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    <title>AI Ed Wiki</title>
    <link>https://edtechdev.github.io/aied/</link>
    <description>Recently added articles on AI in education</description>
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    <lastBuildDate>Tue, 04 Aug 2026 23:00:32 +0000</lastBuildDate>
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      <title>EduZone: A Framework for Evaluating LLM Safety for K-12 Students and Teachers</title>
      <link>https://edtechdev.github.io/aied/pages/eduzone-llm-safety-k12.html</link>
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      <pubDate>Tue, 04 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>**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.**</p>]]></description>
    </item>
    <item>
      <title>Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Framework</title>
      <link>https://edtechdev.github.io/aied/pages/egai-power-systems-education.html</link>
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      <pubDate>Tue, 04 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>**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.**</p>]]></description>
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    <item>
      <title>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</title>
      <link>https://edtechdev.github.io/aied/pages/gpt4o-mini-music-analysis-scoring.html</link>
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      <pubDate>Tue, 04 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>**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.**</p>]]></description>
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    <item>
      <title>OECD Digital Education Outlook 2026</title>
      <link>https://edtechdev.github.io/aied/pages/oecd-digital-education-outlook-2026.html</link>
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      <pubDate>Tue, 04 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>**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.</p>]]></description>
    </item>
    <item>
      <title>Rethinking Generative AI Literacy: An Integrative, Developmental, and Dialectical Framework for K-12 Teacher Education</title>
      <link>https://edtechdev.github.io/aied/pages/rail-ed-genai-literacy-teacher-education.html</link>
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      <pubDate>Tue, 04 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>**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.**</p>]]></description>
    </item>
    <item>
      <title>Access is Not Enough: Human Support Improves Engagement with AI Tutoring</title>
      <link>https://edtechdev.github.io/aied/pages/access-not-enough-ai-tutoring-2026.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
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    <item>
      <title>The agency gap in AI-supported writing: how reactive and proactive agent designs shape multimodal reasoning</title>
      <link>https://edtechdev.github.io/aied/pages/agency-gap-ai-writing.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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]</p>]]></description>
    </item>
    <item>
      <title>Agentic AI and Pedagogical Best Practice: The Tension Between Automation and Learning</title>
      <link>https://edtechdev.github.io/aied/pages/agentic-ai-pedagogical-best-practice-2026.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
    </item>
    <item>
      <title>Agreement Is Not Quality: Blind Expert Verification of Human and LLM Qualitative Coding When Human Consensus Is Not Ground Truth</title>
      <link>https://edtechdev.github.io/aied/pages/agreement-not-quality-llm-coding-verification.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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).</p>]]></description>
    </item>
    <item>
      <title>From Planning to Revision: How AI Writing Support at Different Stages Alters Ownership</title>
      <link>https://edtechdev.github.io/aied/pages/ai-writing-support-stage-ownership-2026.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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**.</p>]]></description>
    </item>
    <item>
      <title>From authentic products to authenticated processes: authentic assessment in AI-rich higher education</title>
      <link>https://edtechdev.github.io/aied/pages/authentic-products-authenticated-processes-2026.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
    </item>
    <item>
      <title>Beyond Detection: redesigning authentic assessment in an AI-mediated world</title>
      <link>https://edtechdev.github.io/aied/pages/beyond-detection-authentic-assessment-ai-2025.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
    </item>
    <item>
      <title>The care-full craft of feedback in an age of generative AI</title>
      <link>https://edtechdev.github.io/aied/pages/care-full-feedback-genai.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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]</p>]]></description>
    </item>
    <item>
      <title>Students' engagement with ChatGPT feedback: implications for student feedback literacy in the context of generative artificial intelligence</title>
      <link>https://edtechdev.github.io/aied/pages/chatgpt-feedback-engagement-genai.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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]</p>]]></description>
    </item>
    <item>
      <title>The Tragedy of the Cognitive Commons: How AI Could Disrupt the Regeneration of Professional Expertise</title>
