🏷️ over-reliance
61 pages tagged with over-reliance(48 articles, 13 concepts)
📄 AI-Assisted Autonomous Learning and Reduced Academic Accomplishment in Vocational Higher Education: The Mediating Role of Hardiness
> **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…
📄 From Enhancement to Over-Reliance: A Mixed-Method Study of Generative AI and Sustainable Learning Performance
> **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…
📄 HAIML: A Human-Centered AI Metacognitive Learning Model — A Framework for Human Agency and Reflective Learning in the Age of Artificial Intelligence
> **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, self-regulated-learning, human-in-the-loop, ai-literacy, cognitive-offloading
📄 Metacognitively Discordant Completion and the Aware Pass-Through of Non-Understanding in Generative AI Learning
> **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…
🏷️ AI Misuse and Learning Harm
> **AI misuse and learning harm** — the causal relationship between students offloading cognitive work to generative AI and reduced durable learning, even when immediate task performance rises. The de…
2026-08-12 · cognitive-offloading, academic-integrity, assessment, self-regulated-learning, motivation
🏷️ Student Misconceptions about AI
> **Student misconceptions about AI** — the inaccurate beliefs students hold about what AI systems are, what they do, and what using them means for learning, especially in academic contexts. Misconcep…
2026-08-12 · ai-literacy, trust-calibration, metacognition, cognitive-offloading, academic-integrity
🏷️ Trust Calibration
> **Trust calibration** — the metacognitive capacity to align one's confidence in an AI system with its actual reliability in a given context, knowing when to trust and when to question its output. Tr…
2026-08-12 · ai-literacy, trust-calibration, human-ai-collaboration, metacognition, hallucination-risk
🏷️ Cognitive Offloading
> **Cognitive offloading** — the use of external tools (including AI) to reduce internal cognitive demand, shifting mental work from the learner to the system. In AI in education, cognitive offloading…
2026-08-09 · cognitive-offloading, cognitive-load-theory, ai-literacy, student-experience, generative-ai
🏷️ Generative AI
> **Generative AI** — AI systems capable of producing text, code, images, and other content, most prominently large language models like GPT-4 and Claude. Generative AI is the technology driving the c…
🏷️ Hallucination Risk
> **Hallucination Risk** — the danger that AI systems generate plausible but factually incorrect or fabricated content in educational contexts, where such errors can mislead learners, undermine trust,…
🏷️ Over-Reliance
> **Over-reliance** — excessive or uncalibrated dependence on AI tools where students delegate cognitive work they should perform themselves, resulting in reduced learning, diminished agency, and the …
2026-08-09 · cognitive-offloading, ai-literacy, trust-calibration, student-experience, generative-ai
🏷️ Pedagogical Safety
> **Pedagogical safety** — the design principle that AI education systems must protect learners from harm, including inappropriate content, unsafe advice, biased treatment, and manipulative interactio…
🏷️ Scaffolding
> **Scaffolding** — structured support that helps learners accomplish tasks they cannot yet complete independently, with support fading as competence grows. In AI in education, scaffolding is the prim…
🏷️ Student Experience with AI
> **Student experience with AI** — how learners perceive, interact with, and are affected by AI tools in educational settings. With over 85 articles in the wiki, student experience is one of the most-…
📄 When Help is Unhelpful: Evaluating AI Tutors for Productive Struggle
> **Synthesis:** Zhang et al. (2026) introduce TutorMoments, a replay-based evaluation framework that tests whether LM tutors adapt their pedagogical actions to context — scaffolding when support is n…
📄 Students' engagement with ChatGPT feedback: implications for student feedback literacy in the context of generative artificial intelligence
A qualitative study of **16 undergraduates** at a Hong Kong teacher-education university who used **ChatGPT 3.5** to obtain feedback on IELTS writing tasks. Data came from unobtrusive screen-recorded …
📄 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).…
📄 GenAI Knowledge, Epistemic Orientation, and Intellectual Values Predict Undergraduate Students' Critical GenAI Use
A correlational study (N = 67 undergraduate psychology students, Bielefeld University) testing two **protective factors against uncritical GenAI overreliance**: (1) **knowledge about genAI** and (2) t…
📄 Generative AI Can Harm Teaching
> The null average performance effect masks strong offsetting heterogeneity — and the exam had severe ceiling compression (control mean 89.2/100, 47% ≥ 95), which also limits power. The belief reversa…
📄 Unanticipated Effects of Generative AI on Expertise Pathways and Performance Perception in System Administration
> **Rana Abou Khamis, Hala Assal, Ashraf Matrawy** — arXiv preprint (2026).…
📄 Sycophantic AI makes human interaction feel more effortful and less satisfying over time
> 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)…
📄 Trust and Reliance on AI in Education: AI Literacy and Need for Cognition as Moderators
> 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 w…
📄 Stop Writing for Me: Generative Refusal in AI Tools for Thought
Position paper exploring "Generative Refusal" — AI tools that strategically withhold text generation to demand user articulation, functioning as a Maieutic Partner rather than a cognitive offloading t…
📄 When AI Does the Work, What Is Learning For? Post-Instrumental Learning and the Risk of Capacity Dissolution
Argues that as AI systems become capable of producing the artifacts through which institutions recognize competence, existing ethical frameworks centered on AI failures become insufficient. Develops t…
📄 Critical AI Tutors: Empower or Enslave?
