Key Factors Influencing Preservice Chinese Teachers’ Willingness for Implementing AI Applications in Higher Education

The swift progression of Artificial Intelligence (AI) is revolutionizing multiple domains, including higher education. This research examines the main variables affecting Chinese preservice teachers' readiness to apply AI in higher education by using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. Structural equation modeling (SEM) was used to analyze a questionnaire (N = 493) that was distributed to preservice teachers in higher institution. The study finds that performance expectancy, habit, and ethical and privacy concerns significantly influence behavior intention, highlighting their importance in fostering AI adoption. Behavior intention is a strong predictor of use behavior, emphasizing the need to focus on behavior to ensure effective technology integration. Effort expectancy, social influence, facilitating conditions, hedonic motivation, or learning value do not significantly affect behavior intention, suggesting their impact may be context-specific. Notably, gender and experience significantly moderate use behavior in a negative way. This indicates that gender differences and various levels of experience impact technology usage, necessitating tailored support strategies. Future research should explore contextual factors and interactions between predictors and moderators to refine AI integration strategies in education.