Modeling generative AI adoption in higher education: An integrated TAM–TPB–SDT framework with SEM validation

This study investigates the determinants of university students' adoption of generative artificial intelligence (GAI) tools in higher education. Integrating the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and Self-Determination Theory (SDT), it develops and tests a complete model that captures cognitive, social, and motivational influences on adoption. A cross-sectional survey was conducted among 517 undergraduate and postgraduate students at Jordanian universities. The data were analyzed using structural equation modeling (SEM) with a two-step approach: confirmatory factor analysis (CFA) to validate the measurement model, followed by SEM to test the hypothesized structural relationships. Reliability, validity, measurement invariance across gender, and mediation effects were assessed. The integrated model showed excellent fit and substantial explanatory power, accounting for 83 % of the variance in behavioral intention and 81.6 % in actual AI use. Relatedness, perceived usefulness, attitude, and autonomy emerged as significant predictors of intention, while behavioral intention and competence predicted actual use. The ease of use strongly influenced usefulness, and mediation analysis confirmed indirect effects through usefulness and attitude. The model was invariant across gender groups, supporting its generalizability. This research extends TAM and TPB by integrating SDT's psychological needs, highlighting relatedness and competence as novel drivers of adoption. It provides the first empirical evidence from Jordan, a region underrepresented in the literature, highlighting that motivational dynamics carry greater weight than social norms in collectivist educational contexts. The study advances theoretical models of technology adoption and offers practical insights for universities and policymakers on promoting responsible and sustainable integration of AI in education.