https://edtechdev.github.io/aied/articles/regulating-ai-tutor-adolescent-srl/): Examines how 98 Grade-9 students across three German Gymnasium schools regulated their use of a Mistral-Large GenAI tutor while preparing for a math exam. Despite overwhelmingly selecting scaffolded support before the session, students' actual interactions were dominated by instrumental requests (asking for answers) with almost no explicit monitoring or evaluation of their own learning. - [Reimagining feedback through generative AI in engineering education](https://edtechdev.github.io/aied/articles/reimagining-feedback-through-generative-ai-in-engineering-education/): **Synthesis:** Pecuchova, Benko, and Drlik (2026) investigate the capacity of a large language model (GPTo1) to generate formative feedback for student-created UML diagrams in a university software engineering course. Across two cohorts (N = 262), AI-generated, teacher-generated, and no-feedback conditions were compared, analyzing student perceptions, learning outcomes, and grading reliability. Re - [Reinforcement Learning Measurement Model](https://edtechdev.github.io/aied/articles/reinforcement-learning-measurement-model-assessment/): Interactive assessments generate sequential process data that conventional item response models (IRT) cannot adequately handle. This paper proposes a **reinforcement learning measurement model** that links action choices to state-action values, extending beyond existing MDP-based measurement approaches. - [Ensuring Reliability in Programming Knowledge Tracing: A Re-evaluation of Attention-augmented Models and Experimental Protocols](https://edtechdev.github.io/aied/articles/reliable-programming-kt/): - Programming Knowledge Tracing (PKT) has advanced through hybrid attention+RNN architectures, which have become the dominant modeling approach in the subfield. - The re-evaluation identifies critical protocol issues in prior work: attention dimension misconfiguration and temporal causality violations. - Under controlled experiments, the advantage of attention-enhanced models over standard DKT is - [Play-Testing REMind: Evaluating an Educational Robot-Mediated Role-Play Game](https://edtechdev.github.io/aied/articles/remind-robot-mediated-roleplay-antibullying-2026/): **Synthesis:** Sanoubari, Fernandes, Rebello, Pan, Houston, and Dautenhahn (2026) present REMind, an educational robot-mediated role-play game designed to support anti-bullying bystander intervention among children. REMind invites players to observe a bullying scenario enacted by social robots, reflect on the perspectives of the characters, and rehearse defending strategies by puppeteering a robot - [Student Evaluation of Repeated AI Feedback Across a Semester of Writing](https://edtechdev.github.io/aied/articles/repeated-ai-writing-feedback-semester/): This short paper provides rare descriptive classroom evidence on what happens when students repeatedly use generative-AI feedback across a full semester of writing coursework. Drawing on 2,988 reflective essay-feedback-appraisal instances from 283 Estonian bachelor students, the authors find that students rated AI feedback as helpful and actionable more often than not, but a growing minority (abou - [Representation Robustness under Executable Reasoning Constraints in Large Language Models for Mathematical Problem Solving](https://edtechdev.github.io/aied/articles/representation-robustness-llm-math-problem-solving/): This study probes how sensitive llm mathematical problem solving is to the surface representation of an item — a question with direct bearing on assessment-validity when LLMs are used for scoring or tutoring in stem-education. Systematically varying representationally equivalent formulations (story problems, word-equations, symbolic equations, and isomorphic paraphrases) across 5 contemporary LLMs - [Reshaping Undergraduate Computer Science Education in the Generative AI Era](https://edtechdev.github.io/aied/articles/reshaping-cs-education-genai/): **Yi-Chieh Lee, Nattapat Boonprakong, Yugin Tan, Harold Soh et al.** — Workshop report from NUS-Google Workshops — cs.CY - [ResidencyRL: Reinforcement Learning in Simulated Clinical Environments](https://edtechdev.github.io/aied/articles/residencyrl-clinical-rl-training-2026/): **Synthesis:** Liévin et al. (2026) present **ResidencyRL**, a reinforcement learning method for training clinical AI agents through simulated multi-turn clinical encounters (up to 60 dialogue turns and 8 tool calls per trajectory). It pairs the policy agent with LLM simulators capable of complex, adversarial behaviors, training against a structured reward aligned to diagnostic accuracy, managemen - [Responsible Assessment in the AI Era: Key Insights from a Future-Focused Conference](https://edtechdev.github.io/aied/articles/responsible-assessment-ai-era-stanford-2026/): **Responsible assessment in the AI era** — assessment grounded in learners' sociocultural contexts and designed to generate valid, trustworthy, context-specific inferences from accumulated evidence, not one-shot outputs. This Stanford Accelerator for Learning + ETS white paper (McGee, Thille, Choi, Ercikan & Hau, 2026, distilled from a January 2026 convening of ~100 education leaders) argues gener - [Rethinking Scaffolding in LLM Tutors: The Interactional Mismatch Between Benchmarks and Real-World Deployments](https://edtechdev.github.io/aied/articles/rethinking-scaffolding-llm-tutors/): **Alexandra Neagu, Jeffrey T. H. Wong, Marcus Messer, Rhodri Nelson, Peter B. Johnson** (2026). Pluralistic Alignment Workshop @ ICML 2026 - [Retrieval-Augmented Tutoring for Algorithm Tracing and Problem-Solving in AI Education](https://edtechdev.github.io/aied/articles/retrieval-augmented-tutoring-algorithm-kite/): KITE (Knowledge-Informed Tutoring Engine) introduces a intelligent-tutoring architecture that grounds its responses in course materials through a multimodal scaffolding. Unlike generic LLM tutors that may drift from curriculum content, KITE retrieves relevant material — lecture slides, problem sets, code examples — before generating Socratic hints, guiding questions, and progressive scaffolds tail - [RoboBlockly Studio: Conversational Block Programming With Embodied Robot Feedback for Computational Thinking](https://edtechdev.github.io/aied/articles/roboblockly-conversational-block-robotics-ct-2026/): **Synthesis:** Li, Du, Sun, and colleagues (2026) design and evaluate RoboBlockly Studio, an integrated interactive system that combines block-based programming, a conversational AI teaching agent, and embodied robot execution to support computational thinking. Recognizing that learners and teachers face challenges connecting abstract program logic to meaningful outcomes, the system creates a tigh - [RoboBuddy in the Classroom: Exploring LLM-Powered Social Robots for Storytelling in Learning and Integration Activities](https://edtechdev.github.io/aied/articles/robobuddy-llm-social-robots-classroom-2025/): **Synthesis:** Tozadore, Ertug, Chaker, and Abderrahim (2025) present RoboBuddy, an intuitive interface that lets teachers create scenario-based storytelling activities from their regular curriculum using LLMs and social robots. The system addresses two practical classroom challenges: the significant planning time required to create improvised scenarios for content delivery (intensified when using - [REC-CBM: Rubric-Aware Error-Correction Concept Bottleneck Models for Trustworthy Open-Ended Grading](https://edtechdev.github.io/aied/articles/rubric-aware-grading-rec-cbm/): **REC-CBM: Rubric-Aware Error-Correction Concept Bottleneck Models** advances the automated-grading frontier by solving a fundamental trust problem: even accurate AI graders are unusable if educators cannot verify their reasoning. Standard llm-based graders operate as black boxes, while earlier Concept Bottleneck Models (CBMs) offer interpretability but fail at modeling rubric dimensions, ordinal - [Same AI, different pathways: Unpacking mechanisms of AI-mediated learning across discipline-institution contexts](https://edtechdev.github.io/aied/articles/same-ai-different-pathways/): **Synthesis:** Same AI, different pathways: Unpacking mechanisms of AI-mediated learning across discipline-institution contexts - [Faculty Readiness for AI-Supported Teaching and Scalable Online Program Delivery in Higher Education: The EPIQ-AI Framework for Epistemic Integrity](https://edtechdev.github.io/aied/articles/sangwa-epiq-ai-faculty-readiness-2026/): **Synthesis:** Sangwa, Ndahayo & Dusengumuremyi (2026) develop the EPIQ-AI Readiness Framework synthesizing data from 2020-2025 to explain how institutions can align faculty capacity, governance, and quality assurance for AI-supported teaching. Key finding: faculty readiness is a sociotechnical alignment problem, not an individual skills deficit. - [SAVVY: Student Attention Visualization for Video-based Learning Analysis](https://edtechdev.github.io/aied/articles/savvy-student-attention-video-learning/): **Shixian Zhou, Minghuan Shen, Xiaolin Wen, Zijun Qiu, Yongliang Jiang, Xiangyang Wu, Fei Wu, Yong Wang, Zhiguang Zhou** — arXiv preprint (2026). ## Synthesis - [Scaffolding Critical Engagement with GenAI: Transforming Ethnic Minority Preparatory Students' Collaborative Discourse in Prompt Engineering Tasks](https://edtechdev.github.io/aied/articles/scaffolding-critical-engagement-genai-minority-students/): **Deliang Wang, Cunling Bian** — AIED 2026 (accepted full paper). - [Designing a mobile chatbot-based learning journaling system for intrinsic motivation and engagement](https://edtechdev.github.io/aied/articles/scheu-mobile-chatbot-journaling-motivation-2026/): A **randomized 2×2 full-factorial field experiment** (N = 179 German university students, 22 days of app use, 12-week follow-up) testing two design principles for a **mobile chatbot-based learning journaling system** aimed at keeping students motivated to maintain reflective learning journals — a known pain point (rapid decline in motivation/engagement after brief use). The two principles: (1) an - [School network reorganization under educational and spatial constraints using classical and quantum optimization](https://edtechdev.github.io/aied/articles/school-network-reorganization-optimization/): **Synthesis:** This paper develops an optimization framework for school network reorganization that integrates geographic, administrative, and educational criteria into an Integer Linear Programming formulation. Applied to the complete public school network of Calabria, Italy, and extended to a hybrid quantum optimization setting, the approach identifies optimal school aggregation plans under diff - [Exploring interfaces and implications for integrating social-emotional competencies into AI literacy for education: a narrative review](https://edtechdev.github.io/aied/articles/sec-ai-literacy-narrative-review-2026/): **Synthesis:** Palmquist, Sigurdardottir, and Myhre (2025) conduct a narrative literature review examining the intersection of AI literacy and social-emotional competencies (SEC) in education, proposing an integrated framework to create a supportive, technologically adept, and emotionally intelligent educational ecosystem. Grounded in the SETCOM project, the review identifies three key themes — AI - [Self-Efficacy and Favorability Shape Learning from Tutoring Systems and Paper Practice](https://edtechdev.github.io/aied/articles/self-efficacy-tutoring-learning/): **Xinfei Cen, Vincent Aleven, Kenneth R. Koedinger, Conrad Borchers, Paulo F. Carvalho** (2026). EC-TEL 2026 - [Towards Self-Referential Analytic Assessment: A Profile-Based Approach to L2 Writing Evaluation with LLMs](https://edtechdev.github.io/aied/articles/self-referential-l2-writing-llm-assessment/): Bannò, Knill & Gales (2026) propose a paradigm shift in automated essay scoring: from **inter-learner ranking** to **intra-learner profiling**. Instead of asking "how does this essay rank against others?", their self-referential framework asks "what are this specific learner's strengths and weaknesses?" - [Assessing the Impact and Underlying Pathways of Sequenced AI Feedback on Student Learning](https://edtechdev.github.io/aied/articles/sequenced-ai-feedback-learning/): **Sequenced AI feedback harms learning despite boosting engagement and positive perceptions.