Inattention moderates how baseline competence frustration and baseline autonomy translate into perceived AI benefits, weakening both positive associations¶

Claim · All claims
Evidence · 1 study (3 entries) · associational r2 · q2
Currency · newest study 2026 · checked against its sources 2026-10-20 (automated, 3 of 3 entries) · not reviewed by a person

Subclaims¶

q2 i? The baseline competence frustration × inattention interaction significantly predicts perceived competence (b = −.018, p = .003); higher inattention attenuates the positive association. → Kevin Zhongyang Shao 2026 q2 i? The baseline autonomy × inattention interaction significantly predicts perceived autonomy (b = −.033, p = .015). → Kevin Zhongyang Shao 2026 (2) q2 i? No other tested inattention interactions were significant, including those involving baseline personal agency or relatedness. → Kevin Zhongyang Shao 2026 (3)

Evidence¶

Kevin Zhongyang Shao 2026¶

Kevin Zhongyang Shao, Denise Wilson, Yale Quan, Sep Makhsous. (2026). Designing Needs- and Attention-Aware AI Learning Tools for Engineering Education: Insights from Psychological Outcomes. arXiv. https://arxiv.org/abs/2607.26338 doi:10.48550/arxiv.2607.26338

q2 · i? · associational · r2

Latent interaction analysis (modsem product-indicator method) on the 206-student survey, with mean-centered inattention. "the interaction between baseline competence frustration and inattention significantly predicted perceived competence (b=−. 018,p=. 003)."

"First, the interaction between baseline competence frustration and inattention significantly predicted perceived competence (𝑏=−. 018,𝑝=. 003). Students with greater baseline competence frustration tended to report greater relief from frustration, but this positive association weakened as inattention increased."

Kevin Zhongyang Shao 2026 (2)¶

Kevin Zhongyang Shao, Denise Wilson, Yale Quan, Sep Makhsous. (2026). Designing Needs- and Attention-Aware AI Learning Tools for Engineering Education: Insights from Psychological Outcomes. arXiv. https://arxiv.org/abs/2607.26338

q2 · i? · associational · r2

Same latent interaction analysis. "the interaction between baseline autonomy and inattention was significant in predicting perceived autonomy (b=−. 033,p=. 015)", indicating the positive autonomy association was weaker for students with lower attention.

"Second, the interaction between baseline autonomy and inattention was significant in predicting perceived autonomy (𝑏=−. 033,𝑝=. 015)."

Kevin Zhongyang Shao 2026 (3)¶

Kevin Zhongyang Shao, Denise Wilson, Yale Quan, Sep Makhsous. (2026). Designing Needs- and Attention-Aware AI Learning Tools for Engineering Education: Insights from Psychological Outcomes. arXiv. https://arxiv.org/abs/2607.26338

q2 · i? · associational · r2

Same latent interaction analysis reported null results for the remaining tested moderators: "No other tested interactions with inattention were statistically significant"; equivalence was not tested.

"No other tested interactions with inattention were statistically significant, including interactions involving baseline personal agency or relatedness."

Discussion¶

Related Claims¶

• Engineering students perceive AI chatbot benefits as strongest for relief from competence frustration, moderate for autonomy, and weakest for relatedness — related
• Baseline motivational states shape perceived AI benefits more than demographics, with female-coded gender the only significant demographic effect (lower perceived competence relief) — related
• Higher baseline autonomy and higher baseline competence frustration predict greater perceived relief from competence frustration after AI chatbot use, while higher baseline personal agency predicts less — related
• Students with higher baseline relatedness satisfaction perceive less additional relatedness support from AI chatbot use — related
• AI Paradox: students report higher confidence in critical AI awareness than in operational AI usage (p = 0.015) — related
• Baseline interaction quality moderates component-outcome relations, with strongest relations for teachers with the lowest baseline quality — related
• Perceived competence predicts isomorphic test performance and perceived autonomy predicts transfer performance — related
• Digital competency weakens the positive relationship between teacher–AI collaboration and work alienation — related