https://edtechdev.github.io/aied/ Recently added articles on AI in education en-us Tue, 04 Aug 2026 15:26:25 +0000 https://edtechdev.github.io/aied/pages/eduzone-llm-safety-k12.html https://edtechdev.github.io/aied/pages/eduzone-llm-safety-k12.html Tue, 04 Aug 2026 00:00:00 +0000 **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.**

]]> https://edtechdev.github.io/aied/pages/egai-power-systems-education.html https://edtechdev.github.io/aied/pages/egai-power-systems-education.html Tue, 04 Aug 2026 00:00:00 +0000 **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.**

]]> https://edtechdev.github.io/aied/pages/gpt4o-mini-music-analysis-scoring.html https://edtechdev.github.io/aied/pages/gpt4o-mini-music-analysis-scoring.html Tue, 04 Aug 2026 00:00:00 +0000 **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.**

]]> https://edtechdev.github.io/aied/pages/rail-ed-genai-literacy-teacher-education.html https://edtechdev.github.io/aied/pages/rail-ed-genai-literacy-teacher-education.html Tue, 04 Aug 2026 00:00:00 +0000 **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.**

]]> https://edtechdev.github.io/aied/pages/access-not-enough-ai-tutoring-2026.html https://edtechdev.github.io/aied/pages/access-not-enough-ai-tutoring-2026.html Mon, 03 Aug 2026 00:00:00 +0000 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 the level needed for reading gains, and achievement did not improve.

]]> https://edtechdev.github.io/aied/pages/agency-gap-ai-writing.html https://edtechdev.github.io/aied/pages/agency-gap-ai-writing.html Mon, 03 Aug 2026 00:00:00 +0000 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 prompted, n = 39) or a **proactive agent** (initiates sequenced questions and feedback, n = 40). GenAI literacy was measured with the validated 20-item **GLAT**. The study introduces the **agency gap**: a relational mismatch between the initiative an AI agent demands and the learner's capacity to initiate, monitor, evaluate, and internalise AI-supported reasoning — neither an individual deficit nor a fixed property of the system.^[raw/papers/caeai-2026-agency-gap-ai-writing.md]

]]> https://edtechdev.github.io/aied/pages/agentic-ai-pedagogical-best-practice-2026.html https://edtechdev.github.io/aied/pages/agentic-ai-pedagogical-best-practice-2026.html Mon, 03 Aug 2026 00:00:00 +0000 Woollaston, Flanagan, Wijerathne & Ogata (2026, AIED HAI-Agency Workshop) review six established pedagogical principles through the lens of **proactive agentic AI** and articulate the central tension: the more an agent automates, the less cognitive work the learner does. Their design response — **intentional friction, dynamic scaffolding, human-in-the-loop oversight, and considered AI utilisation** — is a principled guardrail for the wiki's agentic-education literature.

]]> https://edtechdev.github.io/aied/pages/agreement-not-quality-llm-coding-verification.html https://edtechdev.github.io/aied/pages/agreement-not-quality-llm-coding-verification.html Mon, 03 Aug 2026 00:00:00 +0000 An independent domain expert judged 855 pairwise code-set comparisons blind to source, treating human and machine outputs symmetrically. Human-LLM agreement (mean Jaccard 0.30) fell well below human-human agreement (0.52), yet the blind verifier preferred human and LLM coding at indistinguishable rates (51.5% vs 48.5%, p = 0.537).

]]> https://edtechdev.github.io/aied/pages/ai-writing-support-stage-ownership-2026.html https://edtechdev.github.io/aied/pages/ai-writing-support-stage-ownership-2026.html Mon, 03 Aug 2026 00:00:00 +0000 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 **quality–ownership trade-off**.

]]> https://edtechdev.github.io/aied/pages/authentic-products-authenticated-processes-2026.html https://edtechdev.github.io/aied/pages/authentic-products-authenticated-processes-2026.html Mon, 03 Aug 2026 00:00:00 +0000 Tsiligkiris (2026) reframes authentic assessment as an *evidential and validity-oriented design problem* in AI-rich higher education. His central distinction — **authentic products vs. authenticated processes** — argues that assessment validity under generative AI depends not on realistic outputs alone but on architectures that make human judgement, verification, and responsibility visible. A systematic conceptual review of 37 sources yields a six-dimension framework for redesigning assessment briefs at module and programme level.

