Exploring Pre-Service Teachers' Intention to Use Artificial Intelligence in Education: A Structural Equation Modeling Approach Based on UTAUT2

Abstract

The growing relevance of Artificial Intelligence (AI) in education necessitates a better understanding of its acceptance among future educators. This study investigates the factors influencing pre-service teachers' intention to use AI, employing the UTAUT2 model extended with sociodemographic moderators. A cross-sectional quantitative design was applied to a sample of 908 undergraduate students from Early Childhood and Primary Education programs in Andalusia, Spain. Structural equation modeling results reveal that performance expectancy, effort expectancy, and habitual use are significant predictors of behavioral intention toward AI use. In contrast, the proposed moderating effects of gender and academic year were found to be non-significant. Findings highlight the pivotal role of habitual engagement with AI while questioning the effectiveness of current curricular approaches in promoting its pedagogical use, as academic progression showed no moderating influence. The study emphasizes the need for targeted training and curricular updates to foster meaningful AI integration in teacher education.