Generative artificial intelligence (AI) is reshaping higher education by offering personalized and efficient learning support. However, little is known about what drives students to adopt these technologies. This study explores key factors influencing university students’ adoption of generative AI tools for learning. Through an extensive systematic literature review, these constructs were identified to then propose an adoption model. Data were collected from 145 university students with prior experience using generative AI tools through a structured survey. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the study examined relationships among the factors. Findings reveal that perceived usefulness, attitude, hedonic motivation, and social influence all positively affect students’ behavioral intention to use generative AI. Whereas task-technology fit, perceived ease of use, and trust have an indirect impact. These insights help educators and policymakers better understand how to integrate generative AI into education effectively and responsibly.

Keywords: Adoption, Students, Generative AI, Technology Acceptance Model.