The serial mediation model explains 32.5–37.0% of variance in AI-TPACK, self-efficacy, and AI integration intention¶
Claim · All claims
Evidence · 1 study · associationalr1·q2
Currency · newest study 2026 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person
Subclaims¶
Reading the evidence codes
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q2 i? The model explained 35.5% of variance in AI integration intention, 32.5% in AI-TPACK, and 37.0% in science teaching self-efficacy, with Q² values above zero indicating in-sample predictive relevance. → Zou J 2026
Evidence¶
Zou J 2026¶
Zou J, Li N, Wang X and Du J (2026) From AI literacy to AI-integrated inquiry-based science teaching: the serial mediating roles of AI-TPACK and science teaching self-efficacy among Chinese pre-service science teachers. Front. Psychol. 17:1911909. https://doi.org/10.3389/fpsyg.2026.1911909
q2 · i? · associational · r1
PLS-SEM model assessment for the 548-teacher sample reports R² of 0.355 (intention), 0.325 (AI-TPACK), and 0.370 (self-efficacy); the quote gives "35.5% of the variance in intention" and "32.5% of the variance in AI-TPACK". Stone-Geisser Q² values were 0.250, 0.218, and 0.250.
"The model explained 35.5% of the variance in intention to integrate AI into inquiry-based science teaching, 32.5% of the variance in AI-TPACK"
Discussion¶
Related Claims¶
- AI literacy positively predicts AI-TPACK among Chinese pre-service science teachers — related
- AI-TPACK positively predicts science teaching self-efficacy among Chinese pre-service science teachers — related
- AI literacy relates to AI integration intention through a serial indirect pathway via AI-TPACK and science teaching self-efficacy — related
- Self-efficacy and learning motivation serially mediate the link from AIGC affordance to self-regulated learning — related
- The model explains 62.50% of variance in AI teaching innovation behavior, with moderate predictive relevance and good fit — related
- Actual classroom use of AI among Ghanaian pre-service science teachers is moderate despite strong intentions, indicating an intention-practice gap — related
- Focal paths are robust to background controls, which did not directly predict AI integration intention — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
model-explained-variance-ai-integration-outcomes |
title |
The serial mediation model explains 32.5–37.0% of variance in AI-TPACK, self-efficacy, and AI integration intention |
description |
The serial mediation model explains 32.5–37.0% of variance in AI-TPACK, self-efficacy, and AI integration intention |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
source_checked |
2026-10-20 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — zou-j-2026 |
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