Exploring graduate students’ acceptance and use of generative AI: An application of the UTAUT2 model

Generative artificial intelligence (GenAI) has recently gained significant attention within educational contexts, particularly among university students. Regardless of its significance, there are still a limited number of empirical studies on graduate students’ intention to use this technology in higher education settings. Accordingly, this study investigated the acceptance of GenAI tools by master’s and doctoral students for academic purposes. The research adapted the unified theory of acceptance and use of technology 2 model and surveyed 145 graduate students from various universities through convenience sampling. Data collected through online surveys were analyzed using the partial least squares approach to structural equation modelling. Key findings revealed that factors, including habit (HB), performance expectancy, and hedonic motivation, had a significant effect on students’ behavioral intention (BI) to use GenAI tools. Additionally, the study indicated that the most important predictors for actual GenAI use were HB and BI. Notably, demographic variables, age, gender, and the level of study, showed no significant moderating influence on the relationships among the constructs. This study provides further insight into our understanding of how GenAI tools are accepted by graduate students for academic purposes and contribute to the literature on the factors affecting their intention to use these tools.