]]> https://edtechdev.github.io/aied/pages/beyond-detection-authentic-assessment-ai-2025.html https://edtechdev.github.io/aied/pages/beyond-detection-authentic-assessment-ai-2025.html Mon, 03 Aug 2026 00:00:00 +0000 Kickbusch, Ashford-Rowe, Kemp, Boreland & Huijser (2025) argue the dominant institutional response to generative AI in assessment — surveillance and AI detection — **misdiagnoses the problem**: in an AI-mediated world, authenticity cannot be policed into existence; it must be redesigned. They reconceptualise authenticity as constructed where AI is expected, declared, and scrutinised, and offer discipline-agnostic "design for learning" patterns that position AI as a collaborator rather than a cheating application.

]]> https://edtechdev.github.io/aied/pages/care-full-feedback-genai.html https://edtechdev.github.io/aied/pages/care-full-feedback-genai.html Mon, 03 Aug 2026 00:00:00 +0000 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 landscape: (1) feedback processes should support **meaning-making**, (2) build **educative relationships**, (3) be **trustworthy**, and (4) be respected as a **professional craft**.^[raw/papers/tandf-2026-care-full-feedback-genai.md]

]]> https://edtechdev.github.io/aied/pages/chatgpt-feedback-engagement-genai.html https://edtechdev.github.io/aied/pages/chatgpt-feedback-engagement-genai.html Mon, 03 Aug 2026 00:00:00 +0000 1. **Prompt engineering** — the quality of GenAI feedback is largely determined by prompt clarity (e.g. the CLEAR framework; Lo 2023). 2. **Evaluative judgement** — discerning useful feedback from plausible-but-unreliable output. 3. **Emotional reflexivity** — balancing trust and doubt by understanding GenAI's capabilities and limits (Bearman & Ajjawi 2023). 4. **Ethical decision-making** — deciding how, when, and why to use GenAI feedback so work remains authentic (academic integrity). 5. **Metacognitive skills** — setting feedback goals, planning prompts, self-monitoring interactions, and reflecting on the whole process.^[raw/papers/tandf-2026-chatgpt-feedback-engagement.md]

]]> https://edtechdev.github.io/aied/pages/cognitive-commons-ai-expertise-regeneration.html https://edtechdev.github.io/aied/pages/cognitive-commons-ai-expertise-regeneration.html Mon, 03 Aug 2026 00:00:00 +0000 The paper reframes expertise development as collective stewardship rather than organizational optimization — a systems-level complement to individual-level cognitive-offloading and skill-decay findings, with implications for professional training and AI governance.

]]> https://edtechdev.github.io/aied/pages/connected-ai-lesson-planning-vietnam.html https://edtechdev.github.io/aied/pages/connected-ai-lesson-planning-vietnam.html Mon, 03 Aug 2026 00:00:00 +0000 ConnectED is a human-centered AI system supporting the full instructional lifecycle in Vietnamese education: curriculum-aligned lesson design, interactive student learning, and feedback-driven refinement, built on VietEduQwen, a Vietnamese educational LLM trained with SFT and DPO.

]]> https://edtechdev.github.io/aied/pages/cotal-formative-assessment-scoring-2026.html https://edtechdev.github.io/aied/pages/cotal-formative-assessment-scoring-2026.html Mon, 03 Aug 2026 00:00:00 +0000 Cohn, Ashwin T S, Mohammed & Biswas (2026) introduce **CoTAL** (Chain-of-Thought Prompting + Active Learning): an LLM grading pipeline that couples Evidence-Centered Design with human-in-the-loop prompt engineering and iterative teacher/student feedback refinement. It improves GPT-4's scoring by **up to 38.9% over a non-prompt-engineered baseline** and generalises across science, computing, and engineering — direct evidence that prompt-engineering quality, not model choice, is often the binding constraint in automated-grading.

]]> https://edtechdev.github.io/aied/pages/critical-genai-use-predictors.html https://edtechdev.github.io/aied/pages/critical-genai-use-predictors.html Mon, 03 Aug 2026 00:00:00 +0000 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* (viewing intellectual engagement as worthwhile). Both factors are framed as components of AI literacy and both are trainable, motivating intervention recommendations.^[raw/papers/mdpi-2026-critical-genai-use-predictors.md]

