🏷️ self-regulated-learning

> **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-educat…

2026-08-13 · language-learning, generative-ai, motivation, higher-ed, teacher-role

> **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 mediati…

2026-08-13 · generative-ai, over-reliance, ai-misuse-learning-harm, higher-ed, motivation

> **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. Studen…

2026-08-13 · feedback-loop, formative-assessment, learning-analytics, higher-ed, student-experience

> **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…

2026-08-13 · critical-thinking, cognitive-load-theory, generative-ai, higher-ed, ai-literacy

> **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…

2026-08-13 · generative-ai, feedback-loop, metacognition, music-education, human-ai-collaboration

> **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 lite…

2026-08-13 · generative-ai, over-reliance, ai-literacy, cognitive-offloading, ai-misuse-learning-harm

> **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 th…

2026-08-13 · language-learning, generative-ai, ai-feedback-quality, motivation, teacher-role

> **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…

2026-08-13 · student-experience, motivation, higher-ed, generative-ai, ai-education

> **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/vi…

2026-08-12 · personalized-learning, language-learning, feedback-loop, multimodal, student-modeling

> **Synthesis:** HAIML is a human-centered framework for learning in AI-supported environments that preserves human agency, metacognitive awareness, ethical reasoning, and personal responsibility. Gro…

2026-08-12 · metacognition, human-in-the-loop, ai-literacy, cognitive-offloading, over-reliance

> **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 under…

2026-08-12 · metacognition, cognitive-offloading, over-reliance, academic-integrity, student-experience

> **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 de…

2026-08-12 · over-reliance, cognitive-offloading, academic-integrity, assessment, motivation

> **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, producti…

2026-08-12 · ai-literacy, academic-integrity, assessment, scaffolding, metacognition

> **Synthesis:** Unravelling undergraduates' development of evaluative judgments through AI-supported internal feedback…

2026-08-11 · generative-ai, feedback-loop, higher-ed, writing-instruction, assessment-literacy

> **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…

2026-08-10 · metacognition, generative-ai, cognitive-offloading, student-experience, higher-ed

> **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 · collaborative-learning, chatbot, epistemic-agency, ai-education, ai-tutoring

> **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, language-learning, systematic-review, epistemic-agency

> **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…

2026-08-10 · higher-ed, collaborative-learning, ai-education, ai-tutoring, educational-technology

> **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 …

2026-08-09 · formative-assessment, ai-feedback-quality, automated-grading, scaffolding, ai-tutoring

> **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) …

2026-08-08 · learning-analytics, metacognition, llm, higher-ed, engagement-metrics

> **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 argume…

2026-08-07 · generative-ai, feedback-design, higher-ed, scientific-argumentation, peer-feedback

> **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 …

2026-08-07 · metacognition, generative-ai, critical-thinking, cognitive-offloading, human-in-the-loop

> 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. …

2026-08-03 · authentic-assessment, ai-detection, academic-integrity, assessment, generative-ai

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 jou…

2026-07-29 · generative-ai, higher-ed, student-experience, engagement-metrics, efficacy-study

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 …

2026-07-28 · assessment, metacognition, higher-ed

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 …

2026-07-23 · generative-ai, higher-ed, efficacy-study, student-experience, scaffolding

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-L…

2026-07-22 · llm, generative-ai, ai-literacy, over-reliance, student-experience

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 cogniti…

2026-07-19 · cognitive-offloading, over-reliance, generative-ai, metacognition, learning-gains

> 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 …

2026-07-14 · active-learning, higher-ed, reinforcement-learning, stem-education, scaffolding

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 engag…

2026-07-10 · llm, higher-ed, student-experience, learning-analytics, personalized-learning

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 le…

2026-07-10 · llm, generative-ai, scaffolding, higher-ed, feedback-loop

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 narr…

2026-07-10 · ai-literacy, student-experience, academic-integrity, higher-ed, generative-ai

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 …

2026-07-09 · over-reliance, student-experience, metacognition, teacher-role, formative-assessment

As AI coding agents take over substantial implementation work, developers increasingly lose the informal, effortful problem-solving through which software engineering expertise historically accumulate…

2026-07-08 · agentic-ai, cs-education, generative-ai, higher-ed, llm

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 ans…

2026-06-30 · ai-literacy, k-12, metacognition, stem-education, student-experience

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-ga…

2026-06-25 · learning-analytics, student-modeling, higher-ed, engagement-metrics, efficacy-study

Ganganath et al. (2026) introduce CURIOBOT, a framework that operationalizes Berlyne's four collative variables (novelty, complexity, conflict, uncertainty) as adaptive linguistic interventions in con…

2026-06-23 · llm, intelligent-tutoring, metacognition, scaffolding, active-learning

> **Xinfei Cen, Vincent Aleven, Kenneth R. Koedinger, Conrad Borchers, Paulo F. Carvalho** (2026). EC-TEL 2026…

