传统医学教育评价存在多元性评价缺乏、形成性评价及反馈机制薄弱、个性化与科学性及精准性评价有待提高等问题,人工智能(artificial intelligence,AI)技术的深度融入正在逐步革新传统的教育评价模式,并为提升评价的客观性、实时性和个性化提供了新的路径。本文介绍了AI在医学教育评价中的应用现状,分析其在赋能评估主体多元化、技能评估场景化、过程评价个性化、评价数据精准化和考核管理智能化等方面的优势,梳理中山大学附属第一医院应用AI在多模态评估、腹腔镜操作评价、手术视频识别、虚拟标准化病人和无人执考系统等场景的实践,提出“多模态评价—精准化反馈—交互性反思—个性化指导”的智能化“人机协同”医学教育评价创新生态体系,以期为医学生提供高效全面的动态评估与实时反馈,进而助力其系统性思维、批判性思维与数智胜任力的培养。
张俊隆
,
张昆松
,
冯劭婷
,
谭进富
,
陈传希
,
李辉雁
,
江亮
,
陈淑英
,
黄应雄
. 人工智能赋能医学教育评价创新生态体系的探索与实践[J]. 中华医学教育杂志, 2025
, 45(12)
: 911
-915
.
DOI: 10.3760/cma.j.cn115259-20250531-00610
Traditional medical education assessment still faces issues such as insufficient diversification, weak formative assessment and feedback mechanisms, and the need to improve individualization, rigor, and precision. The in-depth integration of Artificial Intelligence (AI) technology is gradually revolutionizing the traditional assessment model, providing an innovative approach to enhance the objectivity, real-time performance, and personalization of assessment. This paper reviews the current application of AI in medical education assessment, including its advantages in empowering the diversification of assessment subjects, contextualizing skill assessment, personalizing process evaluation, refining assessment data, and intelligentizing assessment management. Meanwhile, it sorts out the practical cases of applying AI in scenarios such as multimodal assessment, laparoscopic operation evaluation, surgical video recognition, virtual standardized patients, and unmanned examination systems at The First Affiliated Hospital of Sun Yat-sen University. The study proposes an intelligent ″human-machine collaboration″ innovative ecosystem for medical education assessment centered on ″Multimodal Assessment-Accurate Feedback-Interactive Reflection-Need-Based Guidance″, which provides medical students with efficient and comprehensive dynamic assessment and real-time feedback, thereby facilitating the cultivation of their systematic thinking, critical thinking, and digital intelligence competence.
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