🏷️ ai-education
118 pages tagged with ai-education(87 articles, 31 concepts)
📄 Examining the Impact of Generative AI on Student Motivation and Engagement: The Mediating Role of Autonomy-Support and Autonomous Motivation in Education
> **Synthesis:** Ahmed and Sultan (2026) investigated how perceived autonomy, competence, relatedness, expectancy, and value influence autonomy support for AI use, autonomous motivation, and ultimatel…
📄 INSIDE the Student's Mind: Jointly Modeling Latent Reasoning and Action in LLM Student Simulators
> **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. …
📄 Structural Silence: When AI Infrastructure Fails Speakers of Underrepresented Languages
> **Synthesis:** Roy & Roy (2026) argue that the **infrastructure of AI** — training corpora, tokenization, benchmarks, deployment architectures — systematically disadvantages speakers of underreprese…
🏷️ 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, th…
🏷️ AI 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 for…
🏷️ Research Methods in 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) …
🏷️ 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…
📄 A framework for characterising and capturing the quality of digital interactions and experiences in early childhood education
> **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 techn…
📄 The Absent Cognitive Baseline: Theorizing a Structural Gap in AI-Native College Students' Academic Self-Assessment
> **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 cogn…
📄 Using AI-Generated Feedback to Improve Critical Thinking and Writing Proficiency
> **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-mon…
📄 AI literacy alone is not enough: Student AI readiness and career adaptability in business and management education
> **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…
📄 Beyond MOOCs: How technical and structural factors shape learner engagement, retention and inclusivity across online learning platforms
> **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 pl…
📄 Coauthorship integrity: Reconceptualising assessment validity for the age of generative artificial intelligence
> **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 concept…
2026-08-10 · generative-ai, assessment, conversational-agents, assessment-validity, academic-integrity
📄 Curriculum as Code: An AI-Assisted Architecture for Instructional Design in STEM Education
> **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…
📄 Effects of AI chatbot-supported cooperative flipped classroom on student collaboration, self-regulated learning and academic performance: A mastery learning perspective
> **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' collaborati…
2026-08-10 · self-regulated-learning, collaborative-learning, chatbot, epistemic-agency, ai-tutoring
📄 Enhancing creative writing with robot-LLM integration: The interplay of embodiment, AI creativity and user engagement
> **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 undergradua…
📄 Enhancing online learning outcomes through virtual companion AI: The role of identity anthropomorphism
> **Synthesis:** Grounded in social presence theory, this study introduces the concept of identity anthropomorphism and adopts multimodal learning analytics (MMLA) combining questionnaires, EEG and ey…
📄 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 bet…
📄 From emotion regulation to academic success: A self-determination theory-based emotional agent-mediated approach
> **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, imp…
📄 Generative AI-enhanced learning experiences for computational thinking: A systematic scoping review and design guidelines
> **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…
📄 Generative AI interactive textbook in electrotechnics: A four-year comparative study on student performance and inclusion
> **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 …
📄 Human-Centric Artificial Intelligence Pedagogy (HCAP) framework developed from TPACK through integration of artificial intelligence literacy and competency
> **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. …
2026-08-10 · ai-literacy, teacher-ai-competency, pedagogical-llm-training, faculty-development, ethics
📄 Hybrid intelligence feedback systems in design thinking development: Stage-specific insights on pedagogical effects and characteristics of generative AI and instructors
> **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 experi…
📄 Learning-to-learn in the age of generative AI: A scoping review and conceptual framework
> **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 pr…
2026-08-10 · generative-ai, higher-ed, self-regulated-learning, language-learning, systematic-review
📄 LUDIA: A Design and Evidence Statement
> **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 r…
📄 Metacognitive AI literacy: going beyond the AI skills gap agenda
> **Synthesis:** Shapiro, Souto-Otero, and Watermeyer (2026) argue that conventional AI literacy frameworks anchored in functional skills acquisition fail to address the fundamental epistemological ch…
📄 Challenges for Musical Education in the Age of AI and Digital Transformation
> **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 ch…