2026-06-17 · intelligent-tutoring, personalized-learning, efficacy-study, higher-ed, student-experience

**Li & Zheng (2026)**. Li & Zheng argue that the four dominant learning theories — behaviorism, cognitivism, constructivism, and connectivism — show significant conceptual limitations as [[generative-…

2026-06-12 · generative-ai, llm, personalized-learning, scaffolding, higher-ed

> **Shravika Mittal, Su Lin Blodgett, Q. Vera Liao**…

2026-06-11 · generative-ai, llm, over-reliance, student-experience, metacognition

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 s…

2026-06-09 · llm, k-12, metacognition, student-experience, scaffolding

📄 DOI: 10.32473/flairs.39.1.141554 Codify (also called AI Tutor) is an [[intelligent-tutoring]] system that leverages [[llm|LLMs]], competency tracking, and adaptive assessment to provide Socratic, d…

2026-05-26 · intelligent-tutoring, llm, stem-education, higher-ed, adaptive-learning

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 activel…

2026-05-26 · llm, metacognition, student-experience, efficacy-study, over-reliance

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 …

2026-05-26 · metacognition, professional-training, adaptive-learning, lifelong-learning, scaffolding

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 technostre…

2026-05-25 · student-experience, over-reliance, ai-literacy, higher-ed, affective-computing

This paper presents a rigorous empirical comparison between [[llm|LLM]]-based and semantic similarity methods for [[automated-grading|automated assessment]] of student self-explanations in programming…

2026-05-23 · llm, automated-grading, feedback-loop, stem-education, higher-ed

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 wor…

2026-05-21 · intelligent-tutoring, teacher-role, student-experience, k-12, human-in-the-loop

Socratic questioning, reflection prompts, misconception checks, and mandatory pauses produce better K-12 engagement than directive answer-giving AI tutors. SocratiCode demonstrates a participatory des…

2026-05-19 · intelligent-tutoring, llm, generative-ai, k-12, scaffolding

This *Nature Reviews Psychology* piece draws a critical distinction that has been under-theorized in AIED research: The authors argue that generative AI easily boosts performance but often bypasses th…

2026-05-14 · generative-ai, metacognition, over-reliance, higher-ed, scaffolding

**Sequenced AI feedback harms learning despite boosting engagement and positive perceptions.** In a randomized experiment with 199 participants, the authors compared two types of AI-generated feedback…

2026-05-11 · feedback-loop, formative-assessment, scaffolding, generative-ai, efficacy-study

> 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 m…

2026-05-10 · generative-ai, higher-ed, scaffolding, faculty-development-genai, metacognition

> 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 a…

2026-05-09 · llm, personalized-learning, adaptive-learning, metacognition, student-experience

>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…

2026-05-08 · ai-literacy, assessment, assessment-validity, k-12, faculty-development

> 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 prof…

2026-05-08 · k-12, personalized-learning, intelligent-tutoring, llm, student-experience

> **EduGage** (Leng et al., 2026) addresses a core challenge: in online/video-based learning, **learners must self-regulate** their engagement with instructional materials. > Sensor-based momentary as…

2026-05-08 · adaptive-learning, learning-analytics, affective-computing, higher-ed, feedback-loop

> Conventional LLM safety benchmarks focus on toxic outputs, jailbreaks, and bias. In education, the primary risks are quieter: > "Solving problems correctly and avoiding toxic language does not make …

2026-05-07 · pedagogical-safety, intelligent-tutoring, adaptive-learning, k-12, higher-ed

> 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…

2026-05-07 · ai-ed-evaluation, ai-education, assessment, formative-assessment, higher-ed

> Three system properties enable the fallacy via two cognitive mediators: > The LLM fallacy is a **cognitive attribution error** in which users misinterpret LLM-assisted outputs as evidence of their o…

2026-05-07 · metacognition, over-reliance, llm, k-12, higher-ed

> Current AI tutoring systems treat each session as independent. Adaptive systems use real-time knowledge tracing (e.g., [[knowledge-tracing-irt|IRT-based models]]) but rarely retain a longitudinal st…

2026-05-07 · llm, personalized-learning, adaptive-learning, intelligent-tutoring, 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…

2026-05-07 · ai-education, higher-ed, generative-ai, multimodal, active-learning

> 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…

2026-05-07 · ai-education, higher-ed, pedagogy, scaffolding, policy-maker

> 1. **Desirable difficulties** — General-purpose AI removes productive struggle; tutoring tools preserve it via graduated hints. 2. **Germane load** — Effective learning requires processing that feel…

2026-05-07 · intelligent-tutoring, llm, generative-ai, personalized-learning, scaffolding

> 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 suppre…

2026-05-07 · metacognition, formative-assessment, k-12, higher-ed, scaffolding

> 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 develop…

2026-05-07 · metacognition, scaffolding, k-12, higher-ed, formative-assessment