      <link>https://edtechdev.github.io/aied/pages/cognitive-commons-ai-expertise-regeneration.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
    </item>
    <item>
      <title>ConnectED: A Curriculum-Aligned AI System for Vietnamese Instructional Lesson Planning and Student Learning</title>
      <link>https://edtechdev.github.io/aied/pages/connected-ai-lesson-planning-vietnam.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
    </item>
    <item>
      <title>CoTAL: Human-in-the-Loop Prompt Engineering for Generalizable Formative Assessment Scoring and Feedback</title>
      <link>https://edtechdev.github.io/aied/pages/cotal-formative-assessment-scoring-2026.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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 <a href="automated-grading.html">automated-grading</a>.</p>]]></description>
    </item>
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      <title>GenAI Knowledge, Epistemic Orientation, and Intellectual Values Predict Undergraduate Students' Critical GenAI Use</title>
      <link>https://edtechdev.github.io/aied/pages/critical-genai-use-predictors.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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]</p>]]></description>
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      <title>Feedback futures: beyond the limits of human and GenAI capacities</title>
      <link>https://edtechdev.github.io/aied/pages/feedback-futures-genai.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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 <a href="chatgpt-feedback-engagement-genai.html">chatgpt-feedback-engagement-genai</a>'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 <a href="learner-centered-feedback-ai.html">learner-centered-feedback-ai</a>).^[raw/papers/tandf-2026-feedback-futures-genai.md]</p>]]></description>
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      <title>Generative AI Can Harm Teaching</title>
      <link>https://edtechdev.github.io/aied/pages/genai-can-harm-teaching-rct-2026.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
    </item>
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      <title>Unanticipated Effects of Generative AI on Expertise Pathways and Performance Perception in System Administration</title>
      <link>https://edtechdev.github.io/aied/pages/genai-expertise-pathways-sysadmin.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
    </item>
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      <title>Comparing Generative AI and teacher feedback: student perceptions of usefulness and trustworthiness</title>
      <link>https://edtechdev.github.io/aied/pages/genai-teacher-feedback-comparison.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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]</p>]]></description>
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      <title>Human-LLM Collaborative Inductive Coding for Conceptualizing K-12 Educator AI Use</title>
      <link>https://edtechdev.github.io/aied/pages/human-llm-collaborative-coding-k12-educator-ai.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
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      <title>Hypergamigication Through Integrating Game Engines and Learning Management Systems: Ender's Game</title>
      <link>https://edtechdev.github.io/aied/pages/hypergamification-game-engine-lms.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
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      <title>Measuring Cognitive Engagement in Collaborative Discourse with an Extended ICAP Framework: Comparing Human Annotation, In-Context Learning, and Reflective LLM Agents</title>
      <link>https://edtechdev.github.io/aied/pages/icap-cognitive-engagement-llm-agents.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
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      <title>Enhancing learner-centered feedback with AI: teachers' practices and perceptions</title>
      <link>https://edtechdev.github.io/aied/pages/learner-centered-feedback-ai.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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]</p>]]></description>
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      <title>Let's Chat: Leveraging Chatbot Outreach for Improved Course Performance</title>
      <link>https://edtechdev.github.io/aied/pages/lets-chat-chatbot-outreach-2026.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
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      <title>To Facilitate or not to Facilitate: Human and LLM Facilitator Tendencies in Online Discussions</title>
      <link>https://edtechdev.github.io/aied/pages/llm-facilitation-timing-online-discussions.html</link>
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      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
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      <title>Responsible Assessment in the AI Era: Key Insights from a Future-Focused Conference</title>
      <link>https://edtechdev.github.io/aied/pages/responsible-assessment-ai-era-stanford-2026.html</link>
      <guid isPermaLink="true">https://edtechdev.github.io/aied/pages/responsible-assessment-ai-era-stanford-2026.html</guid>