]]> https://edtechdev.github.io/aied/pages/feedback-futures-genai.html https://edtechdev.github.io/aied/pages/feedback-futures-genai.html Mon, 03 Aug 2026 00:00:00 +0000 1. **Usefulness, trust, and uptake.** GenAI is valued for speed, clarity, and accessibility, while teacher feedback is trusted for contextual understanding, disciplinary expertise, accountability, and human connection. Uptake depends on experienced care, recognition, and presence — relational conditions GenAI struggles to reproduce.^[raw/papers/tandf-2026-feedback-futures-genai.md] 2. **Immediate task achievement vs longer-term learning.** GenAI excels at helping students complete the task at hand but may orient them toward performance/avoidance goals rather than mastery — echoed in chatgpt-feedback-engagement-genai's finding of weak metacognitive engagement and one-off interactions.^[raw/papers/tandf-2026-feedback-futures-genai.md] 3. **Agency vs dependency.** Agency can be extended via iterative prompting and comparison, but may become "thinner" when students stay active at the level of interaction while ceding evaluative work to the system.^[raw/papers/tandf-2026-feedback-futures-genai.md] 4. **Access, avoidance, and advantage.** GenAI is unlikely to benefit all students equally; non-users cite trustworthiness, preference for human feedback, and academic integrity. Access alone does not guarantee educative uptake.^[raw/papers/tandf-2026-feedback-futures-genai.md] 5. **Teacher judgement and labour redistribution.** GenAI redistributes rather than removes teacher labour — teachers still assess accuracy, tone, relationality, and pedagogical value, and poorly designed tools can *increase* workload (see learner-centered-feedback-ai).^[raw/papers/tandf-2026-feedback-futures-genai.md]

]]> https://edtechdev.github.io/aied/pages/genai-can-harm-teaching-rct-2026.html https://edtechdev.github.io/aied/pages/genai-can-harm-teaching-rct-2026.html Mon, 03 Aug 2026 00:00:00 +0000 Sungu, Lira & Duckworth (2026) ran one of the first large-scale RCTs of a teacher-facing generative AI tool and found it can *harm* students: providing teachers an AI teaching assistant **reduced student intrinsic motivation by 0.11 SD** and — among lower-performing teachers — **cut student achievement by 0.13 SD**. The pattern is a **principal–agent problem**: teachers (agents) gain labor savings from AI delegation while students (principals) bear the cost of displaced relational teaching and scaffolding.

]]> https://edtechdev.github.io/aied/pages/genai-expertise-pathways-sysadmin.html https://edtechdev.github.io/aied/pages/genai-expertise-pathways-sysadmin.html Mon, 03 Aug 2026 00:00:00 +0000 Two unanticipated socio-technical findings: 'compression of traditional expertise pathways' — GenAI acts as both mentor-like tutor and 'ladder-shortening' tool, accelerating unfamiliar-domain task performance while reducing exposure to the foundational build-fail-debug cycles that historically built expertise.

]]> https://edtechdev.github.io/aied/pages/genai-teacher-feedback-comparison.html https://edtechdev.github.io/aied/pages/genai-teacher-feedback-comparison.html Mon, 03 Aug 2026 00:00:00 +0000 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 coded instances, 48 codes). Core conclusion: **GenAI and teacher feedback serve different needs — complementary but not interchangeable**.^[raw/papers/tandf-2026-genai-teacher-feedback-comparison.md]

]]> https://edtechdev.github.io/aied/pages/human-llm-collaborative-coding-k12-educator-ai.html https://edtechdev.github.io/aied/pages/human-llm-collaborative-coding-k12-educator-ai.html Mon, 03 Aug 2026 00:00:00 +0000 A multi-phase human-LLM collaborative pipeline adapted open, axial, and selective coding to build a hierarchical codebook from 45,000 messages exchanged between K-12 educators and a generative AI platform — an instance of LLMs as analytic assistants at a scale manual coding cannot match.

]]> https://edtechdev.github.io/aied/pages/hypergamification-game-engine-lms.html https://edtechdev.github.io/aied/pages/hypergamification-game-engine-lms.html Mon, 03 Aug 2026 00:00:00 +0000 The paper proposes 'hypergamification': using a comprehensive game environment generated from LMS content rather than bolting isolated game design elements onto a course. The key architectural idea is bidirectional integration — the game world is built from the LMS's actual learning content, and player activity flows back into the LMS.

]]> https://edtechdev.github.io/aied/pages/icap-cognitive-engagement-llm-agents.html https://edtechdev.github.io/aied/pages/icap-cognitive-engagement-llm-agents.html Mon, 03 Aug 2026 00:00:00 +0000 This study applies an extended 7-point ICAP framework (Interactive, Constructive, Active, Passive) to characterize cognitive engagement in collaborative dialogue, comparing trained human annotators with LLM-based labeling: in-context learning (ICL), zero-shot prompting, and self-reflective agents.

