The model explains 62.50% of variance in AI teaching innovation behavior, with moderate predictive relevance and good fit¶
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? Predictors explained 62.50% of variance in AI-TIB, 29.60% in AIL, 46.00% in TSE, and 39.80% in PI; all Q2 values exceeded zero, SRMR was 0.027, and NFI was 0.898. → Bai X and Hsieh T-S 2026
Evidence¶
Bai X and Hsieh T-S 2026¶
Bai X and Hsieh T-S (2026) AI teaching innovation behavior among college teachers: a structural equation modeling analysis based on the AI-TPACK framework, teaching self-efficacy, professional identity, and AI literacy. Front. Psychol. 17:1834827. https://doi.org/10.3389/fpsyg.2026.1834827
q2 · i? · associational · r2
Structural model assessment in PLS-SEM evaluated explanatory power and predictive relevance for the 898-teacher sample. The article reports "the predictors explained 62.50% of the variance in AI-TIB" and that all Q2 values exceeded zero, with SRMR 0.027 and NFI 0.898.
"the predictors explained 62.50% of the variance in AI-TIB, 29.60% of the variance in AIL, 46.00% of the variance in TSE, and 39.80% of the variance in PI. Moreover, all Q2 values exceeded zero"
Discussion¶
Related Claims¶
- Among AI-TPACK dimensions, all except AI technological knowledge and integrative AI-TPACK are significantly and positively associated with college teachers' AI teaching innovation behavior — related
- Teaching self-efficacy, professional identity, and AI literacy are each significantly associated with AI teaching innovation behavior, with additional links among the mediators — related
- Professional identity and AI literacy partially mediate AI-TPACK associations with innovation behavior, while AI-TK and AI-TPACK show indirect-only mediation and TSE mediation is only partially supported — related
- The serial mediation model explains 32.5–37.0% of variance in AI-TPACK, self-efficacy, and AI integration intention — related
- The integrated model explains substantial variance in student engagement (R² = 0.700) in GenAI-supported learning — related
- The four-predictor model explains approximately 55.4% of variance in teachers' AI integration ability — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
model-explains-variance-in-ai-teaching-innovation-behavior |
title |
The model explains 62.50% of variance in AI teaching innovation behavior, with moderate predictive relevance and good fit |
description |
The model explains 62.50% of variance in AI teaching innovation behavior, with moderate predictive relevance and good fit |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — bai-x-and-hsieh-t-s-2026 |
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