Extending the Expectation–Confirmation Model to Explain Students' Continuance Intention toward Generative AI in Higher Education

The rapid advancement of Generative Artificial Intelligence (Generative AI) has transformed higher education by providing intelligent support for students in information seeking, idea generation, academic task completion, and self-directed learning. Despite its widespread adoption, understanding the factors influencing students’ continuance intention toward Generative AI remains a critical research issue, as sustained usage is essential for the long-term success of AI-enabled educational technologies. While previous studies have primarily focused on technology acceptance and initial adoption, limited attention has been given to the determinants of post-adoption behavior in the context of Generative AI. To address this gap, this study extends the Expectation-Confirmation Model (ECM) by incorporating trust as an additional construct to explain students’ continuance intention toward Generative AI in higher education. The proposed research model examines the relationships among confirmation, trust, perceived usefulness, satisfaction, and continuance intention. A quantitative research approach was employed using survey data collected from university students who have experience using Generative AI for academic purposes. The total data of 208 were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). This study provides a deeper understanding of the factors influencing students’ post-adoption behavior and contributes to the growing literature on Generative AI in educational settings. The findings offer practical implications for higher education institutions seeking to promote the effective, responsible, and sustainable integration of Generative AI into teaching and learning processes.