Technology acceptance directly predicts reflective AI use and learning engagement in AI-supported programming (survey SEM, N=131)¶
Claim · All claims
Evidence · 1 study · associationalr2·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? In the MLR survey model, technology acceptance significantly predicted reflective use (β = 0.774, p < 0.001) and engagement (β = 0.694, p < 0.001). → 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
Secondary survey dataset (N = 131) analyzed with a parcel-based latent SEM estimated with robust maximum likelihood; the parcel model showed CFI = 0.951, TLI = 0.934, RMSEA = 0.082, and SRMR = 0.029. The article reports "Technology acceptance predicted reflective use (β = 0.774, p < 0.001) and engagement (β = 0.694, p < 0.001)" in the MLR direct-path results. No effect sizes beyond standardized β are printed.
"Technology acceptance predicted reflective use (β = 0.774, p < 0.001) and engagement (β = 0.694, p < 0.001)."
Discussion¶
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) — related
- Reflective AI use predicts programming self-efficacy and engagement, while PSE does not predict engagement (survey SEM) — related
- Hypothesized indirect effects from technology acceptance to engagement through reflective use, PSE, and the serial RU–PSE path are not significant (null result) — related
- ASR usage frequency moderately predicts reflective behavior but does not significantly predict learning motivation — related
- In the TAM structural model, satisfaction mediated sustained use: usability strongly predicted usefulness (β = 0.93), usefulness predicted satisfaction (β = 0.76), but usefulness did not directly predict use intention (β = 0.02, p = .896) — related
- AI literacy directly and positively predicts learning engagement among university students — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
ta-predicts-reflective-use-and-engagement-survey |
title |
Technology acceptance directly predicts reflective AI use and learning engagement in AI-supported programming (survey SEM, N=131) |
description |
Technology acceptance directly predicts reflective AI use and learning engagement in AI-supported programming (survey SEM, N=131) |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
generated |
by process:wiki-ingest on 2026-10-12 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — wang-s-and-zhao-j-2026 |
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