A UTAUT2-Based Model for Predicting AI Acceptance and Use among Physics Pre-service Teachers - Hilman Qudratuddarsi; Jumriani; Risman Saputra (Prodi Pendidikan Fisika Universitas Sarjanawiyata Tamansiswa, 2026)
The rapid emergence of Generative AI has created new opportunities and challenges in teacher education, particularly in STEM fields that rely on conceptual understanding and problem-solving. This cross-sectional survey investigated Generative AI acceptance and use among Indonesian Generation Z pre-service physics teachers using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Data were collected from 249 students at three universities through an adapted questionnaire and analyzed using partial least squares structural equation modeling (PLS-SEM). The measurement model demonstrated strong reliability, convergent validity, and discriminant validity. The structural model showed that performance expectancy, social influence, hedonic motivation, facilitating conditions, and habit significantly predicted behavioral intention, whereas effort expectancy was not significant. Habit emerged as the strongest predictor of behavioral intention and a strong predictor of AI use, while behavioral intention exerted the largest direct effect on AI use. Facilitating conditions significantly predicted behavioral intention but did not directly predict AI use. The model explained 80.4% of the variance in behavioral intention and 76.8% in AI use. These findings highlight the importance of routinized digital practices, intention formation, and institutional support in preparing physics pre-service teachers to integrate Generative AI into future teaching.