** - [Stuck in a Spiral": Shame and Guilt as Social Regulators of AI Use in Computing Education](https://edtechdev.github.io/aied/articles/shame-guilt-ai-regulation-computing-education/): An interview study with 19 computing students through a functionalist perspective of shame and guilt. Findings show these emotions regulate when and how students make their AI use visible, engaging in hiding behaviors and selective disclosure. Students described shaming themselves, peers, and faculty for using AI. Shame and guilt coexist with continued AI use, creating cycles of reduced agency and - [Quality-Conditioned Agreement in Automated Short Answer Scoring: Mid-Range Degradation and the Impact of Task-Specific Adaptation](https://edtechdev.github.io/aied/articles/short-answer-scoring-quality-degradation/): Schleifer, Ariely & Klebanov (2026) investigate a critical gap in automated-grading: **how scoring quality degrades for mid-range student responses**. Most ASAS evaluations focus on clearly correct or incorrect answers, but real classrooms are dominated by partially correct responses where scoring is most challenging. - [Simulating Learners' Task-Selection Strategies and System Constraints in Mastery Learning](https://edtechdev.github.io/aied/articles/simulating-learner-task-selection/): Intelligent Tutoring Systems often grant learners shared control over skill and problem selection. We propose a simulation-based framework to examine how learner task-selection strategies and system constraints shape mastery learning efficiency. - [Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agents](https://edtechdev.github.io/aied/articles/simulating-students-diverse-cognitive-levels-2025/): Wu et al. (2025, ACL) tackle the core challenge of simulating-students: LLMs trained as "helpful assistants" produce overly perfect answers and fail to model the natural imperfections and varied cognitive levels of real learners. They propose a training-free framework that builds a cognitive prototype of each student from a knowledge graph, predicts performance on new tasks, and iteratively refine - [Simulating Students' Java Programming Errors with Large Language Models](https://edtechdev.github.io/aied/articles/simulating-students-java-programming-errors-llms/): This paper investigates whether llm can serve as scalable proxies for students by simulating realistic logical errors in code submissions. Using the CodeWorkout dataset of 74,000+ unique student Java submissions across 37 problems, the authors evaluate five LLMs under three prompting strategies: Input-Output (IO), Chain-of-Thought (CoT), and iterative Self-Refine. - [Simulating Students with Large Language Models: A Review of Architecture, Mechanisms, and Role Modelling in Education with Generative AI](https://edtechdev.github.io/aied/articles/simulating-students-llm-review-2026/): Marquez-Carpintero, Lopez-Sellers & Cazorla (2025) present a thematic review of empirical and methodological studies using LLMs to simulating-students in education. They synthesize evidence on how LLM-based agents emulate learner archetypes, respond to instructional inputs, and interact in multi-agent classroom scenarios, and examine implications for curriculum development, instructional evaluatio - [EduQwen: Pedagogical RL](https://edtechdev.github.io/aied/articles/singh-eduqwen-pedagogical-rl-2026/): **EduQwen: Pedagogical RL** — A multi-stage optimization strategy combining reinforcement learning (DAPO) and supervised fine-tuning (SFT) to enhance the pedagogical knowledge of open-source LLMs, producing a family of dense 32B-parameter models that achieve state-of-the-art performance on the Cross-Domain Pedagogical Knowledge (CDPK) Benchmark, surpassing even much larger proprietary systems such - [Estimating Learners' Skill Acquisition Without Temporal Information](https://edtechdev.github.io/aied/articles/skill-acquisition-without-temporal-info/): Nagai et al. (2026) tackle the practical problem that many real-world educational datasets contain only single-time-point assessments (snapshots) without temporal information, making standard time-series knowledge tracing approaches inapplicable. They propose a novel framework that uses inclusion relations among learners' skill sets — interpreting expanding skill sets as a proxy for learning progr - [Navigating the skill diversity frontier: How skill complexity explains worker resilience](https://edtechdev.github.io/aied/articles/skill-diversity-worker-resilience/): **Synthesis:** Using LinkedIn data on 2.4 million U.S. workers and 16,753 distinct skills, this paper introduces three complementary measures of skill complexity — specialization, diversity, and the diversity frontier — and demonstrates that workers near the frontier are significantly more likely to acquire new skills, receive promotions, and transition into occupations with lower automation expos - [Slide Deck Q&A Quality Assurance App: A Multi-Stage Pipeline for Pedagogical Question Generation](https://edtechdev.github.io/aied/articles/slidesqaqa-pedagogical-question-generation/): SlidesQAQA is a Flask-based system that extracts text and rendered images from PDF lecture slides and processes them through a four-stage llm pipeline: **window planning** (segment extraction), **deck synthesis** (cross-slide reasoning), **slide annotation** (per-slide question generation), and **reconciliation** (deck-level revision to reduce redundancy and improve coverage). The key innovation i - [Co-Creating Buildable and Open Social Robot Study Companions with University Students](https://edtechdev.github.io/aied/articles/social-robot-study-companions/): **Farnaz Baksh, Matevz B. Zorec, Feiazie Baksh, Karl Kruusamae** (2026). ICSR + ART 2026, London - [Socially fluent AI decouples conversational signals from source identity in online interaction](https://edtechdev.github.io/aied/articles/socially-fluent-ai-identity-detection/): This study embedded undisclosed AI agents as teammates in synchronous text-based group interactions across analytical, creative, and ethical tasks with 786 participants making 1,572 identity judgments. The central finding is striking: **humans cannot distinguish AI from human teammates above chance levels**. This failure is not due to a lack of identity-relevant information — computational models - [A Bottom-Up Taxonomy of Student Discourse with a Socratic AI Physics Tutor](https://edtechdev.github.io/aied/articles/socratic-ai-physics-tutor-taxonomy-2026/): **Synthesis:** Large language model (LLM) tutors are being deployed in introductory physics courses at a scale that produces transcript corpora far larger than traditional qualitative coding can absorb. This study builds a bottom-up taxonomy of student discourse from a Socratic AI physics tutor deployed in introductory calculus-based mechanics. Each student turn was assigned an emergent label by a - [The Theoretical Foundation of Socratic Tests: Dynamic, Multimodal, Conversational Examinations](https://edtechdev.github.io/aied/articles/socratic-tests-conversational-assessment/): **Ilya Mikhelson** — Submitted to Computers and Education: Artificial Intelligence (2026). ## Synthesis - [Towards SocratiCode: Designing a Generative AI-Based Programming Tutor for K-12 Students through a 4-Week Participatory Design Study](https://edtechdev.github.io/aied/articles/socraticode-k12-programming-tutor/): - cognitive-load-theory - metacognition - self-regulated-learning - human-in-the-loop-ai ## Connected Articles - [Is Solving Better Than Evaluating GenAI Solutions?](https://edtechdev.github.io/aied/articles/solving-vs-evaluating-genai-solutions/): Randomized A/B crossover study (N=220) in a junior-level algorithms course comparing solution evaluation/critique tasks against traditional solution generation. Finds that evaluation-centered tasks produce comparable learning outcomes with a localized homework advantage that does not transfer to summative performance, suggesting evaluation tasks are a viable pedagogical response to the availabilit - [Special-R1: Reinforcement Learning for Special Education — Aligning LLM Tutors to Diverse Learners through Disability-Adaptive Training](https://edtechdev.github.io/aied/articles/special-r1-rl-special-education/): **Authors:** Unggi Lee, Jihoi Na, Yeil Jeong, Haeun Park, Yeonju Jang (2026) - [Exploring AI-Supported Disciplinary Mediation in Student Project Teams' Text-Based Communication](https://edtechdev.github.io/aied/articles/spritz-ai-disciplinary-mediation-student-teams-2026/): **Synthesis:** Cheng, Chung, Chiu, Lin & Liao (2026) present Spritz, a Discord-based llm technology probe that mediates disciplinary boundaries in interdisciplinary student project teams, finding that students valued AI as both cognitive support for boundary crossing and a relational buffer — while a central tension emerged when AI moved from neutral mediator to advisor or challenger. - [StanBKT: Rethinking Parameter Estimation in Bayesian Knowledge Tracing](https://edtechdev.github.io/aied/articles/stanbkt-bayesian-knowledge-tracing/): StanBKT introduces an open-source Python package for Bayesian Knowledge Tracing (BKT) that moves beyond traditional expectation-maximization (EM) point estimates to full Bayesian inference via Stan. The package supports **four estimation methods** (Hamiltonian Monte Carlo, variational inference, Pathfinder, and optimization), **three model variants** (standard, grouped, and hierarchical BKT), and - [Stanford Evidence Base: AI in K-12 Education](https://edtechdev.github.io/aied/articles/stanford-evidence-base-ai-k12-2026/): **Stanford Evidence Base: AI in K-12 Education** — A 2026 systematic review from the Stanford SCALE Initiative analyzing 818 papers on AI in K-12 education. The central finding is stark: only 20 studies provide strong causal evidence, and zero high-quality causal studies examine U.S. K-12 student settings. The evidence that exists reveals a consistent pattern — AI improves performance during use b - [How State Policy Can Help Teachers Use AI Well](https://edtechdev.github.io/aied/articles/state-policy-teacher-ai/): A NASBE/CRPE policy analysis (May 2026) examining how US states can shape conditions for effective teacher AI adoption — setting guardrails, providing resources, and building capacity without micromanaging implementation. - [Advancing diagram-based reasoning in AI tutoring systems: a structural approach for STEM education](https://edtechdev.github.io/aied/articles/structrag-diagram-reasoning-ai-tutoring/): Presents **StructRAG**, a pattern-aware framework that improves how AI tutoring systems interpret **complex engineering diagrams** (circuit schematics, network topologies, block flowcharts) in STEM. Current AI systems fail on diagrammatic questions because they cannot reliably extract spatial relationships and structural connectivity from noisy real-world diagrams (curved lines, overlapping elemen - [Structural Silence: When AI Infrastructure Fails Speakers of Underrepresented Languages](https://edtechdev.github.io/aied/articles/structural-silence-underrepresented-language-ai-2026/): **Synthesis:** Roy & Roy (2026) argue that the **infrastructure of AI** — training corpora, tokenization, benchmarks, deployment architectures — systematically disadvantages speakers of underrepresented languages *before a model is trained*, reframing dataset scarcity as a structural barrier rather than an isolated technical limitation. Using Bengali as a case in AI-assisted education, they docume - [Structured AI Demonstrations and Student LLM Use in Engineering Mechanics: Study Design and Preliminary Results](https://edtechdev.github.io/aied/articles/structured-ai-demonstrations-engineering-mechanics/): **Shuang Geng, Helen Lallos-Harrell, Jiya Ashar, Thomas J. McKenna, Annwesa Dasgupta, Caleb Farny, Emma Lejeune** — arXiv preprint (2026). ## Synthesis - [The Effects of Structured LLM-Generated Feedback on Programming Assignment Performance](https://edtechdev.github.io/aied/articles/structured-llm-feedback-programming/): - socratic-method - desirable-difficulties ## Connected Articles - [Make or Take: How Students Navigate Self-Created and Instructor-Provided Cheat Sheets](https://edtechdev.github.io/aied/articles/student-cheat-sheets-make-or-take/): Chen, Sakhnini and Istead run a three-wave longitudinal study in a senior software-requirements course where students could use instructor-provided or self-created cheat sheets in exams. Choices were shaped by trust in instructor expertise, desire for personalization, and preparation efficiency, and shifted over time. The make-vs-take decision is fundamentally a metacognition and self-regulated-le - [Archetypes or ability? Clustering for modelling student mathematical competence](https://edtechdev.github.io/aied/articles/student-math-competence-clustering/): On 119,034 students across 13 UK national exams, Bernoulli Mixture Models found few distinct skill clusters — overall ability dominates. A simple explainable model achieved 78% accuracy, competitive with complex approaches. Small personalization gains are possible by accounting for individual question-level strengths, but students don't develop strongly divergent ability profiles across topics. - [Uncovering Students' Mental Models of Generative Artificial Intelligence](https://edtechdev.github.io/aied/articles/student-mental-models-genai/): This study investigates how students conceptualize generative AI (GenAI) and how those mental models shape their academic integration. A student's mental model of GenAI — their beliefs about what it can and cannot do — influences both perceived capability and choices about when to delegate tasks. The authors surface the range of student conceptions, from tool-as-calculator to collaborator, and sho - [How Students (Mis)understand Conditionals and Loops -- A Taxonomy](https://edtechdev.github.io/aied/articles/student-misconceptions-conditionals-loops-taxonomy/): This paper presents a fine-grained taxonomy categorizing novice programmers' difficulties with reading and understanding control flow constructs — specifically conditionals (selection) and loops (iteration). Developed through the Extended Taxonomy Design Process (ETDP), the taxonomy integrates prior research with new empirical data from student quizzes and interviews. It provides a harmonized fram - [Students' Perception Accuracy of Partners' AI Use and its Relation to Collaboration Performance](https://edtechdev.github.io/aied/articles/student-perception-ai-use-collaboration/): Graf et al. (2026) identify a new challenge in collaborative programming education: AI use is now an invisible yet consequential dimension of collaboration, and partners often misread ability and effort from code. In a three-wave longitudinal study of 103 student pairs in an introductory software engineering course, they found that greater misalignment between partners' beliefs about each other's - [It''s OK Because...": The Wild West of Student Rationalization of AI Use in Academic Writing](https://edtechdev.github.io/aied/articles/student-rationalization-ai-writing/): Generative AI challenges academic integrity not only by enabling students to delegate substantial portions of their academic work, but also by blurring the ethical boundaries by which students distinguish acceptable assistance from misconduct. Through semi-structured interviews (n=20), analysis of AI chat logs, and course documents, the researchers identified at least five distinct conceptual site - [Knowing the Rules Is Not Enough: Student Regulatory Awareness and Use of GenAI in Higher Education](https://edtechdev.github.io/aied/articles/student-regulatory-awareness-genai/): Bischof et al. investigate how students' awareness of generative-ai regulations relates to their perceived compliance and actual usage behavior in higher-ed. While previous research mainly examines adoption rates and attitudes, students' awareness