      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>**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 <a href="formative-assessment.html">formative-assessment</a>, portfolio- and conversation-based evidence (<a href="socratic-tests-conversational-assessment.html">socratic-tests-conversational-assessment</a>), <a href="authentic-assessment.html">authentic-assessment</a> in real tasks, and <a href="human-in-the-loop.html">human-in-the-loop</a> design — paired with validity infrastructure for <a href="automated-grading.html">automated-grading</a>, shared definitions of emerging constructs like <a href="ai-literacy.html">ai-literacy</a>, and sustained attention to <a href="equity.html">equity</a>, transparency, and trust.</p>]]></description>
    </item>
    <item>
      <title>SAVVY: Student Attention Visualization for Video-based Learning Analysis</title>
      <link>https://edtechdev.github.io/aied/pages/savvy-student-attention-video-learning.html</link>
      <guid isPermaLink="true">https://edtechdev.github.io/aied/pages/savvy-student-attention-video-learning.html</guid>
      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
    </item>
    <item>
      <title>Scaffolding Critical Engagement with GenAI: Transforming Ethnic Minority Preparatory Students' Collaborative Discourse in Prompt Engineering Tasks</title>
      <link>https://edtechdev.github.io/aied/pages/scaffolding-critical-engagement-genai-minority-students.html</link>
      <guid isPermaLink="true">https://edtechdev.github.io/aied/pages/scaffolding-critical-engagement-genai-minority-students.html</guid>
      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
    </item>
    <item>
      <title>The Theoretical Foundation of Socratic Tests: Dynamic, Multimodal, Conversational Examinations</title>
      <link>https://edtechdev.github.io/aied/pages/socratic-tests-conversational-assessment.html</link>
      <guid isPermaLink="true">https://edtechdev.github.io/aied/pages/socratic-tests-conversational-assessment.html</guid>
      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
    </item>
    <item>
      <title>Advancing diagram-based reasoning in AI tutoring systems: a structural approach for STEM education</title>
      <link>https://edtechdev.github.io/aied/pages/structrag-diagram-reasoning-ai-tutoring.html</link>
      <guid isPermaLink="true">https://edtechdev.github.io/aied/pages/structrag-diagram-reasoning-ai-tutoring.html</guid>
      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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]</p>]]></description>
    </item>
    <item>
      <title>Structured AI Demonstrations and Student LLM Use in Engineering Mechanics: Study Design and Preliminary Results</title>
      <link>https://edtechdev.github.io/aied/pages/structured-ai-demonstrations-engineering-mechanics.html</link>
      <guid isPermaLink="true">https://edtechdev.github.io/aied/pages/structured-ai-demonstrations-engineering-mechanics.html</guid>
      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>**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.</p>]]></description>
    </item>
    <item>
      <title>Sycophantic AI makes human interaction feel more effortful and less satisfying over time</title>
      <link>https://edtechdev.github.io/aied/pages/sycophantic-ai-social-interaction-2026.html</link>
      <guid isPermaLink="true">https://edtechdev.github.io/aied/pages/sycophantic-ai-social-interaction-2026.html</guid>
      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
    </item>
    <item>
      <title>Trust and Reliance on AI in Education: AI Literacy and Need for Cognition as Moderators</title>
      <link>https://edtechdev.github.io/aied/pages/trust-reliance-ai-education-2026.html</link>
      <guid isPermaLink="true">https://edtechdev.github.io/aied/pages/trust-reliance-ai-education-2026.html</guid>
      <pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
    </item>
    <item>
      <title>AI literacy-related domains and AI-TPACK readiness among preservice mathematics teachers: A factor-informed structural equation modelling study</title>
      <link>https://edtechdev.github.io/aied/pages/ai-tpack-preservice-math-teachers.html</link>
      <guid isPermaLink="true">https://edtechdev.github.io/aied/pages/ai-tpack-preservice-math-teachers.html</guid>
      <pubDate>Sat, 01 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
    </item>
    <item>
      <title>Artificial intelligence in vocational education and training: A systematic review of educational purposes, theoretical conceptualizations, and empirical effectiveness</title>
      <link>https://edtechdev.github.io/aied/pages/ai-vocational-education-training-review.html</link>
      <guid isPermaLink="true">https://edtechdev.github.io/aied/pages/ai-vocational-education-training-review.html</guid>
      <pubDate>Sat, 01 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
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    <item>
      <title>Conversational AI as a catalyst for informal learning: An empirical large-scale study on LLM use in everyday learning</title>
      <link>https://edtechdev.github.io/aied/pages/conversational-ai-informal-learning.html</link>
      <guid isPermaLink="true">https://edtechdev.github.io/aied/pages/conversational-ai-informal-learning.html</guid>
      <pubDate>Sat, 01 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
    </item>
    <item>
      <title>Fair and explainable educational recommendations with a hybrid Graph-GRU framework</title>
      <link>https://edtechdev.github.io/aied/pages/fair-explainable-edu-recommendations.html</link>
      <guid isPermaLink="true">https://edtechdev.github.io/aied/pages/fair-explainable-edu-recommendations.html</guid>
      <pubDate>Sat, 01 Aug 2026 00:00:00 +0000</pubDate>
      <description><![CDATA[<p>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.</p>]]></description>
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