> **Critical AI Tutors: Empower or Enslave?** — A position paper presented at the AIED 2025 workshop that issues a stark warning: unchecked use of AI tutors risks creating a generation of cognitively …
🏷️ Affective Computing
> **Affective computing** in education uses physiological and behavioral signals to sense learner emotion and adapt instruction — see [[affective-text-wearable-student-health]], [[multimodal-affective…
📄 Informal Learning Emerges in Everyday Human-LLM Interaction
As LLMs take over task execution, a central worry is that everyday AI use becomes cognitive offloading that erodes people's own capability development. This study analyses 128,569 naturalistic human-L…
📄 EduGuard: A Safe RAG-Based LLM Tutor for Programming Education
EduGuard is a retrieval-augmented generation (RAG) tutoring framework that directly confronts the safety and pedagogical failures of unrestricted LLM tutors in introductory programming. Unrestricted t…
📄 Student Evaluation of Repeated AI Feedback Across a Semester of Writing
This short paper provides rare descriptive classroom evidence on what happens when students repeatedly use generative-AI feedback across a full semester of writing coursework. Drawing on 2,988 reflect…
📄 Is AI making us stupid?
A 3-page **perspective** (opinion/review, not an empirical study) addressing whether AI use erodes human cognition. The authors' answer: **not inherently — but the risk is real and follows the cogniti…
📄 Generative AI without guardrails can harm learning: Evidence from high school mathematics
This landmark field experiment is among the first randomized controlled trials to causally demonstrate that **unguarded generative-AI tutoring can harm skill acquisition**, not merely fail to help. Co…
📄 Navigating the moral panic: encouraging appropriate use of GenAI in the classroom rather than condemning innovation as disruption
> **Jennifer M. Krebsbach & Victoria L. Cross (University of California, Davis)** — *Assessment & Evaluation in Higher Education* (Taylor & Francis). Open Access, CC BY 4.0. doi:10.1080/02602938.2026.…
📄 A Tool-Invariant Framework for Teaching and Assessing Computational Methods in the Age of Agentic AI
> **Larry Engelhardt (Francis Marion University)** — *arXiv:2607.15518* [physics.ed-ph], submitted 17 Jul 2026. CC BY 4.0. doi:10.48550/arXiv.2607.15518. > **Note on type:** This is a *framework / pos…
📄 Measuring How Students Rely on Generative AI in Academic Writing: Development and Multi-Source Validation of the Generative AI Reliance Types Scale (GenAI-RTS)
As generative AI (GenAI) becomes embedded in undergraduate academic writing, *how* students rely on these tools — not merely whether they use them — has emerged as a core question for [[academic-integ…
📄 AI tools in Arab University English classrooms: Looking back and forward
This paper aims to synthesize empirical research on AI tools used to support English as a second/foreign language (EL2) learners in Arab University classrooms (AUCs) between Jan 1st 2023 and Aug 31st …
📄 AIED's Unfinished Mission: Centering Agency and Motivation in the Age of Effortless Bypass
The widespread availability of general-purpose AI that can perform complex cognitive tasks threatens to undermine education at scale. This effortless bypass dilemma sharpens a challenge AIED has long …
📄 The GenAI Skill Bypass: Mapping Divergent Pathways of University Students and Staff AI Literacy
Higher education institutions are increasingly expected to ensure that both students and staff develop Generative AI (GenAI) literacies. In response, they are introducing professional development prog…
📄 Agents That Teach: Designing Incidental Learning Back into AI-Assisted Software Development
As AI coding agents take over substantial implementation work, developers increasingly lose the informal, effortful problem-solving through which software engineering expertise historically accumulate…
📄 When AI Is Wrong on Purpose: How Students Respond to Buggy GenAI Code
As generative AI becomes central to software development, CS education is shifting toward prompt-centered workflows where students describe intended behavior in natural language to elicit code. But pr…
📄 Why Put in This Much Effort?": How AI Availability Shapes Students’ Motivation in Introductory Programming
**Tran, Harper & Price (2026)** examine a pressing motivational paradox in contemporary computing education: the ready availability of AI tools that can complete programming assignments undermines stu…
2026-06-30 · academic-integrity, higher-ed, llm, student-experience, ai-assistance-reduces-persistence