of institutional regulations and their perceived compliance have remained unexplored — an important gap as institutions create and apply AI policies. - [Students' engagement with generative AI in academic learning: A self-determination theory and epistemic network analysis study](https://edtechdev.github.io/aied/articles/students-engagement-with-generative-ai-in-academic-learning-a-self-determination/): **Synthesis:** Isaeva et al. (2026) examine undergraduate students' engagement with generative AI (GenAI) in academic learning at an English-medium university, using self-determination theory (SDT) as the interpretive framework and epistemic network analysis (ENA) to model the structural relationships among themes. Analysis of 23 semi-structured interviews revealed that students frequently describ - [Characterizing Students' LLM Usage Behaviors and Their Association with Learning in Critical Thinking Tasks](https://edtechdev.github.io/aied/articles/students-llm-usage-critical-thinking/): Characterizing Students' LLM Usage Behaviors and Their Association with Learning in Critical Thinking Tasks **Park, Orozco Vasquez, & Conati (2026)** — University of British Columbia. Accepted at EDM 2026. - [Why SuaCode?": Understanding African Students'' Motivations for Taking a Smartphone-Based Online Coding Course](https://edtechdev.github.io/aied/articles/suacode-african-students-motivations/): Addo, Munagah, Kumbol, Uchidiuno and Boateng study why African students enroll in SuaCode, a smartphone-based online coding course (from the team behind the Kwame AI teaching assistant) addressing the fact that under 1% of African secondary-school leavers have fundamental coding skills. Understanding learner motivations informs the design of accessible, AI-supported MOOCs for low-resource contexts - [SupplyNet: Supporting Visual Exploratory Learning in Supply Chain via Contextual Multi-Agent Simulation](https://edtechdev.github.io/aied/articles/supplynet-visual-exploratory-learning/): SupplyNet is a gamified visual simulation system that uses a contextual graph-based llm multi-agent framework to model interdependent supply chain dynamics. Designed for professional-training in supply chain management (SCM), it replaces traditional abstract simulations with a manipulable decision space combining an interactive network view, a branching timeline for "what-if" exploration, and a ta - [Surfacing Isolated Learners with Outcome-Independent Mediation of Feedback between Teachers and Students Using AI](https://edtechdev.github.io/aied/articles/surfacing-isolated-learners/): **Authors:** Junsoo Park, Youssef Medhat, Htet Phyo Wai, Ploy Thajchayapong, Ashok K. Goel (2026) — Georgia Tech - [Multimodal Dialogue in STEM Education](https://edtechdev.github.io/aied/articles/syal-multimodal-dialogue-stem-2026/): **The Multimodal Interference Effect** describes a systemic accuracy drop when LLMs encounter image-rich STEM problems: from ~96% on text-only physics problems to ~74% on multimodal ones. A simple three-step structured dialogue intervention — eliciting visual descriptions, correcting observable misreadings without giving away physics, and re-prompting — corrects 82% of all errors and 100% of visua - [Sycophantic AI makes human interaction feel more effortful and less satisfying over time](https://edtechdev.github.io/aied/articles/sycophantic-ai-social-interaction-2026/): Ibrahim, Hafner, Cheng, Lee, Anselmetti, Willer, Rocher & Yang (2026) provide large longitudinal experimental evidence (N = 3,075; 12,766 conversations; three-week census-representative U.S. sample) that **sycophantic AI — which affirms users' views rather than challenging them — displaces real human relationships**: users became nearly as likely to seek personal advice from the AI as from close f - [The Competence Paradox: Negotiating Ease, Risk, and Creative Identity in Text-to-Image Generative AI Use Among Art and Design Students](https://edtechdev.github.io/aied/articles/t2i-competence-paradox-2026/): **Synthesis:** Liu, Meng, and Zhang (2026) examined technology acceptance of text-to-image (T2I) generative AI in art and design education from both educators' and students' perspectives, using a modified exploratory sequential mixed-methods design (QUAL-QUAN-qual). Based on instructor focus groups, a survey of 417 college students, and semi-structured interviews, they found that performance expec - [TACT: Taxonomy-Aligned Post-Training for Pedagogically Adaptive English Tutoring](https://edtechdev.github.io/aied/articles/tact-pedagogically-adaptive-esl-tutoring/): **Synthesis:** TACT (Taxonomy-Aligned Conversational Tutor) presents a human-grounded framework for training and evaluating pedagogically adaptive ESL tutors powered by llm. Built on a Tutor-Strategy Taxonomy (13 strategies) and a Student-Move Taxonomy, TACT produces TACTutor — a model that improves over its Qwen3.5-4B backbone by 20.30% on a strategy-balanced benchmark and outperforms all evaluat - [Touching and Feeling the Data: A Reusable Software Pipeline for Tactile Statistical Graphs in Accessible Education](https://edtechdev.github.io/aied/articles/tactile-statistical-graphs-accessibility/): **Lawrence Obiuwevwi, Krzysztof J. Rechowicz, Jessica M. Johnson, Erika Frydenlund, Vikas Ashok, Sachin Shetty, Sampath Jayarathna** — IEEE IRI 2026, submitted 1 Jul 2026 - [Taklif.AI: LLM-Powered Platform for Interest-Based Personalized College Assignments](https://edtechdev.github.io/aied/articles/taklif-ai-interest-based-personalized-assignments/): Taklif.AI: LLM-Powered Platform for Interest-Based Personalized College Assignments **Kurdya et al. (2026)** — Multiple institutions. arXiv cs.AI. - [The Role of Artificial Intelligence in Green Education: Optimizing Teacher Workflow and Enhancing Pedagogical Design under Sustainable Development Pedagogy (SDP) Constraints](https://edtechdev.github.io/aied/articles/talebzadeh-ai-green-education-2026/): **Synthesis:** Talebzadeh (2026) conducts a quasi-experimental study with 28 pre-service teacher teams, finding that AI-assisted Sustainable Development Pedagogy constraints significantly improve instructional design quality (t(27) = 13.78, p < 0.001, Cohen's d = 2.80). The intervention transformed teachers from conventional designers into strategic educational managers. - [What Robots Do Matters More Than What They Look Like: Task Context Shapes Trust in Educational HRI](https://edtechdev.github.io/aied/articles/task-context-trust-educational-hri-2026/): **Synthesis:** This Discobot project study (2026) examines how robot appearance and task type jointly influence trust in socially assistive robots (SARs) in educational and information-sharing contexts. Using a within-subjects video-based experiment (N = 81), participants evaluated three robots with distinct appearances while performing three educationally relevant tasks: teaching, procedural inst - [TeachBench - Evaluating LLM Teaching Ability](https://edtechdev.github.io/aied/articles/teachbench-llm-teaching-evaluation/): While LLMs are increasingly used as teaching assistants, their teaching capability remains insufficiently evaluated — a critical gap in current AIED research. - [AI Adoption Among Teachers: Insights on Concerns, Support, Confidence, and Attitudes](https://edtechdev.github.io/aied/articles/teacher-ai-adoption-confidence/): A study of 260 Filipino teachers examined how institutional support, teacher confidence, and teacher concerns influence AI adoption attitudes: - [Towards Synergistic Teacher-AI Interactions with Generative Artificial Intelligence](https://edtechdev.github.io/aied/articles/teacher-ai-teaming-five-levels/): **Synthesis:** Drawing on a systematic literature review, this UCL chapter proposes a five-level framework of teacher-AI teaming—transactional, situational, operational, praxical, and synergistic—to capture how GenAI interactions may replace, complement, or augment teacher competence. The framework moves beyond task division toward collaborative decision-making where teachers and AI engage in nego - [Teacher-Authored Prompts for Configuring Student-AI Dialogue: K-12 Classroom Implementation](https://edtechdev.github.io/aied/articles/teacher-authored-prompts-student-ai-dialogue/): This large-scale K-12 deployment provides empirical evidence that teacher-authored prompts can reliably shape the cognitive quality of student-AI dialogue at classroom scale. The TASD system lets teachers define both the AI's role and the student-facing conversation starter, creating a two-layer orchestration model that produced 71% alignment with instructional goals. The 38% under-reach rate in c - [When Should Teachers Control AI Generation for Mathematics Visuals?](https://edtechdev.github.io/aied/articles/teacher-control-ai-generation-math-visuals/): Generative AI can help teachers rapidly create classroom-ready visual materials, particularly in mathematics where diagrams and visual representations must be **pedagogically meaningful and instructionally correct**. This paper investigates when and how teachers should control AI generation of mathematical visuals. - [Teacher education for artificial intelligence literacy through a self-determination theory perspective](https://edtechdev.github.io/aied/articles/teacher-education-ai-literacy-sdt-2026/): **Synthesis:** Chiu, Bali, Tondeur, Howard, and Chan (2026) apply self-determination theory (SDT) to investigate how need-supportive professional development (PD) impacts teachers' AI literacy, attitudes, anxiety, and engagement in online professional learning communities (PLCs). Using a sequential mixed-methods approach with 382 secondary school teachers, they found that need-supportive PD enhanc - [Balancing Teacher and Student Agency: Co-Orchestration Tool Design Supporting Real-Time Dynamic Pairing](https://edtechdev.github.io/aied/articles/teacher-student-agency-orchestration/): Yang et al. (2026) tackle a fundamental tension in AI-augmented classrooms: how to balance teacher orchestration with student agency during dynamic transitions between individual and collaborative work. Using participatory speed dating with teachers and students, the study maps a three-stage design space (before, during, and after pairing) and proposes a hybrid-control framework for analytic-based - [A Durability and Cross-Language Transfer Benchmark for a Validated Teaching-Feedback Classification Protocol](https://edtechdev.github.io/aied/articles/teaching-feedback-classification-benchmark/): Extends a prior validated protocol for classifying open-ended teaching-evaluation feedback by thematic category and sentiment, introducing a durability and cross-language transfer benchmark. Institutions collect far more teaching feedback than they read; automated classification makes it actionable. - [Teaching Intro AI When the Tools Can Do the Homework: A Course Redesign and a Student Bill of Rights](https://edtechdev.github.io/aied/articles/teaching-intro-ai-course-redesign-bill-of-rights-2026/): **Synthesis:** This experience report describes the redesign of an introductory AI course at the University of Washington Bothell in response to LLMs being able to complete most assignments. The redesign retained the classical core (search, MDPs, reinforcement learning) while adding a strand where students build an LLM from scratch. Assessment was rebuilt around in-class exercises, reflective writ - [Findings of the First Teaching Monster Challenge: A Benchmark of Pedagogical Content Knowledge in AI Agents](https://edtechdev.github.io/aied/articles/teaching-monster-pck-benchmark-2026/): **Synthesis:** Lin et al. (2026) present the **Teaching Monster Challenge**, the first instructional-video generation benchmark that treats the learner persona as an explicit evaluation criterion, measuring whether AI agents can adapt a lesson to a specified learner — teacher-ai-competency. Systems receive a topic and a learner persona and must generate a complete instructional video, screened by - [TeachingCoach: A Fine-Tuned Scaffolding Chatbot for Instructional Guidance to Instructors](https://edtechdev.github.io/aied/articles/teachingcoach-chatbot-instructor-guidance/): **Authors:** Isabel Molnar, Peiyu Li, Si Chen, Sugana Chawla, James Lang, Ronald Metoyer, Ting Hua, Nitesh V. Chawla **Year:** 2026 **Venue:** arXiv (cs.AI) **Year:** 2026 **Venue:** arXiv (cs.AI) - [Teachy Mini: Development and Preliminary Evaluation of a Knowledge-Based Generative Social Robot for Higher Education](https://edtechdev.github.io/aied/articles/teachy-mini-generative-social-robot-higher-ed-2026/): **Synthesis:** Vonschallen, Kaufmann, Oberle, Eyssel, and Schmiedel (2026) operationalize knowledge-based design (KBD) requirements for generative social robots (GSRs) by implementing them in the Reachy Mini robot platform through system prompting, retrieval-augmented generation, and stateful prompt orchestration, producing Teachy Mini — a GSR tutoring system for higher education. Recognizing that - [Technology-Enhanced Tabletop Exercises for Cybersecurity Education: Lessons Learned](https://edtechdev.github.io/aied/articles/tech-enhanced-tabletop-cybersecurity-education/): Innovative practice paper examining the integration of technology-enhanced tabletop exercises into cybersecurity curricula. Addresses the gap between professional TTX practice and university adoption, presenting lessons learned from implementation in computing education contexts. - [Temporal Smoothness Doubly Robust Learning for Debiased Knowledge Tracing](https://edtechdev.github.io/aied/articles/temporal-smoothness-debiased-kt/): - Knowledge tracing (KT) systems suffer from selection bias because exercise recommendations are non-random, so training on observed logs with standard empirical risk produces biased mastery estimates. - The proposed Temporal Smoothness Doubly Robust (TSDR) framework combines a propensity model with an error imputation model, jointly optimizing the KT predictor and the imputation model. - A tempor - [Test-Driven, AI-Assisted Learning: Replacing Lectures with Weekly Closed-Book Tests](https://edtechdev.github.io/aied/articles/test-driven-ai-assisted-learning/): Liu