The integrated model explains substantial variance in student engagement (R² = 0.700) in GenAI-supported learning¶

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

Subclaims¶

q2 i? The model's predictors account for 70% of the variance in student engagement, which the article classifies as substantial. → Ahmed A and Sultan A 2026

Evidence¶

Ahmed A and Sultan A 2026¶

Ahmed A and Sultan A (2026) Examining the impact of generative AI on student motivation and engagement: the mediating role of autonomy-support and autonomous motivation in education. Front. Psychol. 17:1852265. https://doi.org/10.3389/fpsyg.2026.1852265

q2 · i? · associational · r?

Model fitness assessment in the structural model evaluation (Table 6). R² values were AMU 0.610, ASU 0.620, SE 0.700, and SM 0.490; the engagement figure is quoted here. R² is variance explained, not a standardised effect size, so impact is null.

"The R2 value for student engagement (SE) was 0.700, indicating that the independent variables accounted for 70% of the variance in SE, which can be considered substantial."

Discussion¶

Related Claims¶

• The dual-appraisal model explains 42.7% of variance in trust in AI and 45.9% of variance in GenAI dependence — related
• The model explains 62.50% of variance in AI teaching innovation behavior, with moderate predictive relevance and good fit — related
• The TUGA, motivation, and self-efficacy jointly explain 75.3% of the variance in undergraduates’ self-directed learning — related
• Between-student differences explain a large fraction of CASED label variance, limiting what any clip-level visual model can learn — related