Faculty adoption of AI-assisted teaching tools in Chinese higher education: an integrated UTAUT2–TAM framework at Shanghai University

Introduction: This study tests an integrated UTAUT2-TAM framework for faculty adoption of AI-assisted teaching tools at Shanghai University. Methods: A mixed-methods sequential explanatory design combined a quantitative survey of 326 faculty members with 22 semi-structured interviews. PLS-SEM, MICOM, PLS-MGA, and codebook thematic analysis were used. Results: Performance expectancy, perceived usefulness, institutional support, social influence, and AI self-efficacy were the strongest positive correlates of faculty attitude, while perceived risk showed a significant negative association. The model explained 62.1% of variance in attitude, 52.7% in adoption intention, and 48.3% in self-reported adoption behavior. Discussion: The findings demonstrate discipline-specific adoption pathways and identify compliance-driven adoption among senior faculty as a boundary condition for the attitude-mediation mechanism.