]]> https://edtechdev.github.io/aied/pages/learner-centered-feedback-ai.html https://edtechdev.github.io/aied/pages/learner-centered-feedback-ai.html Mon, 03 Aug 2026 00:00:00 +0000 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 presentation, then used the tool, then were interviewed. The study answers two questions: *how teachers interact with* AI feedback tools (RQ1) and *how they perceive* them (RQ2). Framework: Ryan et al.'s (2023) learner-centered feedback dimensions — **Future Impact, Sensemaking, Agency**.^[raw/papers/tandf-2026-learner-centered-feedback-ai.md]

]]> https://edtechdev.github.io/aied/pages/lets-chat-chatbot-outreach-2026.html https://edtechdev.github.io/aied/pages/lets-chat-chatbot-outreach-2026.html Mon, 03 Aug 2026 00:00:00 +0000 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-completion channel (tutoring attendance, homework completion), with no spillover to other courses.

]]> https://edtechdev.github.io/aied/pages/llm-facilitation-timing-online-discussions.html https://edtechdev.github.io/aied/pages/llm-facilitation-timing-online-discussions.html Mon, 03 Aug 2026 00:00:00 +0000 This study asks when (not just how) LLMs should facilitate online discussions, creating PEFK, a corpus standardizing and aggregating facilitation datasets, and running the first survey on facilitation timing with expert facilitators and LLM-as-a-judge models.

]]> https://edtechdev.github.io/aied/pages/responsible-assessment-ai-era-stanford-2026.html https://edtechdev.github.io/aied/pages/responsible-assessment-ai-era-stanford-2026.html Mon, 03 Aug 2026 00:00:00 +0000 **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 generative AI has broken the assumption that final products measure human capability: learners can produce high-quality artifacts without the underlying learning, AI scoring introduces construct-irrelevant variance, and the gap between what is measured and what matters is widening. The field's response is a shift from testing events to systems of inference — continuous and formative-assessment, portfolio- and conversation-based evidence (socratic-tests-conversational-assessment), authentic-assessment in real tasks, and human-in-the-loop design — paired with validity infrastructure for automated-grading, shared definitions of emerging constructs like ai-literacy, and sustained attention to equity, transparency, and trust.

]]> https://edtechdev.github.io/aied/pages/savvy-student-attention-video-learning.html https://edtechdev.github.io/aied/pages/savvy-student-attention-video-learning.html Mon, 03 Aug 2026 00:00:00 +0000 SAVVY is an interactive visual analytics system for video-based learning that integrates visual and auditory attention signals from multimodal brain data to support top-down exploration of student attention variation across instructional videos.

]]> https://edtechdev.github.io/aied/pages/scaffolding-critical-engagement-genai-minority-students.html https://edtechdev.github.io/aied/pages/scaffolding-critical-engagement-genai-minority-students.html Mon, 03 Aug 2026 00:00:00 +0000 Using epistemic network analysis of collaborative discourse, thematic analysis of reflections, and paired-samples t-tests on prompt self-efficacy, the study documents 'strategic repurposing': students initially instrumentalized strategy talk to coordinate efficient copying before shifting toward genuine critical engagement.

]]> https://edtechdev.github.io/aied/pages/socratic-tests-conversational-assessment.html https://edtechdev.github.io/aied/pages/socratic-tests-conversational-assessment.html Mon, 03 Aug 2026 00:00:00 +0000 A stated goal is human-AI alignment for measurement reliability: the conversational format is designed to avoid construct-irrelevant variance from performative anxiety and the power imbalances of face-to-face oral examinations, though the paper is a theoretical foundation with implementation and validation left to future work.

]]> https://edtechdev.github.io/aied/pages/structrag-diagram-reasoning-ai-tutoring.html https://edtechdev.github.io/aied/pages/structrag-diagram-reasoning-ai-tutoring.html Mon, 03 Aug 2026 00:00:00 +0000 1. **Diagram-to-Graph Conversion** — OCR (Tesseract, multi-scale 1.0/1.5/2.0, majority voting) + classical CV (Hough Line Transform, contour detection, Zhang–Suen skeletonisation for curved paths). Edge confidence `Cij = 0.45·scont + 0.25·sprox + 0.20·salign + 0.10·snode`; edges ≥ 0.65 accepted, 0.40–0.65 routed to an *uncertain-edge set* U for pattern-level validation, < 0.40 discarded as noise. 2. **Structural Pattern Retrieval** — abstracts the recognized graph into topology patterns (star, ring, chain/bus, bridge/mesh, tree, hybrid, cross-layer) and retrieves similar templates from a curated library using **graph-edit-distance (GED)** matching. 3. **Pattern-Aware Prompt Construction** — feeds the LLM the graph G, uncertain edges U, retrieved templates, and candidate corrections ΔE. 4. **LLM-Guided Structural Reasoning and Correction** — GPT-4 jointly interprets G, U, T*, and ΔE to decide which uncertain edges to add and which structures to correct, with output ensembling.^[raw/papers/sle-2026-structrag-diagram-reasoning.md]