📄 Not a universal benefit: Examining the differential effects of emotional AI on L2 pre-service teachers' language learning
> **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 qua…
2026-08-10 · affective-computing, language-learning, ai-tutoring, educational-technology, edtech-platform
📄 Not all collaboration benefits from competition: Collaboration modes in a computational thinking game
> **Synthesis:** This study investigated different collaboration modes and how they interact with competition to influence computational thinking learning, group metacognition and in-game behaviours. …
📄 "Polished Artifacts, Fragile Engagement? Tackling the Challenge of Reduced Epistemic Effort in Human-AI Knowledge Construction"
> **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 s…
📄 The (im)possibility of AI literacy
> **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 ma…
📄 Exploring interfaces and implications for integrating social-emotional competencies into AI literacy for education: a narrative review
> **Synthesis:** Palmquist, Sigurdardottir, and Myhre (2025) conduct a narrative literature review examining the intersection of AI literacy and social-emotional competencies (SEC) in education, propo…
📄 A Bottom-Up Taxonomy of Student Discourse with a Socratic AI Physics Tutor
> **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 abs…
📄 Teaching Intro AI When the Tools Can Do the Homework: A Course Redesign and a Student Bill of Rights
> **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 red…
📄 The Scaffolded AI literacy (SAIL) framework: Results of a Delphi study for equitable AI literacy framework design in education
> **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 f…
📄 The synergy of pedagogical agents and metaphorical design: Reducing psychological distance to enhance video learning
> **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 2x…
📄 Unveiling patterns of socially shared regulation in relation to self-regulated learning: The roles of individual profiles and group dynamics in online collaborative learning
> **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…
📄 Will, Skill, Not Tool: Chinese university students' acceptance of generative AI for academic writing in informal English medium instruction settings
> **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…
🏷️ 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' m…
🏷️ 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, com…
📄 Adversarial Stress Testing of Role-Playing Language Agents using Multi-Agent Evaluation
> **Synthesis:** This paper presents a modular multi-agent platform for adversarially stress-testing [[agentic-ai|role-playing language agents]] through structured multi-turn dialogue. With three coor…
📄 Perceptions And Acceptance of Artificial Intelligence in Science Education Programmes: Voices of Pre-Service Science Teachers
> **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…
📄 Chat Debugging: An Exploratory Study of Human-AI Collaboration to Debug Analog Circuits
> **Synthesis:** This exploratory study investigates how undergraduates use [[llm|LLMs]] to debug malfunctioning analog circuits under exam conditions, identifying both promising [[human-ai-collaborat…
📄 Once a Response, Always a Response: Detecting LLM-generated Text via Latent Prompt Restoration
> **Synthesis:** EchoPrompt introduces a training-free zero-shot detector for [[plagiarism-detection|LLM-generated text]] that exploits the latent prompt dependency inherent in machine-generated conte…
📄 Does generative AI narrow education-based productivity gaps? Evidence from a randomized experiment
> **Synthesis:** In a [[rct|randomized controlled trial]] with 1,174 participants, Cruces et al. find that [[generative-ai|generative AI]] substantially narrows education-based productivity gaps, clos…
📄 Policy Fragmentation or Institutional Alignment? Institutional Governance of AI in Universities and Business Schools
> **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 …
📄 Evidence-Grounded Multimodal Knowledge Graph Construction for Multi-Lecture Educational Reasoning
> **Synthesis:** This paper introduces an evidence-grounded multimodal pipeline that constructs provenance-rich [[knowledge-tracing|knowledge graphs]] from lecture videos by integrating speech transcr…
📄 ProPRL: Property-Aware Prerequisite Relation Learning in Educational Knowledge Graphs
> **Synthesis:** ProPRL advances [[adaptive-learning|prerequisite relation learning]] by going beyond conventional link prediction to adaptively integrate complementary educational evidence from conce…
📄 School network reorganization under educational and spatial constraints using classical and quantum optimization
> **Synthesis:** This paper develops an optimization framework for school network reorganization that integrates geographic, administrative, and educational criteria into an Integer Linear Programming…
📄 Navigating the skill diversity frontier: How skill complexity explains 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…
2026-08-09 · workforce-development, upskilling, professional-development, professional-training, llm
📄 TACT: Taxonomy-Aligned Post-Training for Pedagogically Adaptive English Tutoring
