Reflective AI use predicts programming self-efficacy and engagement, while PSE does not predict engagement (survey SEM)¶

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? Reflective use significantly predicted programming self-efficacy (β = 0.519, p < 0.001) and engagement (β = 0.273, p = 0.034), whereas the direct TA→PSE path (p = 0.057) and PSE→ENG path (p = 0.813) were not significant. → Wang S and Zhao J 2026

Evidence¶

Wang S and Zhao J 2026¶

Wang S and Zhao J (2026) Reflective AI use and student engagement in AI-supported programming: technology acceptance, programming self-efficacy, and behavioral trace evidence. Front. Psychol. 17:1929887. https://doi.org/10.3389/fpsyg.2026.1929887

q2 · i? · associational · r2

MLR direct-path estimates from the parcel-based latent SEM on the N = 131 survey dataset, reported in Table 7 with robust standard errors and Wald confidence intervals. The article states "Reflective use predicted programming self-efficacy (β = 0.519, p < 0.001) and engagement (β = 0.273, p = 0.034)" and reports the two non-significant paths. Only p-values are printed for the null paths.

"Reflective use predicted programming self-efficacy (β = 0.519, p < 0.001) and engagement (β = 0.273, p = 0.034). The direct TA→ PSE path was not significant (p = 0.057), and PSE did not predict engagement (p = 0.813)."

Discussion¶

Learner Variables¶

• Motivation — outcome: instruction changes it

Related Claims¶

• Text-based reflective use from Copilot Critical Engagement responses does not predict productive engagement, with limited coder reliability and a TA ceiling effect (exploratory null) — reports the opposite
• Hypothesized indirect effects from technology acceptance to engagement through reflective use, PSE, and the serial RU–PSE path are not significant (null result) — related
• Technology acceptance directly predicts reflective AI use and learning engagement in AI-supported programming (survey SEM, N=131) — related
• Self-efficacy and motivation positively predict self-directed learning, with motivation the strongest direct predictor — related
• Thoughtless use of generative AI significantly and negatively predicts both self-efficacy and motivation — related
• Self-efficacy has a significant positive effect on motivation among undergraduates using generative AI — related