📄 Four Types of LLM Reliance and Their Predictors Among Undergraduate Writers: A Mixed-Methods Study at a Minority-Serving R1 University
Hossain (2026) develops a typology of LLM reliance among undergraduate writers at a minority-serving R1 institution, identifying four distinct profiles: strategic scaffolders who use AI for idea gener…
📄 Students' Perception Accuracy of Partners' AI Use and its Relation to Collaboration Performance
Graf et al. (2026) identify a new challenge in collaborative programming education: AI use is now an invisible yet consequential dimension of collaboration, and partners often misread ability and effo…
📄 Stuck in a Spiral": Shame and Guilt as Social Regulators of AI Use in Computing Education
> An interview study with 19 computing students through a functionalist perspective of shame and guilt. Findings show these emotions regulate when and how students make their AI use visible, engaging …
2026-06-16 · student-experience, higher-ed, academic-integrity, hallucination-risk, learning-analytics
📄 Learning by Chatting? Investigating the Impact of Generative AI on Information Seeking and Learning
> **Shravika Mittal, Su Lin Blodgett, Q. Vera Liao**…
📄 Modularizing Educational LLM-Agency for Fostering Responsible Learning Assistance
The widespread adoption of AI chatbots in education will drastically change learning, making responsible deployment a critical concern. While large language models (LLMs) might have access to sources …
🏷️ AI Plagiarism Detection
Technologies and methods for detecting AI-generated content in academic submissions, including classifier-based approaches, watermarking, and stylistic analysis. The effectiveness and reliability of t…
📄 Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognition
This preregistered between-subjects study (N=559) provides the first rigorous evidence that [[llm]] reasoning traces — increasingly common in AI interfaces — do not improve performance and can activel…
📄 Defining AI Fatigue in Academic Contexts: Dimensions, Indicators, and a Stage-Based Model Using Grounded Theory
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, ai-literacy, higher-ed, affective-computing, self-regulated-learning
📄 Cognitive offloading and the speedup illusion in human-AI interaction
This preregistered large-scale study (N = 1,237) investigates whether people are well-calibrated in estimating the time savings from AI assistance on simple cognitive tasks. The key finding is a **spe…
📄 Socially fluent AI decouples conversational signals from source identity in online interaction
This study embedded undisclosed AI agents as teammates in synchronous text-based group interactions across analytical, creative, and ethical tasks with 786 participants making 1,572 identity judgments…
📄 The 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 …
📄 Combating Harms of Generative AI in CS1 with Code Review Interviews and a Flipped Classroom
Oral code reviews paired with a flipped classroom represent a pragmatic harm-reduction approach to generative AI in CS education. Rather than banning LLMs, Fowles et al. (2026) designed weekly formati…
📄 Evidence of a Cognitive Shift in AI Education: How Students Are Rethinking Human Intelligence?
This paper presents a striking longitudinal finding: as AI becomes a routine educational tool, students systematically revalue **human intelligence (HI) over artificial intelligence (AI)**. Drawing on…
📄 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…
📄 Sycophancy is an Educational Safety Risk: Why LLM Tutors Need Sycophancy Benchmarks
> Sycophancy is an Educational Safety Risk: Why LLM Tutors Need Sycophancy Benchmarks **Kasneci & Kasneci (2026)** — Position paper. arXiv cs.AI/cs.HC.…
📄 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…
📄 Distinguishing performance gains from learning when using generative AI
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…
📄 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…
2026-05-13 · academic-integrity, hallucination-risk, stem-education, feedback-loop, student-experience
📄 The LLM Fallacy and Misattribution of Competence
> 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…
🏷️ Transfer of Learning
> **Transfer of Learning** — the extent to which knowledge or skills acquired in one context (e.g., practice with an AI tool) persist and apply in a different context (e.g., independent performance wi…
2026-05-07 · transfer-of-learning, metacognition, cognitive-load-theory, desirable-difficulties, k-12