Evidence suggests over-reliance on generative AI may negatively affect students' self-regulated learning behaviors, potentially harming retention and transfer to novel contexts¶

Claim · All claims
Evidence · 1 study · review r2 · q2
Currency · newest study 2026 · checked against its sources 2026-10-16 (automated, 1 of 1 entry) · not reviewed by a person

Subclaims¶

q2 i? There is evidence that over-reliance on generative AI might negatively affect students' self-regulated learning behaviors, potentially impacting their ability to retain and transfer knowledge to novel contexts. → Kirk Vanacore 2026

Evidence¶

Kirk Vanacore 2026¶

Kirk Vanacore, Ryan S. Baker, Avery H. Closser, and Jeremy Roschelle. (2026). The Path to Conversational AI Tutors: Integrating Tutoring Best Practices and Targeted Technologies to Produce Scalable AI Agents. arXiv preprint. https://arxiv.org/abs/2602.19303

q2 · i? · review · r2

Narrative review reporting cited evidence that over-reliance on generative AI "might negatively affect students' self-regulated learning behaviors" with potential impacts on retention and transfer. The underlying studies are not described; no effect sizes are printed.

"However, there is evidence that over-reliance on gener- ative AI might negatively affect students’ self-regulated learning behaviors, potentially impacting their ability to retain and transfer knowledge to novel contexts [73, 76]."

Studies bearing on this claim¶

What happens to regulation when learners have a generative AI assistant, and what counteracts offloading? This table gathers the studies behind this claim and the 36 narrower claims that bear on it: 28 distinct studies (10 causal, 6 associational, 5 review, 4 theoretical, 1 quant-synthesis, 1 qualitative, 1 design), q1–q4, 6 of 28 with an effect size coded. One row per study, codes as each entry records them; no effect is averaged and no verdict is drawn. Built by scripts/synthesize_claim.py from eval/synthesis/; a row that looks wrong is fixed on the claim it comes from.

Discussion¶

Learner Variables¶

• Self-Regulation — outcome: instruction changes it

Related Claims¶

• Over-reliance on AI scaffolding risks learned helplessness, so focus should shift to fostering self-regulated AI interaction — possibly the same claim (merge candidate)
• Teachers raise big-picture concerns about GenAI including surveillance, over-reliance, reduced teacher collaboration, job replacement, and training needs — related
• Faster rate of learning may be negatively related to long-term retention (efficiency-effectiveness trade-off) — related
• Risk aversion plays a significant role in teachers' GenAI adoption decisions — related
• Effective AI use increases AI over-reliance, which negatively affects sustainable learning performance — related
• Rule-triggered pedagogical agents increased SRL activities and learning outcomes relative to untriggered agents — related
• Learners learning with generative AI risk offloading monitoring and control of learning to the AI, suppressing learner agency — possibly the same claim (merge candidate)
• Reflective and hybrid feedback outperform direct GenAI feedback on delayed AI-free transfer — a narrower finding that bears on this claim
• A randomized controlled trial with 86 K-12 students found a significant gain in self-regulated-learning strategy use from agentic coaching systems — related
• Perceived AI-adaptive learning environments are negatively associated with college students' self-regulated learning in a three-wave longitudinal survey — related
• Reaching the qualitative SOLO phase in GenAI interaction required repeated meta-level learner interventions, demanding the very self-regulatory skills the tool was expected to support — related
• Student-agent interaction featured a significant Query-Evaluation-Query verification loop (RM-QR-EA), distinct from the linear QR-UN path of student-instructor interaction — related
• Within the AI-assisted group, the frequency of checking the AI's claims against sources predicted decision-making gains, with no students flagged for over-reliance — a narrower finding that bears on this claim
• HelpCoach led to greater knowledge retention one week later than pre-task help-seeking training alone — reports the opposite
• LLMs can deliver scalable, personalized, interactive feedback almost instantly, reducing dependency on teacher availability and supporting self-regulated learning in large courses — reports the opposite
• Reflective strategies such as cognitive forcing, pre-commitment, and uncertainty communication reduce overreliance on AI and improve decision quality, but have been evaluated only as short-term single-session interventions — related
• GenAI use collapses SRL cycle transitions, with metacognitive activity restructuring around the tool — a narrower finding that bears on this claim
• Self-regulated learning ability significantly moderates Gen-AI's effect on HOT: high-SRL students show ES = 0.863 versus ES = 0.284 for low-SRL students — related
• Effort regulation negatively predicts, and general academic help seeking positively predicts, reported AI dependency; time management is non-significant — related
• HelpCoach did not significantly outperform Baseline in improving or retaining self-reported self-regulation — related
• In both interaction-log and think-aloud records from a GenAI-assisted instructional-design task, regulation was the most frequent operational label and monitoring the least frequent — related
• Learners using a GenAI system issued prompts that offloaded tasks that may have been important for them to perform themselves, such as differentiating concepts and deciding what to learn — a narrower finding that bears on this claim
• Unscaffolded GenAI access did not significantly change pre-service teachers' self-regulated learning relative to traditional learning — related
• Advanced learners may benefit from learner-controlled selection of learning tasks and levels of generality — related