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空军军医大学基础医学院计算机基础教研室,西安 710032
张国鹏,Email: zhanggp@fmmu.edu.cn
收稿:2025-11-01,
网络首发:2026-07-31,
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贠佳琪, 闫玉平, 李改霞, 等. 数智时代军医大学计算机基础课程形成性考核模式改革研究[J/OL]. 中华医学教育探索杂志, 2026,1-6.
Yun Jiaqi, Yan Yuping, Li Gaixia, et al. Exploration of formative assessment model for computer basic courses in military medical universities in the digital intelligence era[J/OL]. Chinese Journal of Medical Education Research, 2026, 1-6.
贠佳琪, 闫玉平, 李改霞, 等. 数智时代军医大学计算机基础课程形成性考核模式改革研究[J/OL]. 中华医学教育探索杂志, 2026,1-6. DOI: 10.3760/cma.j.cn116021-20251101-02333.
Yun Jiaqi, Yan Yuping, Li Gaixia, et al. Exploration of formative assessment model for computer basic courses in military medical universities in the digital intelligence era[J/OL]. Chinese Journal of Medical Education Research, 2026, 1-6. DOI: 10.3760/cma.j.cn116021-20251101-02333.
本文分析了传统考核模式在人工智能(generative artificial intelligence,GenAI)冲击下暴露出的知识验证失效、思维培养难落实及学生能力两极分化加剧等问题,并在此基础上结合医学生实际特点提出以能力培养为导向的重构思路,推动考核理念从“知识复现”转向“思维培养”,通过分层任务设计构建“防AI替代”的差异化考核场景,并结合AI辅助批改与人工复核方式进行多元过程性评价。以军医大学560名学员为研究对象的初步实践表明,该体系将计算思维、人机协作能力与医学专业身份进行了早期融合,能有效引导学生进行健康的人机协作,验证了该模式在保障评价公平、促进学生计算思维与数智素养发展方面的可行性与有效性,为智能时代医学计算机基础教育评价改革提供了新路径。
The study analyzes the shortcomings of the traditional assessment model under the impact of GenAI
including the failure of knowledge verification
difficulties in cultivating thinking skills
and the exacerbation of student ability polarization. Building on this analysis and considering the specific characteristics of medical students
a reconstruction approach oriented towards ability development is proposed. This approach shifts the assessment philosophy from "knowledge reproduction" to "thinking cultivation"
constructs differentiated "AI-resistant" assessment scenarios through hierarchical task design
and implements a multifaceted process evaluation method combining AI-assisted grading and manual review. Preliminary practice involving 560 students at a military medical university demonstrates that this system facilitates the early integration of computational thinking and human-AI collaboration skills with their professional identity as medical students. It effectively guides students towards healthy human-AI collaboration
verifying the model's feasibility and effectiveness in ensuring assessment fairness and promoting the development of students' computational thinking and digital-intelligent literacy. This provides a new pathway for the reform of computer basic education assessment among medical students in the intelligence era.
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