> **Synthesis:** TACT (Taxonomy-Aligned Conversational Tutor) presents a human-grounded framework for training and evaluating pedagogically adaptive ESL tutors powered by [[llm|LLMs]]. Built on a Tuto…
📄 The Role of Artificial Intelligence in Green Education: Optimizing Teacher Workflow and Enhancing Pedagogical Design under Sustainable Development Pedagogy (SDP) Constraints
> **Synthesis:** Talebzadeh (2026) conducts a quasi-experimental study with 28 pre-service teacher teams, finding that AI-assisted Sustainable Development Pedagogy constraints significantly improve in…
📄 HiLLM-CD: LLM-Enhanced Hierarchical Cognitive Diagnosis
> **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 concep…
🏷️ 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 educ…
🏷️ 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 le…
🏷️ Assessment
> **Assessment** is a central concept in AI in education research, connected to 8 articles in this wiki. …
🏷️ 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, c…
🏷️ 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 i…
🏷️ Design Thinking
> **Design Thinking** — a key concept in AI in education research. Explored across 1 articles in this wiki.…
🏷️ Edtech Platform
> **Edtech Platform** is a central concept in AI in education research, connected to 15 articles in this wiki. …
🏷️ Efficacy Study
> **Efficacy Study** — a key concept in AI in education research. Explored across 6 articles in this wiki.…
🏷️ Engagement Metrics
> **Engagement metrics** — the range of observable signals and measurement approaches researchers and systems use to operationalize [[student-engagement|student engagement]] in AI-supported learning: …
🏷️ AI in Higher Education
> **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, …
🏷️ 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 throu…
🏷️ 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. Kno…
🏷️ 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 sp…
🏷️ Multimodal
> **Multimodal** — a key concept in AI in education research. Explored across 3 articles in this wiki.…
🏷️ RCT
> **RCT** — a key concept in AI in education research. Explored across 2 articles in this wiki.…
🏷️ 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 S…
🏷️ 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 …
📄 Mapping the Emerging Curriculum for AI-Assisted Software Engineering via Syllabus Analysis
> **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 curri…
2026-08-07 · curriculum-design, software-engineering, instructional-design, higher-ed, generative-ai
📄 Learning to Use AI for Learning: Teaching Responsible Use of AI Chatbot to K-12 Students Through an AI Literacy Module
> **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. Depl…
📄 Guidelines for Designing AI Technologies to Support Adult Learning
> **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 desig…
🏷️ Adult Learning
> **Adult Learning** — a key concept in AI in education research. Explored across 1 articles in this wiki.…
📄 Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Framework
> **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 …
📄 The care-full craft of feedback in an age of generative AI
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 te…
📄 The Tragedy of the Cognitive Commons: How AI Could Disrupt the Regeneration of Professional Expertise
> **Nolan Lovett** — Human Resource Development Review (author accepted manuscript, 2026).…
📄 Feedback futures: beyond the limits of human and GenAI capacities
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 …
📄 Unanticipated Effects of Generative AI on Expertise Pathways and Performance Perception in System Administration
> **Rana Abou Khamis, Hala Assal, Ashraf Matrawy** — arXiv preprint (2026).…
2026-08-03 · generative-ai, professional-training, cognitive-offloading, over-reliance, lifelong-learning
📄 Patterns of Learner-AI Interaction and Academic Performance in an Object-Oriented Programming Course
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 …
📄 ISD Agent Benchmark
> **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 conte…
📄 Auditing Institutional Heterogeneity for Generative AI in Patient Education: A Large-Scale Study of 102 US 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 …
📄 Why SuaCode?": Understanding African Students' Motivations for Taking a Smartphone-Based Online Coding Course
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…
🏷️ 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 [[pr…
📄 A Longitudinal Analysis of Public Discourse on AI Ethics in Education Using Twitter Data
**Akriti Bagale, Nafisa Mehjabin, Ali Unlu, Aditya Johri, et al. (2026)** - George Mason University; University of Virginia. arXiv preprint. Bagale, A., Mehjabin, N., Unlu, A., Johri, A., et al. (2026…
📄 The Environmental Cost of LLMs in AIED: Reporting and Practices
> **Sabrina C. Eimler, Lukas Erle, Daniel Flood, Aditi Haiman, Luca Häckert, André Helgert, Lachlan McGinness, Büsra Yapici**…
📄 AI-Driven Analytics of Team-Teaching Talk: Acoustic Patterns across Experience, Cohorts and the Learning Design