et al. (2026) report on a 13-week Test-Driven, AI-Assisted (TDAA) redesign of a Theory of Computation course at HKUST (Guangzhou). The course replaced all lectures with self-directed, AI-assisted learning and weekly closed-book tests serving as high-frequency quality gates. AI agents helped the instructor prepare learning paths, course websites, test drafting, grading workflows, and content re - [Text Simplification for Intelligent Tutoring](https://edtechdev.github.io/aied/articles/text-simplification-its/): **MuTSE** (Roscan et al., 2026) addresses a critical need in **Intelligent Tutoring Systems (ITS)**: delivering content at the right reading level for each learner. - [The Scaffolded AI literacy (SAIL) framework: Results of a Delphi study for equitable AI literacy framework design in education](https://edtechdev.github.io/aied/articles/the-scaffolded-ai-literacy-sail-framework-results-of-a-delphi-study-for-equitabl/): **Synthesis:** MacCallum, Parsons, and Mohaghegh (2026) report on a three-round Delphi study that created the Scaffolded AI Literacy (SAIL) framework — a broadly applicable, age-agnostic framework for developing equitable AI literacy across all stages of education. Unlike most existing frameworks, which aggregate older literature or focus on non-generalizable contexts, SAIL provides a scaffolded c - [The synergy of pedagogical agents and metaphorical design: Reducing psychological distance to enhance video learning](https://edtechdev.github.io/aied/articles/the-synergy-of-pedagogical-agents-and-metaphorical-design-reducing-psychological/): **Synthesis:** This study examined the effects of pedagogical agents (real vs. virtual) and metaphorical design on learners' performance, attention, comprehension, and psychological distance in a 2x2 between-subjects design with 129 learners. Results showed both virtual pedagogical agents and metaphorical design improved learning performance and reduced psychological distance. Metaphorical design - [TibetCPR: A Multimodal Tactile Feedback System for CPR Training in High-Altitude Regions](https://edtechdev.github.io/aied/articles/tibetcpr-ai-training-feedback/): **Yibo Meng, Ruiqi Chen, Zhiming Liu, Xiaolan Ding** — Accepted at MobileHCI 2026 — cs.HC - [A Tool-Invariant Framework for Teaching and Assessing Computational Methods in the Age of Agentic AI](https://edtechdev.github.io/aied/articles/tool-invariant-framework-agentic-ai/): **Larry Engelhardt (Francis Marion University)** — *arXiv:2607.15518* [physics.ed-ph], submitted 17 Jul 2026. CC BY 4.0. doi:10.48550/arXiv.2607.15518. - [Tracing GenAI Literacy: Student-AI Interaction Patterns in Academic Writing](https://edtechdev.github.io/aied/articles/tracing-genai-literacy-interaction-patterns/): Identifies interaction signatures of LLM literacy using Epistemic Network Analysis (ENA) on logs from 162 students. High-literacy students exhibit iterative, strategic refinement and dense cognitive networking, while low-literacy students rely on direct, linear commands. This work emphasizes that ai-literacy is a developmental capacity requiring structured scaffolding and prompt-engineering discip - [Beyond Perspectives: A Trio-Ethnography of Interpretation Evolution in LLM-Supported Programming Education](https://edtechdev.github.io/aied/articles/trio-ethnography-llm-programming-education/): This experience report introduces trio-ethnography — structured dialogue between two computing educators with differing teaching philosophies and one undergraduate CS student — as a method for surfacing how educators' interpretations of students' AI use evolve. The central finding is that much AI-supported learning is invisible from the classroom: across three conversations, the student's lived-ex - [Trust and Reliance on AI in Education: AI Literacy and Need for Cognition as Moderators](https://edtechdev.github.io/aied/articles/trust-reliance-ai-education-2026/): Pitts, Rani & Mildort (2026, AIED) show with 432 undergraduates that **higher trust in an AI assistant is associated with lower appropriate reliance**: students who trusted the assistant more were worse at discriminating correct from misleading AI suggestions during Python problem-solving. The relationship is non-linear and **moderated by AI literacy and need for cognition** — trust is not a safe - [TurtleAI: Benchmarking Multimodal Models for Visual Programming in Turtle Graphics](https://edtechdev.github.io/aied/articles/turtleai-visual-programming-benchmark/): **Synthesis:** Vision-language models (VLMs) have been explored for visual programming, where they generate code to solve visual tasks. However, most prior work focuses on visual programming for productivity; it remains unclear how well current VLMs perform on education-oriented visual programming and what factors - [The Tutoring Effectiveness Index: Predicting LLM Math Tutor Quality from Four Conversation Signals](https://edtechdev.github.io/aied/articles/tutoring-effectiveness-index/): **Authors:** Shim Jaechang, Unggi Lee (2026) — CIKM 2026 - [Tutoring-Specific vs. General-Purpose AI in Education](https://edtechdev.github.io/aied/articles/tutoring-specific-vs-general-ai/): 1. **Desirable difficulties** — General-purpose AI removes productive struggle; tutoring tools preserve it via graduated hints. 2. **Germane load** — Effective learning requires processing that feels effortful. General AI short-circuits this. See cognitive-load-theory. 3. **Metacognition suppression** — When AI completes reasoning, students lose practice in monitoring their own understanding. - [Supporting Tutors in the Gig Economy with Automated Feedback: A Case Study on Ringle](https://edtechdev.github.io/aied/articles/tutors-gig-economy-automated-feedback/): Park et al. (2026) explore AI-powered automated feedback for tutors on Ringle, a popular online English tutoring platform in the gig economy. Their research probe analyzed tutors' lessons and provided automated feedback, followed by a survey of 36 tutors. Findings reveal that while tutors perceived automated feedback more negatively than learner feedback, they valued it for self-monitoring and und - [Thinking Through AI: Advancing Cognitive and Collaborative Research for AI in Education](https://edtechdev.github.io/aied/articles/tzirides-thinking-through-ai-2025/): **Synthesis:** Tzirides, Galla, Cope & Kalantzis (2025) introduce the "Thinking Through AI" framework combining cognitive labs, think-aloud protocols, and cyber-social methods. A pilot with 30 students at a rural Alaska school showed how AI-enabled tools can enhance middle school writing instruction, with the framework positioning educators and learners as co-creators in AI tool design. - [Understanding Student Effort Using Response-Time Propensities During Problem Solving](https://edtechdev.github.io/aied/articles/understanding-student-effort-response-time/): Adaptive learning systems produce substantial learning gains, yet many students engage too briefly or superficially to benefit. This paper addresses the central challenge of **measuring student effort** during multi-step problem solving using response-time propensities. - [The University AI Didn''t Replace: Rethinking Universities in the AI Era](https://edtechdev.github.io/aied/articles/universities-ai-era-rethinking/): **Synthesis:** Rather than replacing universities, generative AI **redefines their essential functions** — this paper proposes a four-level framework of institutional AI adoption and argues that the central challenge is moving from isolated, individual-driven experimentation to strategic integration, supported by workload and recognition systems. - [Unveiling patterns of socially shared regulation in relation to self-regulated learning: The roles of individual profiles and group dynamics in online collaborative learning](https://edtechdev.github.io/aied/articles/unveiling-patterns-of-socially-shared-regulation-in-relation-to-self-regulated-l/): **Synthesis:** This study employed a three-layer analytical method combining cluster analysis, content analysis and complex network analysis to investigate how socially shared regulation of learning (SSRL) patterns evolve in relation to individual self-regulated learning (SRL) profiles. Data from 60 undergraduates in a 16-week course with over 16,000 trace entries revealed three SRL profiles based - [Towards Valid Student Simulation with Large Language Models](https://edtechdev.github.io/aied/articles/valid-student-simulation-llm-2026/): Yuan et al. (2026) present a conceptual and methodological framework for valid LLM-based simulating-students. They identify the **competence paradox** — broadly capable LLMs asked to emulate partially knowledgeable learners produce unrealistic error patterns and learning dynamics — and reframe student simulation as a constrained generation problem governed by an explicit **Epistemic State Specific - [VeriForge: Mitigating Latent Knowledge Gaps in Narrative Drafting via Mixed-Initiative Scaffolding](https://edtechdev.github.io/aied/articles/veriforge-narrative-drafting-scaffolding-2026/): **Synthesis:** Sun et al. (2026) present VeriForge, a mixed-initiative generative-ai writing system that assumes initiative over domain discovery while the author retains initiative over narrative synthesis, using proactive highlighting, dual-stream querying with source-anchored Knowledge Cards, and a spatial Knowledge Canvas to surface latent knowledge gaps without homogenizing voice. - [VETTING: A dual-LLM framework for in-loop safety verification via policy isolation in educational AI](https://edtechdev.github.io/aied/articles/vetting-dual-llm-safety-education/): **Synthesis:** VETTING: A dual-LLM framework for in-loop safety verification via policy isolation in educational AI - [From Idea to Classroom in Days: Using "Vibe Coding" to Create a Programming Process Visualizer from IDE Activity Logs](https://edtechdev.github.io/aied/articles/vibe-coding-programming-process-visualizer/): Describes rapid development of a Thonny log visualizer using AI-assisted 'vibe coding' to make student programming processes visible to teachers. Piloted in a 160-student introductory programming course. Provides interactive timelines, session summaries, code-size graphs, and programming-process replays supporting teacher decision-making and academic-integrity clarification. - [Vibe Compiler: A Research-Logic Synthesis Tool That Runs without Prompt Engineering -Toward Enhancing Metacognition for Sustaining Agency in the Age of Generative AI-](https://edtechdev.github.io/aied/articles/vibe-compiler-metacognition-genai-agency-2026/): **Synthesis:** This paper introduces the Synthesis-Analysis Reciprocity Model and the Vibe Compiler tool to preserve human epistemic agency during GenAI-assisted intellectual work. The model frames intellectual construction as a reciprocal interaction between Synthesis (combining components into a whole) and Analysis (decomposing a whole into components), arguing that over-reliance on AI for synth - [VISMATIC: Secure Containerized Framework for Process-Oriented CS Education Monitoring](https://edtechdev.github.io/aied/articles/vismatic-secure-sandbox-cs-education/): Addresses a critical tension in stem-education: the widespread adoption of generative AI makes it impossible to distinguish authentic student effort from AI code synthesis by evaluating final submissions alone. The paper presents **VISMATIC**, a rootless containerized framework that pairs robust environment isolation with explicit user-interaction tracking at the API level. - [Evaluating a Visual Query Tracer and Builder for Learning Declarative Logic Programming](https://edtechdev.github.io/aied/articles/visual-query-tracer-declarative-logic-learning/): Nemo Explain Visualizer (nev) is an interactive visual query tracer and builder for the Datalog reasoner Nemo. Although built for expert users, the authors conducted a qualitative study with 14 participants at varying levels of involvement in a university knowledge-graph course to assess whether such tools help students learn declarative logic programming. - [What Makes Words Hard? Sakura at BEA 2026 Shared Task on Vocabulary Difficulty Prediction](https://edtechdev.github.io/aied/articles/vocabulary-difficulty-prediction/): This paper presents two complementary approaches to predicting vocabulary difficulty for language learners, achieving state-of-the-art results in the BEA 2026 Shared Task. The work advances both the accuracy and explainability of NLP systems for educational applications. - [Say What? Examining Text and Voice Input Modalities for Prompt-Based Programming in Computing Education](https://edtechdev.github.io/aied/articles/voice-text-prompt-problems-computing-education/): Nearly all prior research on LLMs in computing education has used text input, yet voice-enabled interfaces are becoming common. This exploratory study investigated how introductory programming students interact with **Prompt Problems** — tasks requiring natural-language prompts to generate correct code — under free choice of text or voice (N = 919). For two of three problems, students who typed we - [Robotics and Artificial Intelligence in Education: Transformations, Challenges, and Future Directions](https://edtechdev.github.io/aied/articles/white-wu-robotics-ai-education-2026/): **Synthesis:** White & Wu (2026) critically examine the integration of AI and robotics into education, arguing that while transformative potential exists at all levels, effective integration requires sustained investment, coherent policy, rigorous teacher preparation, and ethical practice. The review finds research remains geographically concentrated, methodologically short-term, and insufficientl - [Will, Skill, Not Tool: Chinese university students' acceptance of generative AI for academic writing in informal English medium instruction settings](https://edtechdev.github.io/aied/articles/will-skill-not-tool-chinese-university-students-acceptance-of-generative-ai-for-/): **Synthesis:** By adopting the Will, Skill, Tool (WST) model, this study explores how EMI students' intentions to use GenAI for academic writing