Baseline motivational states shape perceived AI benefits more than demographics, with female-coded gender the only significant demographic effect (lower perceived competence relief)¶

Claim · All claims
Evidence · 1 study (2 entries) · associational r2 · q2
Currency · newest study 2026 · checked against its sources 2026-10-19 (automated, 2 of 2 entries) · not reviewed by a person

Subclaims¶

q2 i? Female-coded gender was negatively associated with perceived competence-related outcomes (β = −.145, p = .037). → Kevin Zhongyang Shao 2026 q2 i? The only significant demographic influence in the SEM was gender; baseline psychological needs and personal agency mattered more than demographic composition. → Kevin Zhongyang Shao 2026 (2)

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

SEM covariate result from the 206-student survey with effect-coded demographics. "Female-coded gender was also negatively associated with perceived competence related outcomes (β=−. 145,p=. 037)"; the authors call the effect modest.

"Female-coded gender was also negatively associated with perceived competence related outcomes (𝛽=−. 145,𝑝=. 037)."

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

Discussion of the SEM: "The only significant evidence of demographic influence in the SEM results occurred in relation to students' gender", supporting the authors' statement that baseline needs and agency mattered more than demographic composition.

"The only significant evidence of demographic influence in the SEM results occurred in relation to students’ gender."

Discussion¶

Learner Variables¶

• Gender — predictor: outcomes differ by group: a gap reflecting circumstances and treatment, not a cause

Related Claims¶

• Engineering students perceive AI chatbot benefits as strongest for relief from competence frustration, moderate for autonomy, and weakest for relatedness — 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 — a broader claim this one bears on
• Inattention moderates how baseline competence frustration and baseline autonomy translate into perceived AI benefits, weakening both positive associations — related
• Students with higher baseline relatedness satisfaction perceive less additional relatedness support from AI chatbot use — related
• In agentic AI-supported online learning, perceived autonomy, competence, and relatedness are each positively associated with learning motivation — related
• Chatbot design carries a documented female-by-default gender bias — related
• Gender is associated with small differences in GenAI use, literacy, and monitoring, while educational level shows no significant differences — related
• Female students and students with lower prior achievement experienced more negative motivational changes — related