Assessing ChatGPT adoption in Jordanian medical education: a UTAUT model approach.

Background: ChatGPT has shown significant promise in transforming medical education by streamlining research and improving teaching methods. However, its adoption in Middle Eastern medical education has remained underexplored. This study investigated the factors influencing the adoption of ChatGPT in Jordanian medical education using a modified Unified Theory of Acceptance and Use of Technology (UTAUT) framework.

Methods: A cross-sectional survey was conducted with medical students and faculty members at the University of Jordan. A validated 33-item questionnaire distributed online and on campus targeted individuals familiar with the ChatGPT. Structural equation modeling (SEM) assessed the relationships between key constructs, including Performance Expectancy (PE), Effort Expectancy (EE), Perceived Risk (PR), Facilitating Conditions (FC), and attitude (ATT).

Results: Among 127 participants (53% male, mean age 23.2 ± 7.6), ATT was significantly influenced by PE and EE, explaining 37% of its variance. Behavioral Intention (BI) was predicted by ATT and had a significant positive effect on actual usage. FC did not significantly influence EE or BI, suggesting a limited reliance on external support. Contrary to expectations, PR did not negatively affect ATT, indicating that utility outweighed concerns about misinformation or privacy. Overall, the model explained 53% of the variance in BI and 36.5% of the variance in actual usage.

Conclusion: The adoption of ChatGPT in Jordanian medical education is driven by perceived utility and ease of use, with attitudes playing a pivotal role. Addressing misinformation risks and improving trust through tailored strategies can foster broader integration of AI tools, such as ChatGPT, in medical training.