Students' help requests to the AI tutor were predominantly instrumental rather than executive, with procedural content and low verification¶

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Among students with at least one REQUEST turn, requests were predominantly instrumental (M=75.1%) rather than executive (M=24.9%), with procedural content most frequent (M=37.4%) and verification low (M=16.3%, median 0%). → 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 help-seeking typology over the 96 students with at least one REQUEST turn, reported descriptively in the results section (Figure 3). The article notes the low verification rate contrasts with the 69.7% who said they wanted the AI to check their understanding.

"requests were predominantly instrumental (M= 75.1%,M dn= 85.7%) rather than executive (M= 24.9%). Content was primarily procedural (M= 37.4%) and con- ceptual (M= 25.1%), followed by answer-seeking (M= 21.1%) and verification (M= 16.3%,M dn= 0%)"

Discussion¶

Related Claims¶

• Adolescent students' stated pre-chat intentions with an AI math tutor tilt strongly toward scaffolded support rather than answer offloading — 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 — related
• Grade-9 students' LLM-supported mathematical interactions are strongly request-dominated, with little explicit planning, monitoring, or evaluation — related
• Within a single LLM session, students' help-seeking shifts toward answer- and verification-seeking while explicit self-regulation modestly increases — related
• Robot embodiment changes verification and debugging: learners treat robot execution as perceptual evidence for hypothesis-driven revisions, while AI-Only learners rely on generic help-seeking — 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 — related
• Students' stated pre-interaction intentions only weakly correspond to their enacted help-seeking with an LLM — related
• Chat-driven vibe-coding builders converge on a single generate-and-check script whose dominant time cost is dead waiting for the tool — related
• Adolescent digital self-regulation is enacted through a typology of four strategy types differing in executive effort versus external scaffolding — related