are shaped by AI-specific variables. Survey data from 512 university students at an EMI university in China found that will-related factors (attitudes, perceived risks, perceived importance of policy) and the skill factor (AI literacy) were significant an - [Through the WordStream Glass: Revisiting Quantitative Encoding for Qualitative Learning Analytics](https://edtechdev.github.io/aied/articles/wordstream-glass-learning-analytics/): Revisits WordStream (2009) as a quantitative encoding for qualitative learning analytics; demonstrates how structured coding can surface cohort-level trends while preserving individual narrative context. - [Explainable Artificial Intelligence in Education (XAI-ED)](https://edtechdev.github.io/aied/articles/xai-education-framework/): 📄 DOI: 10.1016/j.caeai.2022.100074 - [HiLLM-CD: LLM-Enhanced Hierarchical Cognitive Diagnosis](https://edtechdev.github.io/aied/articles/xie-hillm-cd-2026/): **Synthesis:** Xie, Yang, Zhang, Li, Wang, Yang & Gao (2026) propose HiLLM-CD, a tree-structured framework for cognitive diagnosis that represents student proficiency as node-wise values on a concept tree, enabling coarse-to-fine diagnosis. A multi-agent LLM pipeline eliminates the need for expert annotations by automatically generating concept trees and exercise-concept links from educational tex - [How YouTube Frames ChatGPT Use in Education: An Epistemic Network Analysis with Supporting Multimodal Metadata](https://edtechdev.github.io/aied/articles/youtube-frames-chatgpt-education/): Uses epistemic network analysis of multimodal YouTube metadata (transcripts, titles, thumbnails, comments) to show how different creator groups frame ChatGPT use in education, revealing divergent narratives around learning support versus academic-integrity risk. The work connects to broader debates about how generative-ai systems reshape student-experience and the conditions under which AI support - [Comprehensive Review of Intelligent Tutoring Systems](https://edtechdev.github.io/aied/articles/zerkouk-comprehensive-review-its-2025/): **Comprehensive Review of Intelligent Tutoring Systems** — Journal of Computers in Education (2025). A systematic literature review covering 2010–2025 that analyzes the deployment and effectiveness of Intelligent Tutoring Systems (ITS) in real educational settings. The review examines the full landscape of ITS research — pedagogical strategies, natural language processing, adaptive learning mechan - [When Help is Unhelpful: Evaluating AI Tutors for Productive Struggle](https://edtechdev.github.io/aied/articles/zhang-tutormoments-2026/): **Synthesis:** Zhang et al. (2026) introduce TutorMoments, a replay-based evaluation framework that tests whether LM tutors adapt their pedagogical actions to context — scaffolding when support is needed, pushing for rigor when students are ready, and avoiding over-scaffolding. Evaluating 462 teacher-annotated transcripts from grades 2-7 math tutoring, they find frontier models default toward over ## Concepts - [Academic Integrity](https://edtechdev.github.io/aied/concepts/academic-integrity/): **Academic integrity** — the ethical framework governing honest academic work in the age of AI. The wiki documents how the concept has been reframed by generative AI: from a problem of detecting dishonest output to a design problem of making honest work visible, verifiable, and worth producing. Academic integrity research in this space has evolved from detection-focused approaches toward fundament - [Accessible Learning](https://edtechdev.github.io/aied/concepts/accessible-learning/): **Accessible Learning** — the design and delivery of educational experiences that accommodate diverse learner needs, spanning physical, cognitive, sensory, and situational differences. In AI in education, accessible learning research examines both how AI tools can remove barriers for disabled and neurodivergent learners and how AI systems themselves must be designed to avoid creating new accessibi - [Active Learning](https://edtechdev.github.io/aied/concepts/active-learning/): **Active Learning** — instructional approaches that engage students in doing things and thinking about what they are doing, rather than passively receiving information. In AI in education, active learning research examines both how AI tools can support active learning pedagogies and how active engagement with AI tools — rather than passive consumption — affects learning outcomes. - [Adaptive Learning](https://edtechdev.github.io/aied/concepts/adaptive-learning/): **Adaptive learning** — AI-driven educational systems that adjust content, pacing, and instructional strategies based on individual learner characteristics and performance. Adaptive learning is the operational goal of much AI in education research: using student models to personalize instruction. - [Adaptive Prompt Routing](https://edtechdev.github.io/aied/concepts/adaptive-prompt-routing/): **Adaptive prompt routing** — dynamically selecting or composing LLM prompts per task and learner — connects the wiki's prompt-engineering and learning-to-prompt-adaptive-tutoring threads, where prompt selection becomes part of the tutoring system itself rather than a user skill. - [AI from the Administrator Perspective](https://edtechdev.github.io/aied/concepts/administrator/): Stub — pending source ingestion. AI adoption, strategy, and governance from the institutional administrator and leadership perspective. - [Adult Learning](https://edtechdev.github.io/aied/concepts/adult-learning/): **Adult Learning** — a key concept in AI in education research. Explored across 1 articles in this wiki. - [Affective Computing](https://edtechdev.github.io/aied/concepts/affective-computing/): **Affective computing** in education uses physiological and behavioral signals to sense learner emotion and adapt instruction — see affective-text-wearable-student-health, multimodal-affective-its-presentation, and kar-mathbuddy-affective-math-tutoring-2025. The wiki also documents emotional risks of AI interaction, including sycophantic-ai-social-interaction-2026 and shame-guilt-ai-regulation-com - [Affective Tutoring](https://edtechdev.github.io/aied/concepts/affective-tutoring/): Integrating emotional awareness into AI tutoring systems can yield measurable pedagogical gains, but the same affective sophistication risks amplifying harms if learner agency is eroded by empathetic-seeming automation.^kar-mathbuddy-affective-math-tutoring-2025^favero-critical-ai-tutors-empower-enslave-2025 - [Learner Agency](https://edtechdev.github.io/aied/concepts/agency/): **Learner agency** — the capacity of learners to act intentionally, make choices, and exercise control over their own learning. In AI in education, agency is a central concern because AI tools can both support and undermine learners' control: well-designed AI preserves and amplifies learner autonomy, while over-reliance or passive acceptance of AI output can erode it. Agency connects to self-regul - [Agentic AI in Education](https://edtechdev.github.io/aied/concepts/agentic-ai/): **Agentic AI** — AI systems that autonomously plan, execute, and adapt multi-step workflows to achieve learning goals, going beyond single-turn Q&A to act as persistent, goal-directed collaborators: AI tutors that scaffold over extended interactions, multi-agent systems orchestrating instructional designs, and agents that co-regulate learning. The paradigm shift from prompt-responding tool to acti - [AI Ed Evaluation](https://edtechdev.github.io/aied/concepts/ai-ed-evaluation/): **AI-ed evaluation** — the body of methods, benchmarks, and criteria used to assess whether AI education tools (LLM-based tutors, automated graders, feedback systems, agents) actually work — not just on headline accuracy, but on reliability, pedagogical quality, validity, and real learning impact. A recurring theme across the wiki's research is that evaluation must be domain-specific, reliability- - [AI Education](https://edtechdev.github.io/aied/concepts/ai-education/): **AI Education** — the broad field encompassing both AI in education (using AI to teach) and AI literacy (teaching about AI). As the wiki's umbrella concept, AI education connects instructional technology, learning science, educational policy, and AI development. - [AI Feedback Quality](https://edtechdev.github.io/aied/concepts/ai-feedback-quality/): **AI feedback quality** — the accuracy, usefulness, timeliness, and pedagogical value of feedback generated by AI systems for learners. As AI-generated feedback becomes ubiquitous in education, understanding what makes feedback effective — and when it falls short — is critical to ensuring AI supports rather than undermines learning. - [AI Governance Education](https://edtechdev.github.io/aied/concepts/ai-governance-education/): **AI governance in education** spans institutional policy, regulation, and privacy: genai-policies-higher-ed-computing, genai-declaration-frameworks-higher-education, and genai-assessment-governance document how universities translate AI capability into acceptable-use frameworks and assessment rules. - [AI Literacy](https://edtechdev.github.io/aied/concepts/ai-literacy/): **AI literacy** — the knowledge, skills, and critical dispositions needed to understand, evaluate, and effectively use AI technologies in educational contexts. AI literacy spans foundational understanding of how AI works, practical competence in using AI tools, critical evaluation of AI outputs, and ethical awareness of AI's societal implications. - [AI Misuse and Learning Harm](https://edtechdev.github.io/aied/concepts/ai-misuse-learning-harm/): **AI misuse and learning harm** — the causal relationship between students offloading cognitive work to generative AI and reduced durable learning, even when immediate task performance rises. The defining feature is a performance–learning gap: AI inflates assisted performance while degrading unassisted, closed-book, and retention outcomes. - [AI Tutoring](https://edtechdev.github.io/aied/concepts/ai-tutoring/): **AI tutoring** — the use of AI (especially llm and intelligent-tutoring) to provide personalized, adaptive, scalable instructional support: conversational tutors, scaffolded feedback systems, adaptive platforms, and agent-based tutors with long-term learner models. Effectiveness hinges on pedagogical design (scaffolding, feedback quality, autonomy balance) rather than the model alone — see measur - [Assessment Validity in AI Education](https://edtechdev.github.io/aied/concepts/assessment-validity/): **Assessment validity** — whether assessments measure what they claim to measure. AI in education raises fundamental validity questions: do AI-graded assessments assess student learning or AI prompting skill? Does AI use invalidate traditional assessment assumptions? - [Assessment](https://edtechdev.github.io/aied/concepts/assessment/): **Assessment** is a central concept in AI in education research, connected to 8 articles in this wiki. - [Automated Assessment](https://edtechdev.github.io/aied/concepts/automated-assessment/): **Automated assessment** — the use of AI to evaluate student work, from formative quizzes to high-stakes exams. Automated assessment spans multiple modalities — multiple-choice, short answer, essay, code, and performance-based evaluation. - [Automated Essay Scoring](https://edtechdev.github.io/aied/concepts/automated-essay-scoring/): **Automated Essay Scoring (AES)** — the use of AI to evaluate and score written essays, spanning traditional statistical approaches, fine-tuned language models, and increasingly accessible LLM-based prompting strategies. AES research in this wiki covers scoring accuracy, fairness and bias, psychometric validity, and practical accessibility for educators. - [Automated Grading](https://edtechdev.github.io/aied/concepts/automated-grading/): **Automated grading** — AI systems that evaluate student work, from multiple-choice scoring to essay assessment and code review. Automated grading is one of the most mature and widely-deployed AI in education applications. - [Automated Question Generation](https://edtechdev.github.io/aied/concepts/automated-question-generation/): Automated question generation leverages NLP and LLMs to create educational assessments at scale. Wei & Stamper (2025) introduced the **generate-then-validate** paradigm, reducing hallucination by 62% compared to direct generation and achieving 89% accuracy on STEM datasets. - [Benchmark](https://edtechdev.github.io/aied/concepts/benchmark/): **Benchmark** — standardized test suites and evaluation frameworks used to measure AI model performance on educational tasks. Benchmarks enable reproducible comparison across models and approaches, and are essential for evaluating the reliability, fairness, and pedagogical quality of AI in education systems. - [Bias Mitigation](https://edtechdev.github.io/aied/concepts/bias-mitigation/): **Bias mitigation** in educational AI requires auditing models across the pipeline: gender-bias-transfer-llm-writing, ai-scoring-language-bias-physics, llm-cultural-relevance-k12, and equity (merged into equity) document bias sources and mitigation strategies from data curation to prompt design. - [Block-Based Programming](https://edtechdev.github.io/aied/concepts/block-programming/): **Block-based programming** — a visual programming paradigm in which learners build programs by snapping together graphical blocks (e.g., Scratch, Blockly) rather than typing text. Block-based environments lower the barrier to programming-education by eliminating syntax errors and making program structure visible, which is especially valuable for beginners and younger learners. They are widely use - [Cognitive Diagnosis](https://edtechdev.github.io/aied/concepts/cognitive-diagnosis/): **Cognitive diagnosis** — the inference of a learner's latent knowledge state — the specific concepts, skills, and misconceptions they have or lack — from their responses or behavior. It is the assessment-side counterpart to knowledge-tracing, focused on characterizing *what* a student knows rather than only predicting their next performance. - [Cognitive