医学教育管理

• 调查研究 • 上一篇    下一篇

医学生人工智能应用素养的现状与分群分析

  

  1. 1.武汉大学人民医院妇产科,武汉 430060; 2.武汉大学人民医院检验科,武汉 430060
  • 收稿日期:2025-11-17 修回日期:2025-12-09 出版日期:2026-07-02 发布日期:2026-07-02
  • 基金资助:

    1.武汉大学本科教育质量建设综合改革项目:智能助管在临床实习生与规培生带教中的创新应用与管理体系构建(ZG241257);2.武汉大学医学部教学研究项目:临床医学研究生科研基础能力“精准滴灌”培养模式构建与实践(2025YB12);3.武汉大学研究生导师育人方式创新项目:基于“医学+X”联合培养与思政教育的妇产科学研究生育人模式探索(413100115);4.武汉大学医学部教学研究项目(2024ZD07)

Current situation and cluster analysis of artificial intelligence application literacy among medical students

  1. 1. Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan 430060, China; 2. Department of Laboratory Medicine, Renmin Hospital of Wuhan University, Wuhan 430060, China
  • Received:2025-11-17 Revised:2025-12-09 Online:2026-07-02 Published:2026-07-02

摘要:

目的 剖析医学生人工智能(artificial intelligenceAI)应用素养的群体差异,并基于调查结果提出针对性的医学AI教育培养策略,同时论证大规模开放在线课程(massive open online courseMOOC)在医学AI教育落地中的可实现性与应用价值。方法 采用横断面调查方法,对武汉大学人民医院527名医学相关专业学生进行问卷调查。基于“认知-应用-态度”三维模型评估其AI应用素养,并开展分群比较分析。结果 医学生AI应用素养呈现显著群体分化:“先驱者”(16.50%)具备高认知、高应用水平且态度积极;“矛盾者”(41.70%)认知水平较高但应用能力薄弱,其核心障碍在于责任界定困惑(89.50%)与技能缺失(85.10%);“旁观者”(41.80%)则表现为低认知、低应用与态度模糊。不同群体对医学AI教育存在差异化需求,且MOOC相较于传统线下授课在覆盖群体、灵活度等方面具备显著优势。结论 医学生AI应用素养存在明显分层,需构建分层分类的医学AI教育体系,而MOOC是实现该教育体系高效落地的优选载体,可为医学生AI复合能力的提升提供可行路径。

关键词:

 , 医学人工智能|应用素养|分群分析|大规模开放在线课程|医学教育

Abstract:

Objective To analyze the group differences in medical students' application literacy of artificial intelligence (AI), put forward targeted medical AI education and training strategies based on the survey results, and demonstrate the feasibility and application value of massive open online course (MOOC) in the implementation of medical AI education.Methods A cross-sectional survey was conducted among 527 medical-related majors in Renmin Hospital of Wuhan University. Their AI application literacy was evaluated based on the three-dimensional "cognition-application-attitude" model, and group comparison analysis was carried out.Results Significant group differentiation was observed in medical students' AI application literacy: "Pioneers" (16.50%) had high cognition, high application level and positive attitude; "Contemplators" (41.70%) had relatively high cognitive level but weak application ability, with core obstacles being confusion about responsibility definition (89.50%) and skill deficiency (85.20%); "Bystanders" (41.80%) showed low cognition, low application and ambiguous attitude. Different groups had differentiated demands for medical AI education, and MOOC had significant advantages over traditional offline teaching in terms of group coverage and learning flexibility.Conclusion Obvious stratification exists in medical students' AI application literacy, so a hierarchical and categorized medical AI education system needs to be constructed. MOOC is an optimal carrier for the efficient implementation of this education system, which can provide a feasible path for improving medical students' AI comprehensive abilities.