伦敦医学学生采用大型语言模型:探索性定性访谈研究
1Department of Surgery and Cancer, Imperial College London, London, United Kingdom.
JMIR formative research
|January 19, 2026
概括
医学学生经常使用像ChatGPT这样的大型语言模型 (LLM) 进行学习,但在准确性和过度依赖方面面临挑战. 课程必须适应,以确保人工智能在医学教育中安全有效地整合.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 健康 专业 教育 卫生 专业 教育
背景情况:
- 大型语言模型 (LLM) 在医疗保健中越来越受欢迎.
- 在了解医学学生与LLMs的现实世界经验方面存在差距.
研究的目的:
- 探索在伦敦的医学学生中对LLM使用的感知好处,促进者和障碍.
主要方法:
- 与15名医学学生进行半结构面试.
- 以技术接受模型为指导的诱导性主题分析.
主要成果:
- 学生使用LLM进行总结,概念澄清和问题生成,重视速度和易用性.
- 促进者包括同行学习和可访问性;障碍包括不准确性,过度依赖和隐私问题.
- 初级学生努力检测不准确的LLM输出,影响信任和批判性思维.
结论:
- LLM提供了效率,但也引起了对可靠性,技能降低和学术诚信的担忧.
- 迫切需要课程来指导安全的LLM使用,并调整教学/评估策略.
- 未来的研究应该包括对AI时代课程开发的多样化的环境和教育工作者的观点.
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