Load Theory](https://edtechdev.github.io/aied/concepts/cognitive-load-theory/): **Cognitive Load Theory** is a central concept in AI in education research, connected to 7 articles in this wiki. - [Cognitive Offloading](https://edtechdev.github.io/aied/concepts/cognitive-offloading/): **Cognitive offloading** — the use of external tools (including AI) to reduce internal cognitive demand, shifting mental work from the learner to the system. In AI in education, cognitive offloading is the central mechanism through which AI tools can either support or undermine learning: appropriate offloading frees cognitive resources for higher-order thinking, while excessive offloading bypasses - [Collaborative Learning](https://edtechdev.github.io/aied/concepts/collaborative-learning/): **Collaborative Learning** — instructional approaches where students work together to solve problems, complete tasks, or construct knowledge, supported or mediated by AI tools. In AI in education, collaborative learning research spans AI as a collaboration partner, AI as a mediator of human collaboration, and the design of collaborative AI tutoring systems. - [Computational Thinking](https://edtechdev.github.io/aied/concepts/computational-thinking/): **Computational thinking** — a problem-solving approach involving decomposition, pattern recognition, abstraction, and algorithmic design. In AI education, computational thinking is both a prerequisite for understanding AI systems and a skill that AI tools can help develop. - [Confidence Aware AI Assessment](https://edtechdev.github.io/aied/concepts/confidence-aware-ai-assessment/): **Confidence-aware AI assessment** — models that report uncertainty alongside scores — is examined in confidence-aware-student-drawing-assessment, cong-confidence-asag-2026, and llm-psychometric-calibration-cdp: calibrated confidence improves trust and enables appropriate delegation (trust-calibration, ai-ed-evaluation). - [Constructivist](https://edtechdev.github.io/aied/concepts/constructivist/): **Constructivist** learning theory — knowledge built through active experience — underpins wiki analyses of AI as genai-mindtool-generative-learning and icap-cognitive-engagement-llm-agents: AI tools support construction only when learners generate, not merely consume (active-learning, educational-theory). - [Creativity](https://edtechdev.github.io/aied/concepts/creativity/): **Creativity** — the capacity to generate novel and valuable ideas, solutions, or artifacts. In the AI era, creativity is a central educational stake: generative AI can both amplify creative work (as a divergent-thinking partner) and undermine it (by homogenizing output and replacing the generative process). - [Critical Thinking](https://edtechdev.github.io/aied/concepts/critical-thinking/): **Critical thinking** — the ability to analyze, evaluate, and synthesize information — is both a skill that AI tools can help develop and a competency that students must apply when using AI. In AI in education research, critical thinking appears in two interrelated forms: as a learning objective (teaching students to think critically) and as a safeguard against uncritical AI reliance. - [CS Education and AI](https://edtechdev.github.io/aied/concepts/cs-education/): **CS Education** — computer science education is the most-researched STEM subfield in the wiki, benefiting from natural alignment between AI tools and programming tasks. Code generation, debugging assistance, and automated code review are its primary AI applications. - [Culturally Relevant Pedagogy](https://edtechdev.github.io/aied/concepts/culturally-relevant-pedagogy/): Culturally Relevant Pedagogy (CRP), introduced by Gloria Ladson-Billings (1995), centers marginalized students' cultural references in curriculum design. Wang et al. (2025) demonstrate that **LLMs can support K-12 teachers** in implementing CRP: 78% of teachers found AI suggestions helpful for diversifying curriculum materials. - [Curriculum Design](https://edtechdev.github.io/aied/concepts/curriculum-design/): **Curriculum Design** — the process of planning and structuring what is taught across courses, programs, and institutions, including learning objectives, content sequencing, assessment strategies, and skill progression. In the AI era, curriculum design must balance foundational knowledge with emerging AI competencies, determining not just what students learn but how they learn to work with and cri - [Design Thinking](https://edtechdev.github.io/aied/concepts/design-thinking/): **Design Thinking** — a key concept in AI in education research. Explored across 1 articles in this wiki. - [Desirable Difficulties](https://edtechdev.github.io/aied/concepts/desirable-difficulties/): **Desirable difficulties** — the finding that harder retrieval conditions improve long-term learning (Bjork) — is the theoretical counterweight to AI smoothing: agentic-ai-pedagogical-best-practice-2026 calls for intentional friction, and generative-ai-reduced-study-time-math documents the cost of removing productive struggle. In the AI era, the principle warns that tools which eliminate effortful - [Digital Divide](https://edtechdev.github.io/aied/concepts/digital-divide/): **Digital divide** — the unequal distribution of access to, skills for, and benefits from digital (and increasingly AI) technologies across individuals, communities, and nations. In AI education, the digital divide is a central equity concern: generative AI is rapidly reshaping learning, and the gap between those who can use it effectively and critically and those who cannot threatens to deepen ex - [DOT Framework Survey: Practitioner Beliefs and Behaviors in AI-Enhanced Education](https://edtechdev.github.io/aied/concepts/dot-framework-survey/): A 2026 cross-sectional survey (n=72) by Gibson, Azukas, and Knezek examined how higher education practitioners think about and use AI in teaching, grounded in the **DOT Framework** — a synthesis of design-thinking and open-systems-theory. - [Dual-Process Theory](https://edtechdev.github.io/aied/concepts/dual-process-theory/): **Dual-process theory** — the account of cognition as operating through two interacting systems: a fast, automatic, intuitive System 1 and a slower, effortful, analytical System 2. In education, dual-process theory frames when learners rely on quick heuristics versus deliberate reasoning, and why they sometimes take cognitive shortcuts. - [Edtech Platform](https://edtechdev.github.io/aied/concepts/edtech-platform/): **Edtech Platform** is a central concept in AI in education research, connected to 15 articles in this wiki. - [Educational Measurement](https://edtechdev.github.io/aied/concepts/educational-measurement/): **Educational measurement** — psychometric theory applied to learning data — runs through the wiki's item-response-theory, knowledge-tracing, and assessment-validity pages: LLM-era measurement must reconcile classical psychometrics with new AI-generated response streams (psychometrically-aware-ai, educational-nlp). - [Educational NLP](https://edtechdev.github.io/aied/concepts/educational-nlp/): **Educational NLP** applies language technologies to learning: llm-item-difficulty-prediction, teaching-feedback-classification-benchmark, llm-sentiment-analysis-education-research, and vocabulary-difficulty-prediction show LLMs advancing analysis of student language at scale (educational-measurement, educational-nlp). - [Educational AI Policy](https://edtechdev.github.io/aied/concepts/educational-policy-ai/): **Educational AI policy** — the formal and informal rules governing AI use in educational institutions, from national legislation to classroom guidelines. Policy research in the wiki spans institutional governance, curriculum mandates, and teacher preparation requirements. - [Robots in Education](https://edtechdev.github.io/aied/concepts/educational-robotics/): **Robots in education (educational robotics)** — the use of physical or simulated robots as tools for teaching and learning. Educational robotics spans a wide spectrum: from programmable kits that teach computational thinking and programming, to socially assistive and humanoid robots that tutor, tell stories, model sign language, or rehearse social skills. It is valued for fostering problem solvin - [Efficacy Study](https://edtechdev.github.io/aied/concepts/efficacy-study/): **Efficacy Study** — a key concept in AI in education research. Explored across 6 articles in this wiki. - [Embodied Learning](https://edtechdev.github.io/aied/concepts/embodied-learning/): **Embodied learning** — the pedagogical principle that learning is grounded in bodily experience, physical interaction, and the sensory-motor context of the learner. Embodied approaches hold that cognition is not purely abstract but shaped by the body and its interaction with the environment. In AI in education, embodiment is realized through educational-robotics and social-robots, whose physical - [Engagement Metrics](https://edtechdev.github.io/aied/concepts/engagement-metrics/): **Engagement metrics** — the range of observable signals and measurement approaches researchers and systems use to operationalize student-engagement in AI-supported learning: behavioral (time-on-task, activity counts, interaction frequency), cognitive (depth of processing, critical engagement, discourse analysis), affective (emotion, motivation), and contextual (multitasking, attention). In AI-edu - [Equity in AI Education](https://edtechdev.github.io/aied/concepts/equity-in-ai-education/): Equity in AI Education addresses systemic disparities in access to, representation within, and benefits from AI educational tools. Three critical dimensions emerge: - [Equity in AI Education](https://edtechdev.github.io/aied/concepts/equity/): **Equity** — the principle that AI in education should serve all learners fairly, without exacerbating existing disparities. Equity research in the wiki examines access gaps, bias in AI systems, culturally responsive design, and the distribution of AI's benefits and harms. - [Ethics in AI Education](https://edtechdev.github.io/aied/concepts/ethics/): **Ethics** — the moral principles governing the design, deployment, and use of AI in educational contexts. AI education ethics spans data privacy, algorithmic fairness, transparency, accountability, and the broader question of what AI should and should not do in learning environments. - [Experiential Learning](https://edtechdev.github.io/aied/concepts/experiential-learning/): **Experiential learning** — learning through direct experience, reflection, and the application of knowledge in authentic or hands-on contexts ("learning by doing"). Drawing on Kolb's experiential learning cycle (concrete experience, reflective observation, abstract conceptualization, active experimentation), experiential approaches emphasize that learners learn most deeply when they act, observe - [Faculty Development](https://edtechdev.github.io/aied/concepts/faculty-development/): **Faculty development** — the processes, programs, and institutional supports that help educators develop the skills and confidence to teach effectively with AI. Faculty development spans individual training, curriculum redesign, and institutional policy change. - [Feedback Loop](https://edtechdev.github.io/aied/concepts/feedback-loop/): **Feedback loop** — the cyclical process where AI systems assess student work, deliver feedback, observe the student's response, and adapt subsequent instruction. Effective feedback loops close the gap between current and desired performance. - [Formative Assessment in AI Education](https://edtechdev.github.io/aied/concepts/formative-assessment/): Assessment designed to inform ongoing instruction and learning, as opposed to summative evaluation. AI systems can generate, validate, and adapt formative assessment items at scale, though quality varies dramatically across assessment types. - [Game-Based Learning](https://edtechdev.github.io/aied/concepts/game-based-learning/): **Game-based learning (GBL)** — the use of games themselves (digital or physical) as the medium and context for learning, where the game's mechanics, challenges, and progression carry educational content. Learners learn *through* playing. GBL is distinct from gamification, which adds game elements (points, badges, leaderboards) to non-game activities. In AI and robotics education, GBL is used to m - [Gamification](https://edtechdev.github.io/aied/concepts/gamification/): **Gamification** — the application of game-design elements (points, badges, levels, leaderboards, challenges, progress bars) to non-game contexts to motivate and engage users. Unlike game-based-learning (where learning happens *through* a game), gamification layers game mechanics onto an existing learning activity without turning it into a full game. In education, gamification is used to boost mot - [Generative AI](https://edtechdev.github.io/aied/concepts/generative-ai/): **Generative AI** — AI systems capable of producing text, code, images, and other content, most prominently large language models like GPT-4 and Claude. Generative AI is the technology driving the current wave of AI in education research. - [AI Governance](https://edtechdev.github.io/aied/concepts/governance/): **AI governance** — the frameworks, policies, institutional structures, and norms that guide the responsible design, deployment, and use of artificial intelligence in education. Governance spans formal institutional mechanisms (AI steering groups, policies on academic integrity and acceptable use, ethical review) and informal norms (faculty guidelines, professional development, cultures of respons - [Hallucination Risk](https://edtechdev.github.io/aied/concepts/hallucination-risk/): **Hallucination Risk** — the danger that AI systems generate plausible but factually incorrect or fabricated content in educational contexts, where such errors can mislead learners, undermine trust, and produce invalid assessments. Hallucination is particularly consequential in education because students may lack the domain knowledge to detect AI errors, and teachers may rely on AI-generated