Understanding ChatGPT adoption among medical students: an integrated theoretical model of psychological barriers and individual factors.

BACKGROUND

The growing adoption of generative artificial intelligence (GenAI), particularly ChatGPT, has created new opportunities for medical education by supporting learning, problem-solving, and knowledge acquisition. However, evidence regarding the psychological and individual factors influencing ChatGPT adoption among medical students remains limited. This study develops and tests an integrated theoretical model that combines Privacy Theory, Technostress Theory, Affect Theory, Diffusion of Innovation Theory, and the Theory of Planned Behavior to explain ChatGPT adoption.

MATERIAL AND

METHODS

A quantitative cross-sectional study was conducted among undergraduate medical students at King Faisal University, Saudi Arabia using a structured, self-administered questionnaire. The proposed model examined the effects of privacy concern, technophobia, guilt feelings, and innovativeness on behavioral intention and ChatGPT usage behavior, with behavioral intention serving as the mediating variable. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).

RESULTS

The findings showed that innovativeness had a significant positive relationship with behavioral intention (β = 0.579, p < 0.001), while behavioral intention had a strong positive relationship with ChatGPT usage behavior (β = 0.777, p < 0.001). In addition, behavioral intention significantly mediated the relationship between innovativeness and ChatGPT usage behavior (β = 0.450, p < 0.001). In contrast, privacy concern, technophobia, and guilt feelings were not significantly related to behavioral intention, and their indirect relationships with ChatGPT usage behavior were also not significant.

CONCLUSION

Within the proposed model, personal innovativeness was the only antecedent significantly associated with behavioral intention, whereas the psychological barriers were not statistically significant. By integrating five complementary theoretical perspectives into a single framework, this study extends current understanding of generative AI adoption in medical education and highlights behavioral intention as the key mechanism linking individual characteristics to actual use. The findings provide theoretical insights and practical guidance for promoting the responsible and effective integration of ChatGPT into medical education through initiatives that foster innovativeness and AI literacy.