Factors influencing medical students' adoption of AI educational agents: an extended UTAUT model.

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

SUBMITTER: Zhao X 

PROVIDER: S-EPMC12681116 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature