AI knowledge and attitudes toward AI did not significantly predict teachers' AI integration ability¶

Claim · All claims
Evidence · 1 study · associational r1 · q3
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i? AI Knowledge (p = 0.466) and Attitude Toward AI in ESD (p = 0.343) showed no statistically significant effect on AI Integration Ability. → Riandi Riandi 2026

Evidence¶

Riandi Riandi 2026¶

Riandi Riandi, Ismail Ismail, Ida Kaniawati, Wahyu Sopandi, Diana Ayu Rostikawati, Defrizal Hamka & Suhendar Suhendar. (2026). Teacher involvement in developing sustainable education materials for AI integration in green energy education. Scientific Reports. https://doi.org/10.1038/s41598-025-34422-4

q3 · i? · associational · r1

In the same PLS-SEM survey of 122 teachers, both paths were non-significant; the article prints p-values but no effect sizes for these null findings, so no magnitude is reported. Equivalence was not tested.

"In contrast, AI Knowledge and Attitude Toward AI in ESD did not significantly affect AI Integration Ability (p = 0.466 and p = 0.343, respectively)."

Discussion¶

Related Claims¶

• Teachers' expectancy beliefs about successfully using AI applications shape their motivation to use them (theoretical argument). — the general claim this finding bears on, with the studies behind it
• The four-predictor model explains approximately 55.4% of variance in teachers' AI integration ability — related
• Teacher involvement in developing ESD-based teaching materials significantly predicts AI integration ability (β = 0.236, p < 0.05, f² = 0.127) — related
• Review reports that teachers' beliefs, attitudes and TPACK—not AI knowledge alone—mediate AI integration — related
• SDT need satisfaction predicted post-intervention willingness beyond baseline capacity, while post-intervention TPACK showed only a weak, non-significant association with willingness — related
• Teachers' practical use of AI in science and green energy learning significantly predicts their AI integration ability (β = 0.391, p < 0.01, f² = 0.180) — related
• In the fitted model, teachers' knowledge and willingness to change predicted teaching practice, whereas professional beliefs did not significantly influence teaching practice — related
• Additive TPACK–belief integrations produce construct overlap, causal ambiguity, and weak intervention guidance — related
• Teachers' attitudes toward GenAI tools significantly predict their behavioural intention to integrate them — related
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