**Yuchen Liu, Roberto Martinez-Maldonado, Riordan Alfredo, Paola Mejia-Domenzain, Dwi Rahayu, Sadia Nawaz** — AIED 2026 — cs.HC, cs.AI This paper presents an AI-based speech processing approach to ana…
📄 Fair and explainable educational recommendations with a hybrid Graph-GRU framework
> **Synthesis:** Fair and explainable educational recommendations with a hybrid Graph-GRU framework…
🏷️ DOT Framework Survey: Practitioner Beliefs and Behaviors in AI-Enhanced Education
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 [[…
📄 I can't read your mind": A Study of Neurodivergent Computing Students' Experiences with Collaborative Active Learning
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 struct…
📄 The efficiency-gain illusion: People underestimate the rate of AI use and overestimate its benefits on simple tasks
Across three pre-registered studies (N=2,691), this paper documents systematic miscalibration in how people perceive their own [[generative-ai|AI]] usage. The authors find that people not only use AI …
📄 Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build
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 …
📄 The Hidden Cost of Contextual Sycophancy: an AI Literacy Intervention in Human-AI Collaboration
LLM sycophancy creates a feedback loop where user errors propagate into AI advice, degrading outcomes; AI literacy training reduces but doesn't eliminate this contextual sycophantic dependence. This A…
📄 Computational Thinking Development in AI Agent Creation: A Mixed-Methods Study
> Computational Thinking Development in AI Agent Creation: A Mixed-Methods Study **Sun et al. (2026)** — Multiple institutions. arXiv cs.CY.…
📄 Little Impact of ChatGPT Availability on High School Student Test Score Performance
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 scho…
📄 Improving Hybrid Human-AI Tutoring by Differentiating Human Tutor Roles Based on Student Needs
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% standar…
📄 A Framework for Institutional Change in the Age of 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-…
📄 Pedagogical Promise and Peril of AI: A Text Mining Analysis of ChatGPT Research Discussions in Programming Education
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 mo…
📄 Preparing Students for AI-Powered Materials Discovery: A Workflow-Aligned Framework for AI Literacy, Equity, and Scientific Judgment
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…
📄 Cost-of-Ethics Crisis: Beliefs, Decisions, and Justifications in the Job Searches of Computer Science Students in Canada and the United States
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 con…
📄 When Should Teachers Control AI Generation for Mathematics 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 instructio…
📄 How State Policy Can Help Teachers Use AI Well
> 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 microm…
📄 NSMQ Riddles: A Benchmark of Scientific and Mathematical Riddles for Quizzing Large Language Models
> 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 f…
📄 Agentic Workflows in 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. (202…
2026-05-07 · agentic-ai, benchmark, intelligent-tutoring, pedagogical-llm-training, human-in-the-loop-ai
📄 Authentic Assessment
> Wiggins (1990) proposed AA as a counterbalance to standardised tests: direct examination of "student performance on worthy intellectual tasks." > Authentic assessment (AA) has evolved from workplace…
📄 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 …
📄 LLM-Based Educational Simulation: Evaluating Temporal Student Persona Stability Across ADHD Profiles
> 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 cri…
📄 Multimodal Learning with Generative AI
> The guide adopts a middle way between "techno-fixing" and rejecting AI as an existential threat. It argues that: > A comprehensive educator's guide to integrating Generative AI into multimodal teach…
📄 Principled AI in Education
> The framework rests on three interconnected anchors that must be addressed *before* selecting tools: > Rejecting the binary promise-vs-peril discourse and the rush to immediate implementation, Finke…
🏷️ 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 underst…
🏷️ Formative Assessment in AI Education
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 var…
🏷️ Human-in-the-Loop AI for Education
Educational AI systems that strategically interleave automated generation with human expert judgment, preserving pedagogical quality while scaling production. Two recent implementations illustrate dis…
🏷️ Training Pedagogical LLMs for Tutoring
> 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 rewa…
🏷️ 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 betwe…
📄 Towards Self-Referential Analytic Assessment: A Profile-Based Approach to L2 Writing Evaluation with LLMs
> 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 ot…
📄 Robotics and Artificial Intelligence in Education: Transformations, Challenges, and Future Directions
> **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 require…
📄 Review of Artificial Intelligence in Education from 2020 to 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 laye…
📄 Thinking Through AI: Advancing Cognitive and Collaborative Research for AI in Education
> **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 stude…