diagn - [Help-Seeking](https://edtechdev.github.io/aied/concepts/help-seeking/): **Help-Seeking** — a key concept in AI in education research. Explored across 4 articles in this wiki. - [AI in Higher Education](https://edtechdev.github.io/aied/concepts/higher-ed/): **AI in Higher Education** — the integration of artificial intelligence into university teaching, learning, assessment, and administration. Higher education is the most-studied context in the wiki, with over 100 articles examining how AI transforms college-level instruction, institutional policy, and student experience. - [Human AI Collaboration](https://edtechdev.github.io/aied/concepts/human-ai-collaboration/): **Human-AI collaboration** — the division of cognitive labor between people and models — is the wiki's core interaction theme: human-ai-collaboration-trust-expectations, humanlike-ai-collaborative-writing, genai-mindtool-generative-learning, and teacher-student-agency-orchestration examine trust, agency, and complementary roles (human-in-the-loop-ai, agentic-ai). - [Human-in-the-Loop AI for Education](https://edtechdev.github.io/aied/concepts/human-in-the-loop-ai/): Educational AI systems that strategically interleave automated generation with human expert judgment, preserving pedagogical quality while scaling production. Two recent implementations illustrate distinct architectures: - [Human-Robot Interaction](https://edtechdev.github.io/aied/concepts/human-robot-interaction/): **Human-robot interaction (HRI)** — the interdisciplinary study of how people and robots interact, encompassing perception, communication, collaboration, and the social, cognitive, and ethical dynamics of that interaction. In education, HRI underlies how learners perceive, trust, and learn with robots — whether programming a robot, conversing with a tutoring robot, or rehearsing social scenarios. - [Instructional Design with AI](https://edtechdev.github.io/aied/concepts/instructional-design/): **Instructional Design** — the systematic process of creating effective learning experiences through the analysis of learning needs and the design, development, implementation, and evaluation of instructional materials and activities. AI is transforming instructional design by automating content creation, enabling adaptive learning paths, supporting data-driven iteration, and augmenting — rather t - [Intelligent Tutoring](https://edtechdev.github.io/aied/concepts/intelligent-tutoring/): **Intelligent Tutoring Systems (ITS)** — a well-established subfield of AI in education that uses AI to model student knowledge, adapt instruction, and provide personalized feedback, typically through model-tracing, knowledge tracing, and scaffolded problem-solving. ITS research predates the LLM era but has been transformed by generative AI, creating hybrid systems that combine structured domain m - [Item Response Theory](https://edtechdev.github.io/aied/concepts/item-response-theory/): **Item response theory** — psychometric modeling of item difficulty and ability — meets LLMs in llm-item-difficulty-prediction, llm-psychometric-calibration-cdp, and knowledge-tracing-irt: AI predicts difficulty and calibrates assessment, improving measurement precision (educational-measurement, assessment-validity). - [K-12 AI Education](https://edtechdev.github.io/aied/concepts/k-12-ai-education/): K-12 AI Education encompasses the integration of artificial intelligence literacy, tools, and pedagogical approaches into primary and secondary education. Recent research reveals three critical pillars: - [K-12 AI Education](https://edtechdev.github.io/aied/concepts/k-12/): **K-12 AI education** — the use of artificial intelligence in primary and secondary education, spanning AI literacy curricula, AI tutoring, teacher support, and safety considerations unique to younger learners. - [Knowledge Graph](https://edtechdev.github.io/aied/concepts/knowledge-graph/): **Knowledge graph** — a structured representation of concepts and their relationships used to model domain knowledge, student understanding, and learning dependencies in AI in education systems. Knowledge graphs enable AI systems to reason about what students know, what they need to learn next, and how concepts relate to each other. - [Knowledge Tracing](https://edtechdev.github.io/aied/concepts/knowledge-tracing/): **Knowledge tracing** — modeling what learners know over time by tracking their performance on exercises and predicting future mastery. It is the wiki's richest modeling thread, spanning Bayesian, deep learning, and LLM-enhanced approaches to tracking student knowledge as it evolves. - [Language Learning](https://edtechdev.github.io/aied/concepts/language-learning/): **Language Learning** — the study of how AI supports second language (L2) acquisition, writing development, and linguistic diversity in educational settings. AI in education research in this wiki spans AI interlocutors for spoken dialogue, automated writing evaluation for L2 learners, reading support, and concerns about language bias in AI scoring systems. - [Learning Analytics](https://edtechdev.github.io/aied/concepts/learning-analytics/): **Learning analytics** — the measurement, collection, analysis, and reporting of data about learners and their contexts for the purpose of understanding and optimizing learning. AI has transformed learning analytics from descriptive dashboards to predictive and prescriptive systems. - [Learning Gains](https://edtechdev.github.io/aied/concepts/learning-gains/): **Learning gains** — measurable improvements in student knowledge, skills, or competencies resulting from educational interventions, including AI-assisted instruction. In AI in education research, learning gains serve as the primary outcome measure for evaluating whether AI tools actually improve learning — not just engagement or satisfaction. - [Lifelong Learning and AI](https://edtechdev.github.io/aied/concepts/lifelong-learning/): Stub — pending source ingestion. Lifelong learning and AI support for continuous education beyond formal schooling. - [Large Language Models (LLMs)](https://edtechdev.github.io/aied/concepts/llm/): **Large Language Models (LLMs)** — neural network models trained on vast text corpora that generate human-like text, powering most modern AI in education applications. LLMs are the computational backbone of generative AI tutoring, assessment, and content generation in education. - [Math Education](https://edtechdev.github.io/aied/concepts/math-education/): **Math Education** — the study of how students learn mathematics and how AI can support mathematics teaching, spanning affective tutoring, cognitive diagnosis from handwritten work, productive struggle evaluation, help-seeking behavior, teacher-AI collaboration for visual generation, and student-AI interaction trajectories. Math education is the most active domain-specific research area in this wi - [Metacognition](https://edtechdev.github.io/aied/concepts/metacognition/): Metacognition — thinking about one's own thinking — is both a target of AI education research (can AI tools develop students' metacognitive skills?) and a risk factor (AI completing tasks may suppress metacognitive practice).^stanford-evidence-base-ai-k12-2026^scheu-mobile-chatbot-journaling-motivation-2026 - [Motivation](https://edtechdev.github.io/aied/concepts/motivation/): **Motivation** — the psychological processes that initiate, direct, and sustain goal-directed behavior. In AI in education, motivation research examines how AI tools affect learners' and teachers' motivation — whether AI scaffolds or undermines persistence, curiosity, and intrinsic engagement — and how motivational states shape the effectiveness of AI-mediated learning. - [Multimodal](https://edtechdev.github.io/aied/concepts/multimodal/): **Multimodal** — a key concept in AI in education research. Explored across 3 articles in this wiki. - [Neurodiversity](https://edtechdev.github.io/aied/concepts/neurodiversity/): **Neurodiversity** — the framing that neurological differences such as autism, ADHD, dyslexia, and dyspraxia are natural variations in human cognition rather than deficits to be corrected. In education, a neurodiversity-affirming approach designs learning environments that accommodate and leverage these differences rather than forcing conformity to a single cognitive norm. - [Open Source](https://edtechdev.github.io/aied/concepts/open-source/): **Open-source** AI in education is studied in lata-ferpa-compliant-local-llm-autograder, vismatic-secure-sandbox-cs-education, and open-source (tag) pages: local open models address privacy, cost, and customization but bring deployment and quality burdens (regulation, ai-education). - [Over-Reliance](https://edtechdev.github.io/aied/concepts/over-reliance/): **Over-reliance** — excessive or uncalibrated dependence on AI tools where students delegate cognitive work they should perform themselves, resulting in reduced learning, diminished agency, and the displacement of skill development. Over-reliance is the behavioral manifestation of excessive cognitive-offloading: when offloading becomes the default rather than a strategic choice. - [Pedagogical Agent](https://edtechdev.github.io/aied/concepts/pedagogical-agent/): **Synthesis**: Pedagogical agents are AI-driven conversational interfaces embedded in learning environments that use pedagogical strategies (eliciting, telling, scaffolding) to support learner engagement, reflection, and metacognition. Designs vary from simple information providers to interactive dialogue partners that adapt to learner states. - [Training Pedagogical LLMs for Tutoring](https://edtechdev.github.io/aied/concepts/pedagogical-llm-training/): Domain-specialized optimization can transform a mid-sized open-source model (Qwen3-32B) into a pedagogical domain expert that outperforms far larger proprietary systems — but only when training rewards *guiding* rather than *answering*.^singh-eduqwen-pedagogical-rl-2026 Classical instructional design theory (ADDIE, Dick & Carey) combined with modern ReAct reasoning achieves the highest performance - [Pedagogical Safety](https://edtechdev.github.io/aied/concepts/pedagogical-safety/): **Pedagogical safety** — the design principle that AI education systems must protect learners from harm, including inappropriate content, unsafe advice, biased treatment, and manipulative interaction patterns. Safety is particularly critical for K-12 contexts. - [Peer Review](https://edtechdev.github.io/aied/concepts/peer-review/): **Peer review** — the practice in which students read, evaluate, and provide feedback on one another's work, most often writing. In writing pedagogy, peer review is a long-standing best practice: students learn both from receiving feedback and from providing criteria-based feedback to others, and interactions with peers about their writing correlate with deeper learning, audience awareness, and pe - [Personalized Learning](https://edtechdev.github.io/aied/concepts/personalized-learning/): Tailoring educational experiences to individual learner profiles, including prior knowledge, learning pace, preferences, and affective states. AI enables personalization at scale, though the gap between *system personalization* and *learner-perceived personalization* remains an open measurement challenge. - [Physics Education](https://edtechdev.github.io/aied/concepts/physics-education/): **Physics Education** — the study of how students learn physics and how to teach it more effectively, spanning Socratic AI tutoring, computational thinking assessment, student trust and AI adoption patterns, automated scoring validity, and teacher preparation. The physics education articles in this wiki are notable for their domain-specificity: they explore how AI tools interact with the unique co - [AI Plagiarism Detection](https://edtechdev.github.io/aied/concepts/plagiarism-detection/): Technologies and methods for detecting AI-generated content in academic submissions, including classifier-based approaches, watermarking, and stylistic analysis. The effectiveness and reliability of these tools remain contested, particularly as LLM outputs become more sophisticated. - [Privacy in AI Education](https://edtechdev.github.io/aied/concepts/privacy/): **Privacy** — the protection of student data, identity, and autonomy in AI-augmented learning environments. Privacy concerns intensify as AI systems collect increasingly granular behavioral data for personalization and analytics. - [Professional Training and AI](https://edtechdev.github.io/aied/concepts/professional-training/): **Professional training** — the use of AI for workforce development, corporate learning, and professional skill acquisition. Professional training extends AI in education beyond formal schooling into workplace and lifelong learning contexts. - [Programming Education](https://edtechdev.github.io/aied/concepts/programming-education/): **Programming education** — the teaching and learning of computer programming, from introductory block-based programming to advanced software development. In the AI era, programming education increasingly incorporates computational thinking, AI-assisted tools, and embodied approaches (such as robots) to help learners connect abstract code to meaningful, observable outcomes. It spans k-12 and highe - [Project-Based Learning](https://edtechdev.github.io/aied/concepts/project-based-learning/): **Project-based learning (PBL)** — an active, learner-centred pedagogy in which students learn by engaging in extended, real-world projects that require inquiry, problem solving, and the application of knowledge to produce tangible outcomes. PBL emphasizes student autonomy, collaboration, and authentic tasks, and is widely used with technology — including educational-robotics and AI — to give lear - [Prompt Engineering](https://edtechdev.github.io/aied/concepts/prompt-engineering/): **Prompt engineering** — the practice of designing and refining inputs to large language models to achieve desired outputs. In education, prompt engineering serves dual roles: as a learner skill (students must learn to prompt effectively) and