Focal paths are robust to background controls, which did not directly predict AI integration intention¶

Claim · All claims
Evidence · 2 studies · 2 associational r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? After adding gender, year, major, university type, AI use frequency, AI training, and practicum experience as controls, every focal path kept its direction and significance with coefficient changes not exceeding 0.008, and no control directly predicted intention. → Zou J 2026 q2 i? AI use frequency showed limited associations with self-efficacy: sometimes (β = 0.099) and often (β = 0.096) users reported higher STSE than rare users, while very frequent use was not significant. → Zou J 2026 (2)

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 · r2

Two-stage control analysis with standardized construct scores and 5,000 case-wise bootstrap samples; the quote reports that "the absolute changes in path coefficients did not exceed 0.008". Explained variance increased only marginally (incremental f² of 0.012–0.026).

"the direction and significance of every focal path remained unchanged after the controls were included, and the absolute changes in path coefficients did not exceed 0.008"

Zou J 2026 (2)¶

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 · r2

Secondary control-model analysis of the same 548-teacher sample; the quote reports the sometimes/often versus rarely contrast with "β = 0.099, p = 0.032" and "β = 0.096, p = 0.033", and that very frequent use was not significant.

"compared with par- ticipants who rarely used AI, those who used AI sometimes (β = 0.099, p = 0.032) or often (β = 0.096, p = 0.033) reported higher STSE, whereas very frequent AI use was not significant"

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
• Structural paths did not differ significantly across AI usage-frequency groups, though exploratory coefficients varied numerically — related
• Main structural paths remain substantively unchanged after controlling for gender, major, and AI tool use frequency — possibly the same claim (merge candidate)
• Exploratory multi-group analyses suggest competence-related paths differ by educational level and gender, with heterogeneity concentrated downstream of intention — related
• Demographic differences in AI anxiety are small, with only grade showing a significant but modest effect — related
• General AI literacy is not significantly related to in-service science educators' AI use or to their age, gender, or years of service — related
• Innovative competence differs by background variables, with AI interest level showing the strongest association — related
• The serial mediation model explains 32.5–37.0% of variance in AI-TPACK, self-efficacy, and AI integration intention — related
• Structural paths are stable when continued-use intention and current use are modeled as separate outcomes, and AI anxiety is not directly related to continued use — related
• Gender and migration background have only very small impacts on the strength of single comparison effects — related