During AI-tutor interactions, students' turns were dominated by help requests with near-absent monitoring and evaluation¶

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · newest study 2026 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person

Subclaims¶

q2 i? REQUEST turns averaged 72.9% of task-relevant turns, while MONITOR and EVALUATE were nearly absent (medians of 0%; means 5.7% and 3.4%). → Rania Abdelghani 2026

Evidence¶

Rania Abdelghani 2026¶

Rania Abdelghani, Peter Kaiser, and Kou Murayama. (2026). Regulating the AI Tutor: Intentions, Help-Seeking, and Self-Regulated Learning in Adolescent GenAI Use. Accepted at the NextGen Learning Interfaces Workshop, AIED 2026. https://arxiv.org/abs/2606.08568 doi:10.48550/arxiv.2606.08568

q2 · i? · associational · r2

AI-coded deductive SRL scheme applied to all 808 student turns using Gemini 2.5 Pro with a frozen prompt, fixed seed. The article reports descriptive distributions (Figure 3) and states most students rarely made comprehension needs explicit or evaluated AI responses; no inferential test is printed.

"Interactions were dominated by REQUEST turns (M= 72.9%of task-relevant turns;M dn= 75.0%, IQR63.6–87.5%). PLAN was the only other frequent function (M= 18.1%,M dn= 15.1%). MON- ITOR and EVALUATE were nearly absent, with medians of0%and means of5.7%and3.4%respectively"

Discussion¶

Learner Variables¶

• Self-Regulation — outcome: instruction changes it

Related Claims¶

• Evidence suggests over-reliance on generative AI may negatively affect students' self-regulated learning behaviors, potentially harming retention and transfer to novel contexts — the general claim this finding bears on, with the studies behind it
• Students' help requests to the AI tutor were predominantly instrumental rather than executive, with procedural content and low verification — related
• Grade-9 students' LLM-supported mathematical interactions are strongly request-dominated, with little explicit planning, monitoring, or evaluation — possibly the same claim (merge candidate)
• Meta-procedural requests are prominent in student discourse with a Socratic AI tutor: Next Step Guidance is the second-most common discourse move despite the tutor being designed not to direct students — a narrower finding that bears on this claim
• Within a single LLM session, students' help-seeking shifts toward answer- and verification-seeking while explicit self-regulation modestly increases — related
• The top of the student-discourse distribution contains essentially no categories of explicit conceptual reasoning, prediction, comparison to similar problems, or critical engagement with the tutor's suggestions — related
• LLM-based tools may interfere with reading comprehension and retention despite learners finding them helpful — related