Preparing for the AI era: Science teachers' readiness and professional development needs for generative AI integration in secondary education

Abstract

Despite the growing importance of Generative Artificial Intelligence (GAI) in education, significant gaps exist in understanding science teachers' readiness and professional development needs for effective GAI integration, particularly in secondary education. This mixed-method study examined science teachers’ technological readiness, identified critical adoption factors, and developed an implementation framework for GAI in secondary science education. The research involved 30 science teachers from different-sized schools in Thailand, utilizing surveys, interviews, and document analysis. Factor analysis revealed four key components influencing AI adoption: technology infrastructure (28.45 % variance), administrative support (22.33 %), AI knowledge and skills (18.76 %), and innovation attitude (15.22 %). Findings indicated significant disparities in technological readiness between large schools (M = 4.12, SD = 0.58) and small schools (M = 2.98, SD = 0.82), with infrastructure access emerging as the strongest predictor (β = 0.56, p < .001). The resulting implementation framework, validated by experts and teachers (92 % agreement rate), provides a comprehensive guide addressing both technical and pedagogical needs. While high teacher interest in AI integration (87 %) suggests promising potential, the study highlights the need for targeted interventions to address resource disparities and develop formal AI policies, particularly in smaller schools. • School size impacts AI readiness with infrastructure as the strongest predictor. • Four factors explain AI adoption: infrastructure, support, knowledge, and attitude. • Only 13 % of schools have formal AI policies despite high teacher interest (87 %). • Framework addresses technical and pedagogical needs across school contexts. • Resource gaps between large and small schools affect AI implementation readiness.

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