as a system design lever (developers craft prompts that shape AI tutoring behavior). - [Psychometrically Aware AI](https://edtechdev.github.io/aied/concepts/psychometrically-aware-ai/): **Psychometrically aware AI** — models aligned with measurement theory — is the standard advanced in llm-psychometric-calibration-cdp, llm-item-difficulty-prediction, confidence-aware-ai-assessment, and item-response-theory: calibrated, uncertainty-aware AI assessment preserves validity and trust (educational-measurement, assessment-validity). - [RAG (Retrieval-Augmented Generation)](https://edtechdev.github.io/aied/concepts/rag/): **RAG (Retrieval-Augmented Generation)** — an AI architecture that combines information retrieval with text generation, allowing LLMs to ground responses in external knowledge sources rather than relying solely on training data. In education, RAG addresses hallucination, enables curriculum-grounded tutoring, and powers domain-specific AI tutors. - [RCT](https://edtechdev.github.io/aied/concepts/rct/): **RCT** — a key concept in AI in education research. Explored across 2 articles in this wiki. - [Reducing AI Misuse](https://edtechdev.github.io/aied/concepts/reducing-ai-misuse/): **Reducing AI misuse** — the design, pedagogical, and policy levers that prevent students from substituting generative AI for their own cognitive work and instead steer them toward ethical, productive use. Effective approaches are sorted by impact rather than popularity, and the strongest evidence favors **structural levers** — tool guardrails and assessment redesign — that change the environment - [AI Regulation in Education](https://edtechdev.github.io/aied/concepts/regulation/): **AI regulation** — the laws, policies, and governance frameworks that control how AI is developed and deployed in educational settings. Regulation in the wiki spans government policy, institutional governance, and industry self-regulation. - [Reinforcement Learning](https://edtechdev.github.io/aied/concepts/reinforcement-learning/): **Reinforcement learning** trains AI tutors and agents through reward signals: special-r1-rl-special-education, singh-eduqwen-pedagogical-rl-2026, pedagogical-safety-rl, and ai-coaching-rl-skill-development align RL with pedagogical objectives, including safety and skill transfer (intelligent-tutoring, agentic-ai). - [Research Methods in AIED](https://edtechdev.github.io/aied/concepts/research-methods-aied/): **Research methods in AIED** — the set of empirical designs, data-collection strategies, and analytic techniques researchers use to study AI in education: whether and how AI tools support (or harm) learning, and under what conditions. The wiki's corpus spans experimental, survey, qualitative, design-based, computational-benchmark, and review methods. Each has distinct strengths and limitations, an - [Scaffolding](https://edtechdev.github.io/aied/concepts/scaffolding/): **Scaffolding** — structured support that helps learners accomplish tasks they cannot yet complete independently, with support fading as competence grows. In AI in education, scaffolding is the primary design principle for ensuring AI tools support learning rather than replace it. - [Self-Determination Theory](https://edtechdev.github.io/aied/concepts/self-determination-theory/): **Self-Determination Theory (SDT)** — a psychological theory of human motivation positing that intrinsic motivation and well-being depend on satisfying three basic psychological needs: autonomy, competence, and relatedness. In AI in education, SDT provides a framework for designing AI tools and professional development that support rather than undermine learners' and teachers' motivation. - [Self-Efficacy](https://edtechdev.github.io/aied/concepts/self-efficacy/): **Self-efficacy** — a learner's belief in their capability to successfully perform a task or achieve a goal. Drawing on social cognitive theory (Bandura), self-efficacy shapes motivation, effort, persistence, and learning engagement. In AI in education, self-efficacy matters in two ways: AI tools can build learners' confidence and autonomy (e.g., by providing feedback and scaffolding), and learner - [Self-Regulated Learning](https://edtechdev.github.io/aied/concepts/self-regulated-learning/): Self-regulated learning (SRL) describes learners as active participants who can shape and develop their cognitive and behavioral actions in a successful way. AI tools can either scaffold SRL development or inadvertently short-circuit it by removing the regulatory demands that build expertise.^scheu-mobile-chatbot-journaling-motivation-2026^stanford-evidence-base-ai-k12-2026 - [Simulating Students](https://edtechdev.github.io/aied/concepts/simulating-students/): **Simulating students** — using LLM-based agents to model learner behavior, cognition, and social dynamics for educational research, design, and training. Simulated students let researchers evaluate pedagogical approaches, model diverse learner profiles, test educational AI before deployment, and train teachers — tasks that are difficult, slow, or ethically constrained to do systematically with re - [Simulation](https://edtechdev.github.io/aied/concepts/simulation/): **Simulation** — the use of modeled environments, agents, or scenarios to support learning through practice and feedback in contexts that are safe, repeatable, and often otherwise inaccessible. Simulations let learners act, make errors, and see consequences without real-world cost, and are increasingly powered by AI and agent-based modeling. - [Social-Emotional Learning](https://edtechdev.github.io/aied/concepts/social-emotional-learning/): **Social-emotional learning (SEL)** — the process of developing the competencies that enable individuals to synchronize thoughts, emotions, and actions to foster positive interactions with oneself and others: self-awareness, self-management, social awareness, relationship skills, and responsible decision-making (the CASEL framework). In AI in education, SEL is increasingly recognized as critical b - [Social Robots](https://edtechdev.github.io/aied/concepts/social-robots/): **Social robots** — robots designed to engage people through social interaction, using human-like cues such as speech, gesture, facial expression, and personality to communicate, teach, assist, or accompany. In education, social robots (humanoids like iCub, Pepper, Reachy, and companion robots) are used for tutoring, storytelling, role-play, language support, and as study companions. Their social - [Socratic AI Dialogue](https://edtechdev.github.io/aied/concepts/socratic-ai-dialogue/): Socratic dialogue — asking structured questions rather than providing answers — is one of the strongest pedagogical scaffolds for deep learning. When automated via AI, it produces measurable reasoning gains but also requires careful calibration to avoid frustrating learners or displacing human mentorship.^hashmi-socratic-physics-chatbot-2025^favero-critical-ai-tutors-empower-enslave-2025 - [Socratic Method](https://edtechdev.github.io/aied/concepts/socratic-method/): **Socratic Method** — a pedagogical approach rooted in guided questioning and dialogue rather than direct instruction, now being adapted for generative AI tutoring systems. In AI in education, the Socratic method is operationalized through LLMs that ask probing questions, scaffold reasoning, and withhold direct answers — aiming to promote deeper understanding and productive struggle rather than an - [Special Education](https://edtechdev.github.io/aied/concepts/special-education/): **Special Education** — the design and delivery of instruction for learners with disabilities, spanning cognitive, physical, sensory, and neurodevelopmental differences. AI in education research in this wiki explores how AI tools can support diverse learner needs through personalization, adaptive scaffolding, and accessible interfaces — while also examining the risks of AI systems that overlook or - [STEM Education and AI](https://edtechdev.github.io/aied/concepts/stem-education/): **STEM Education** — science, technology, engineering, and mathematics education is the most common domain for AI in education research in the wiki. STEM's structured knowledge, clear right/wrong answers, and computational nature make it an ideal testbed for AI tutoring and assessment. - [Storytelling in Education](https://edtechdev.github.io/aied/concepts/storytelling-in-education/): **Storytelling in education** — the use of narrative as a pedagogical tool to engage learners, convey meaning, and support knowledge construction, creativity, and emotional connection. Storytelling is a natural and motivating way for learners to make sense of the world, and it is increasingly combined with technology — including AI and social-robots — to create interactive, adaptive narrative expe - [Student Engagement](https://edtechdev.github.io/aied/concepts/student-engagement/): **Student engagement** — the degree and quality of a learner's active involvement in the learning process, most often decomposed into behavioral, cognitive, and affective dimensions. In AI-education research, student engagement is both a key outcome (does an AI tool keep students engaged?) and a mechanism (does engagement mediate between AI design and learning?). It is conceptually distinct from t - [Student Experience with AI](https://edtechdev.github.io/aied/concepts/student-experience/): **Student experience with AI** — how learners perceive, interact with, and are affected by AI tools in educational settings. With over 85 articles in the wiki, student experience is one of the most-researched dimensions of AI in education. - [Student Misconceptions about AI](https://edtechdev.github.io/aied/concepts/student-misconceptions-ai/): **Student misconceptions about AI** — the inaccurate beliefs students hold about what AI systems are, what they do, and what using them means for learning, especially in academic contexts. Misconceptions are not a single falsehood but a family of calibration errors that cluster around two core mistakes: misjudging what the model is (authority vs. tool, neutral vs. biased, understanding vs. generat - [Student Modeling](https://edtechdev.github.io/aied/concepts/student-modeling/): **Student modeling** — the broad practice of representing learner characteristics including knowledge, skills, affective states, engagement, and preferences in computational form. Student modeling is the foundation upon which adaptive and personalized AI in education systems are built. - [Teacher AI Competency](https://edtechdev.github.io/aied/concepts/teacher-ai-competency/): Teacher AI competency encompasses the knowledge, skills, and dispositions required for effective AI integration in educational contexts. Emerging frameworks identify three competency dimensions: - [Teacher Role in AI-Enhanced Education](https://edtechdev.github.io/aied/concepts/teacher-role/): **Teacher role** — how AI reshapes the work, identity, and agency of educators. With 50+ articles examining this dimension, the wiki documents a fundamental transformation: from sole knowledge authority to orchestrator of human-AI learning environments. - [Transfer of Learning](https://edtechdev.github.io/aied/concepts/transfer-of-learning/): **Transfer of Learning** — the extent to which knowledge or skills acquired in one context (e.g., practice with an AI tool) persist and apply in a different context (e.g., independent performance without the tool). In AI in education, transfer is the central open question: whether performance gains students show *with* AI tools translate into durable learning they can demonstrate *without* them. - [Trust Calibration](https://edtechdev.github.io/aied/concepts/trust-calibration/): **Trust calibration** — the metacognitive capacity to align one's confidence in an AI system with its actual reliability in a given context, knowing when to trust and when to question its output. Trust calibration is the direct antidote to over-reliance: it is the skill of matching trust to evidence rather than to an AI's confident fluency. - [Trust in AI](https://edtechdev.github.io/aied/concepts/trust/): **Trust in AI** — the willingness of learners and educators to rely on AI systems for learning, judgment, and decision-making. Trust is a precondition for effective use of AI in education, but it is a double-edged sword: appropriate trust enables productive engagement, while over-trust leads to over-reliance and under-trust prevents beneficial use. Trust is shaped by the perceived competence, tran - [Universal Design for Learning](https://edtechdev.github.io/aied/concepts/universal-design-for-learning/): **Universal Design for Learning (UDL)** — an educational framework that designs instruction to be accessible and effective for the widest range of learners by proactively building in flexible means of engagement, representation, and action/expression, rather than retrofitting accommodations for individuals. - [Well-Being](https://edtechdev.github.io/aied/concepts/well-being/): **Well-being** — the positive state of being mentally, physically, and socially healthy, encompassing emotional, psychological, and social dimensions. In AI in education, well-being has become a central concern because the rapid integration of generative AI into learning environments can affect students' and educators' mental health, motivation, belonging, anxiety, and sense of agency — raising qu - [AI in Writing Education](https://edtechdev.github.io/aied/concepts/writing-education/): **AI in writing education** — the use of AI tools for writing instruction, assessment, feedback, and the study of how generative AI reshapes the writing process itself. Writing education is one of the most AI-affected domains, because LLMs excel at the very activities writing instruction centers on — text generation, revision, and evaluation. Research in this area spans automated scoring, AI feedb - [Zone Of Proximal Development](https://edtechdev.github.io/aied/concepts/zone-of-proximal-development/): **Zone Of Proximal Development** is a central concept in AI in education research, connected to 8 articles in this wiki.