Grade-9 students' LLM-supported mathematical interactions are strongly request-dominated, with little explicit planning, monitoring, or evaluation¶

Claim · All claims
Evidence · 1 study · qualitative r? · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Of 880 substantive student turns, 82% were requests, versus 10% plan, 6% monitor, and 2% evaluate. → Abdelghani 2026

Evidence¶

Abdelghani 2026¶

Abdelghani, R., Kaiser, P., Murayama, K. (2026). Changes in Help-Seeking Strategies Predict unaided Performance during AI-based Mathematical Learning. https://arxiv.org/abs/2606.28472 doi:10.48550/arxiv.2606.28472

q2 · i? · qualitative · r?

Descriptive analysis of the conversational corpus from 112 Grade-9 students chatting with Mistral Large during 40-minute open-ended mathematical-modelling practice; turns were coded with a validated turn-level codebook. The authors report "82% were classified asREQUEST" with plan, monitor, and evaluate rare.

"Of the 880 substantive student turns, 82% were classified asREQUEST, compared with 10%PLAN, 6%MONITOR, and 2%EVALUATE."

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
• Within a single LLM session, students' help-seeking shifts toward answer- and verification-seeking while explicit self-regulation modestly increases — related
• Students' stated pre-interaction intentions only weakly correspond to their enacted help-seeking with an LLM — related
• Contingent tutoring held delegation at the level of an answer-on-request assistant while delivering more artifacts, one decision at a time — related
• Planning was the most frequently coded category (777; 14.8%), with Discourse Continuity (561; 10.7%) reflecting multi-turn reformatting requests — related
• Students' help requests to the AI tutor were predominantly instrumental rather than executive, with procedural content and low verification — related
• Learning-oriented intentions did not significantly predict enacted in-chat behavior; only selecting final solutions predicted executive and answer-seeking requests — related
• During AI-tutor interactions, students' turns were dominated by help requests with near-absent monitoring and evaluation — possibly the same claim (merge candidate)
• 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
• 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