STUDENTS' ADOPTION OF GENERATIVE AI IN HIGHER EDUCATION:

Abstract

The rapid advancement of Generative Artificial Intelligence (GenAI) has significantly transformed the higher education landscape, creating new opportunities for enhancing teaching and learning while simultaneously raising important ethical and academic concerns. As students increasingly integrate GenAI tools into their academic activities, understanding the factors influencing their adoption and the resulting educational outcomes has become a critical area of inquiry. This study examines students' adoption of Generative AI in higher education by exploring the influence of perceived opportunities and perceived challenges on adoption behavior and its subsequent impact on transformative learning. Drawing upon the Technology Acceptance Model and Transformative Learning Theory, the study conceptualizes perceived opportunities through dimensions such as improved productivity, personalized learning, and faster information access, while perceived challenges encompass ethical concerns, accuracy concerns, and academic integrity issues. A quantitative research design is proposed, targeting university students who actively engage with Generative AI tools for academic purposes. Data will be collected through a structured questionnaire and analyzed using Structural Equation Modeling (SEM) to test the proposed relationships. The study posits that perceived opportunities positively influence students' adoption of Generative AI, whereas perceived challenges negatively affect adoption intentions. Furthermore, the adoption of Generative AI is expected to contribute positively to transformative learning by fostering critical reflection, enhanced knowledge acquisition, and adaptive learning experiences. The findings are anticipated to provide valuable insights for educators, policymakers, and higher education institutions seeking to promote the responsible and effective integration of Generative AI in academic environments. By addressing both the benefits and challenges associated with GenAI adoption, the study contributes to the emerging discourse on technology-enabled educational transformation and responsible AI implementation in higher education.