Abstract Background Artificial intelligence (AI) is reshaping the landscape of medical education with unprecedented depth and breadth. As technologies like large language models and natural language processing advance, AI agents with multimodal interaction capabilities—such as intelligent teaching assistants and virtual simulation labs—are demonstrating immense potential. Concurrently, medical students face challenges including a disconnect between theoretical knowledge and clinical practice, excessive cognitive load, and a lack of personalized practical opportunities. Medical education AI agents are poised to address these issues, but their successful integration hinges on student acceptance and adoption. This study aims to fill a gap in the current empirical research by investigating the key psychological mechanisms and behavioral factors that influence medical students’ adoption of AI educational agents. Methods This study constructed an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model by integrating four key variables tailored to the medical education context: AI Trust, Perceived Risk, Hedonic Motivation, and Trialability. A cross-sectional survey was conducted with an initial sample of 200 clinical medicine students following their interaction with a custom-developed interactive medical education AI agent. After excluding invalid responses, a final valid sample of 155 participants was retained. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to validate the theoretical model and test the research hypotheses. Results The constructed model demonstrated strong explanatory power, successfully accounting for 85.3% of the variance in students’ behavioral intention (R² = 0.853). Effort Expectancy (β = 0.362, p < 0.001) and Performance Expectancy (β = 0.297, p < 0.001) were the strongest direct positive predictors of behavioral intention, with Facilitating Conditions (β = 0.258, p = 0.002) also showing a significant impact. A noteworthy finding was that Social Influence had no significant effect on behavioral intention (β = 0.038, p = 0.633). Furthermore, Hedonic Motivation had a significant positive influence on both Effort Expectancy (β = 0.818, p < 0.001) and Performance Expectancy (β = 0.237, p < 0.001). AI Trust, Trialability, and lower Perceived Risk also significantly enhanced students’ Performance Expectancy. Conclusions The findings indicate that for medical students, who are highly autonomous professional learners, the intrinsic value of an AI educational tool (i.e., its utility and ease of use) is the dominant factor in their adoption decisions, far outweighing the social influence of peers or authorities. Therefore, the key to successfully promoting such technologies lies in building users’ intrinsic trust, reducing their perceived risks, and providing an engaging, immersive learning experience. These findings provide a solid empirical basis for the optimal design of medical AI educational agents and for strategies to effectively integrate them into the curriculum.
Yue et al. (Fri,) studied this question.