]]> https://edtechdev.github.io/aied/pages/structured-ai-demonstrations-engineering-mechanics.html https://edtechdev.github.io/aied/pages/structured-ai-demonstrations-engineering-mechanics.html Mon, 03 Aug 2026 00:00:00 +0000 **APA:** Geng, S., Lallos-Harrell, H., Ashar, J., McKenna, T. J., Dasgupta, A., Farny, C., & Lejeune, E. (2026). Structured AI demonstrations and student LLM use in engineering mechanics: Study design and preliminary results. arXiv:2607.28710.

]]> https://edtechdev.github.io/aied/pages/sycophantic-ai-social-interaction-2026.html https://edtechdev.github.io/aied/pages/sycophantic-ai-social-interaction-2026.html Mon, 03 Aug 2026 00:00:00 +0000 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 friends and family, and reported lower satisfaction with real-world interactions.

]]> https://edtechdev.github.io/aied/pages/trust-reliance-ai-education-2026.html https://edtechdev.github.io/aied/pages/trust-reliance-ai-education-2026.html Mon, 03 Aug 2026 00:00:00 +0000 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 proxy for appropriate use.

]]> https://edtechdev.github.io/aied/pages/ai-tpack-preservice-math-teachers.html https://edtechdev.github.io/aied/pages/ai-tpack-preservice-math-teachers.html Sat, 01 Aug 2026 00:00:00 +0000 SEM study of 130 preservice math teachers in South Africa (Global South context). AI-TPACK readiness explained 53% of variance. Positive associations: prior AI use, critical-ethical appraisal, and support/enablers. Senior cohorts showed sharper gains in pedagogical AI competence. Year level effects less stable in sensitivity analysis. Cautious empirical account of AI-TPACK readiness in an under-studied context.

]]> https://edtechdev.github.io/aied/pages/ai-vocational-education-training-review.html https://edtechdev.github.io/aied/pages/ai-vocational-education-training-review.html Sat, 01 Aug 2026 00:00:00 +0000 First systematic review of AI in vocational education (26 studies, 2015-2026). Intelligent XR shows consistent positive effects on procedural competence; ITS foster declarative and procedural knowledge; AI chatbots support self-regulation. Only 5 randomized experimental studies found. Documents a paradox: constructivist theories are espoused but behaviorist AI implementations dominate. Warns against an educational 'Turing Trap' of replicating human instruction instead of augmenting human judgment.

]]> https://edtechdev.github.io/aied/pages/conversational-ai-informal-learning.html https://edtechdev.github.io/aied/pages/conversational-ai-informal-learning.html Sat, 01 Aug 2026 00:00:00 +0000 Large-scale survey of 776 German participants. 87% already use LLMs in everyday learning routines. Young adults lead adoption. Four learner types identified based on task patterns and device usage. Paradoxical trust behaviors: users rely on LLMs while distrusting accuracy and privacy. Emphasizes need for multi-modal learning, collaborative features, source provision, and design for diverse learner types.

]]> https://edtechdev.github.io/aied/pages/fair-explainable-edu-recommendations.html https://edtechdev.github.io/aied/pages/fair-explainable-edu-recommendations.html Sat, 01 Aug 2026 00:00:00 +0000 Hybrid HKG-GRU framework for educational recommendations combining heterogeneous graph embeddings with sequential modeling (152 students, 59 resources, ~150K interactions). Multi-objective training with GroupDRO for fairness, MMR reranking for diversity, and built-in explainability through path-based and counterfactual analyses. HR@10=0.68, MRR=0.41. Addresses popularity bias and cold-start fairness in educational recommenders.

]]> https://edtechdev.github.io/aied/pages/genai-mindtool-generative-learning.html https://edtechdev.github.io/aied/pages/genai-mindtool-generative-learning.html Sat, 01 Aug 2026 00:00:00 +0000 Argues GenAI can serve as a Mindtool (knowledge representation tool) to facilitate Generative Learning rather than short-circuiting it. Proposes 8 pedagogical roles for GenAI: learning strategy/study buddy, collaborative thinking tool, possibility engine, Socratic opponent, personal tutor, exploratory research engine, motivator, and dynamic assessor. Provides a pedagogical framework for designing generative learning activities using GenAI.

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