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<p>Artificial intelligence-generated content (AIGC) is an emerging technology with growing influence across numerous fields, yet factors shaping its sustained adoption—particularly among specialized groups such as medical students—remain poorly understood. This study examines the determinants of medical students’ intention to use AIGC tools, integrating the Unified Theory of Acceptance and Use of Technology (UTAUT), Diffusion of Innovations theory, and Perceived Risk theory into a comprehensive framework. Data were collected from 401 medical students and analyzed using structural equation modeling. The results indicate that performance expectancy, effort expectancy, and social influence are the strongest positive predictors of usage intention, while perceived risk and perceived trust did not show significant effects. These findings underscore the importance of enhancing usability, social support, and integration into educational workflows. The study provides actionable insights for medical educators, technology developers, and policymakers seeking to promote AIGC adoption through tailored training, ethical guidelines, and system improvements.</p>

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Unlocking medical students’ adoption of AIGC tools: a multi-theory perspective

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月28日

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  • Zuwen Zhou,
  • Huanhuan Qi,
  • Zhuofan Li,
  • Chen Wang,
  • Huiying Qi,
  • Liping Guo

摘要

Artificial intelligence-generated content (AIGC) is an emerging technology with growing influence across numerous fields, yet factors shaping its sustained adoption—particularly among specialized groups such as medical students—remain poorly understood. This study examines the determinants of medical students’ intention to use AIGC tools, integrating the Unified Theory of Acceptance and Use of Technology (UTAUT), Diffusion of Innovations theory, and Perceived Risk theory into a comprehensive framework. Data were collected from 401 medical students and analyzed using structural equation modeling. The results indicate that performance expectancy, effort expectancy, and social influence are the strongest positive predictors of usage intention, while perceived risk and perceived trust did not show significant effects. These findings underscore the importance of enhancing usability, social support, and integration into educational workflows. The study provides actionable insights for medical educators, technology developers, and policymakers seeking to promote AIGC adoption through tailored training, ethical guidelines, and system improvements.