Integrating AI Chatbot in Medical Education: A Technology Acceptance Study of ChatGPT using UTAUT2

The integration of Artificial Intelligence (AI) chatbots into medical education represents a transformative shift, offering new opportunities for personalized learning while also presenting unique challenges related to user acceptance and institutional readiness. Adapted based on the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2), this study examines the factors influencing the adoption of ChatGPT among undergraduate medical students. Using a convenience sampling method, data were collected from 70 undergraduate medical students via an online questionnaire. The responses were analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) to test the hypothesized relationships within the UTAUT2 framework. Findings reveal that Habit and Facilitating Conditions are significant predictors of students’ behavioural intention to use ChatGPT, emphasizing the role of prior behavioural usage patterns and institutional support in influencing ChatGPT adoption. In contrast, Performance Expectancy, Effort Expectancy, Hedonic Motivation, and Price Value did not exhibit significant influence on behavioural intention, suggesting that perceived usefulness, ease of use, enjoyment, and cost considerations are less influential in this context. These results contribute to the growing discourse on AI integration in medical education, The study highlights the need for acceptance and adoption strategies that go beyond enhancing functionality or user experience, and instead focus on cultivating habitual use and ensuring institutional support.