Ecological Predictors of AI Literacy in Chinese K-12 Teachers: A Structural Equation Modeling Study

Abstract

Although AI is being rapidly developed and applied in education, gaps remain in factors affect teachers’ AI literacy. A cross-sectional survey of 1,680 teachers was conducted to explore relationships between school environment, social environment, teacher self-efficacy, and AI literacy via structural equation modeling (CFI = 0.986; RMSEA = 0.03). The results showed that teachers’ AI literacy was 3.89 ± 1 (out of 5) in total, and the theory-practice gap was significant: stronger performance in awareness (β = 0.75) and ethics (β = 0.76), but weaker performance in application literacy (β = 0.72) and evaluation literacy (β = 0.81). School environment had the strongest direct effect on AI literacy (β = 0.270, p < 0.001), followed by teacher self-efficacy, which served as an important mediator (β = 0.259, p < 0.001). Social environment had no direct effect on teachers’ AI literacy (β = 0.060, p = 0.362), implying that distal effects need to be mediated by school. Demographic analysis showed urban–rural differences, decline after age 40, and subject differences (science > liberal arts). Therefore, we suggest that policymakers should transfer to supporting school-level interventions with targeted resources allocation. School leaders should create supportive technological environments and self-efficacy programs. In addition, teachers should participate in hands-on training with a focus on practical skills. This study provides useful references for integrating AI into K-12 education in China.