Preservice science teachers' preparedness for integrating AI in science teaching: A Structural Equation Modeling approach

The integration of artificial intelligence (AI) in science education is becoming essential, yet research on preservice science teachers’ preparedness for AI adoption remains scarce. This study addressed this gap by examining the factors influencing AI integration readiness using a Structural Equation Modeling (SEM) approach. Data were collected from 350 preservice science teachers in private and public higher education institutions through a structured survey. The SEM results revealed that prior technology experience significantly predicted AI readiness (β = 0.257, p < 0.001), confidence in learning AI (β = 0.273, p < 0.001), and self-transcendent goals (β = 0.267, p < 0.001). Additionally, attitude towards AI strongly influenced AI readiness (β = 0.504, p < 0.001) and confidence in learning AI (β = 0.338, p < 0.001). Engagement in AI learning emerged as the strongest predictor of preparedness for AI integration (β = 0.803, p < 0.001). These findings highlight the importance of AI-focused teacher training programs and experiential learning strategies to enhance AI competency. The study underscores the need for curriculum enhancements to foster AI engagement and mitigate AI-related anxiety, ensuring that future educators are well-equipped for AI-driven pedagogy in science education.