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Abstract

Purpose: Medical students increasingly use artificial intelligence (AI)–based chatbots as learning aids, yet adoption patterns vary widely across educational contexts. This study aims to identify the factors that influence the use of AI-based chatbots among U.S. medical students.

Methods: A survey was administered to preclinical students (N =61) at a U.S. public medical school using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The survey assessed performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, and usage behavior associated with the adoption of AI-based chatbots. Regression analyses were conducted to investigate the relationships between UTAUT constructs and both behavioral intention and usage behavior.

Results: Performance expectancy was a strong predictor of both behavioral intention (β = 1.10, p < 0.001) and actual usage behavior (β = 2.53, p < 0.001). Effort expectancy, social influence, and facilitating conditions were not significantly associated with usage behavior, although facilitating conditions showed a positive trend with behavioral intention.

Conclusions: This study suggests that adoption of AI chatbots is primarily driven by perceived educational value rather than ease of use or social influence among preclinical medical students. Institutions seeking to integrate AI into medical education should establish clear expectations for use and provide training that emphasizes responsible use of AI-based chatbots.

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