Determinants of ChatGPT Adoption in Higher Education Students: A UTAUT Framework Analysis

Abstract

As generative AI technologies such as ChatGPT gain traction, their capacity to transform higher education learning experiences is widely acknowledged. Yet, the specific drivers behind student acceptance of these tools are not yet fully understood. To address this gap, the current study investigates the key factors determining ChatGPT adoption by applying the theoretical lens of the Unified Theory of Acceptance and Use of Technology (UTAUT). Employing a quantitative approach, the research gathered survey data from 256 undergraduates in Metro Manila, Philippines, and analyzed it using Partial Least Squares Structural Equation Modeling (PLS-SEM). The analysis confirmed that effort expectancy, performance expectancy, soial influence, and facilitating conditions are all instrumental in forming a student’s intention to use the technology. Performance expectancy was identified as the primary determinant. Interestingly, effort expectancy exerted a negative influence, underscoring that students prioritize the utility of ChatGPT over its simplicity. Subsequently, the study established a strong, positive link between the intention to use and actual user behavior. These findings not only affirm the relevance of the UTAUT model for studying educational AI but also provide actionable recommendations for institutions seeking to foster AI-enhanced learning ecosystems. The research thus advances theoretical knowledge of technology acceptance and holds practical implications for optimizing AI integration in education. Further investigation into other contextual variables in varied educational settings is recommended.

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