Within-session help-seeking development predicts unaided post-test performance beyond pre-test performance and static AI-use features¶

Claim · All claims
Evidence · 1 study (2 entries) · associational r? · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? After the static block was included, temporal features increased explained variance from R2 = .185 to R2 = .281 (∆R2 = .096, F(2,81) = 5.43, p = .006). → Abdelghani 2026 q2 i? The HS change score was positively associated with unaided post-test performance (β = .29, 95% CI [.10, .49], p = .003): students whose requests became more understanding-oriented performed better without AI. → Abdelghani 2026 (2)

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? · associational · r?

Hierarchical regression with N = 89 adding the two within-session shift indices (SRL and HS change) after pre-test and static features. The temporal block significantly increased explained variance: "∆R 2 =.096,F(2,81) = 5.43,p=.006".

"after the static block had already been included, the temporal features increased explained variance fromR2 =.185toR 2 =.281,∆R 2 =.096,F(2,81) = 5.43,p=.006."

Abdelghani 2026 (2)¶

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? · associational · r?

Coefficient for the HS change score in the full model with N = 89. Students whose requests became relatively more understanding-oriented subsequently performed better without AI; the association was significant after adjustment (p = .007).

"In the full model, the HS change score was positively associated with unaided post-test performance,β=.29, 95% CI[.10,.49],p=.003"

Discussion¶

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
• Static whole-session summaries of AI use add no detectable post-test variance beyond pre-test performance — related
• Within-session SRL change shows no detectable association with unaided post-test performance — reports the opposite
• Within a single LLM session, students' help-seeking shifts toward answer- and verification-seeking while explicit self-regulation modestly increases — related