Secondary Teachers’ Acceptance of Generative Artificial Intelligence in Teaching Based on the TAM

Abstract

Artificial Intelligence (AI) is transforming global education by enhancing teaching effectiveness and supporting personalized learning. Among its applications, generative AI has gained increasing attention for its ability to create instructional content, assist assessment, and streamline lesson preparation. This study aimed to examine secondary school teachers’ acceptance of generative AI in teaching using the Technology Acceptance Model (TAM). A quantitative survey design was employed, involving 367 teachers from secondary schools in Negeri Sembilan, Malaysia, who were selected through simple random sampling. Data were collected via a structured questionnaire and analyzed using descriptive statistics, Pearson correlation, and multiple regression. Results indicated high acceptance levels for perceived usefulness, perceived ease of use, and behavioral intention. The correlation analysis revealed strong and positive relationships among the three constructs, while regression results showed that both perceived usefulness and perceived ease of use significantly predicted behavioral intention, explaining 72.1% of its variance. Notably, perceived ease of use emerged as the stronger predictor. These findings confirm the applicability of the TAM framework in educational contexts and highlight the importance of training, user-friendly AI systems, and supportive policies to enhance teachers’ readiness. Future studies are recommended to explore qualitative insights and extended models to better understand sustainable AI integration in education.