<![CDATA[The integration of artificial intelligence (AI) in higher education triggers a crucial dilemma; students rely on AI for academic efficiency while facing concerns over data stability, information bias, and privacy risks. This study analyzes the factors influencing students' satisfaction and continuance intention to use AI-based applications. Utilizing a quantitative survey approach, data were gathered from 211 university students. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to evaluate the structural model and relationships between variables. Theoretically, this study integrates the Technology Acceptance Model (TAM) and the Expectation Confirmation Theory (ECT). The novelty lies in examining AI continuance intention in higher education by incorporating the moderating effects of perceived risk. The results reveal that perceived usefulness and perceived ease of use are strong predictors of continuance intention, with satisfaction acting as a central mediator. These findings confirm that functionality and operational simplicity drive student usage loyalty. Practically, educational institutions and technology developers must prioritize user-friendly interfaces and tangible utility to ensure long-term engagement and sustainable AI adoption. Ultimately, the primary contribution of this research lies in its empirical integration of TAM and ECT within the generative AI context, offering a novel framework that deconstructs conventional assumptions regarding technology anxiety among digital-native students.]]>
