中华医学教育杂志

中华医学教育杂志 ›› 2026, Vol. 46 ›› Issue (8): 567-572.DOI: 10.3760/cma.j.cn115259-20250903-01055

• 课程改革与建设 • 上一篇    下一篇

人工智能赋能医学生科研训练课程的探索

高芙蓉1, 许洁2, 金彩霞1, 李姣2, 王娟3, 刘玉红4, 李思光3, 张玲5, 曹金凤6, 章小清7, 吕立夏1   

  1. 1同济大学医学院生物化学与分子生物学系,上海 200331;
    2同济大学医学院实验教学中心,上海 200331;
    3同济大学医学院细胞与遗传学系,上海 200331;
    4同济大学图书馆信息素养教研部,上海 200092;
    5同济大学医学院人体解剖及组织胚胎学系,上海 200331;
    6同济大学医学院本科教学办公室,上海 200331;
    7同济大学医学院干细胞中心,上海 200331
  • 收稿日期:2025-09-03 发布日期:2026-07-29
  • 通讯作者: 吕立夏, Email: lulixia@tongji.edu.cn
  • 基金资助:
    2023年度上海高校市级重点课程(1500104273);同济大学2025年本科智慧课程建设(4250152009/144,4250152009/145)

Exploration of artificial intelligence empowering the ″scientific research training″ course for medical students

Gao Furong1, Xu Jie2, Jin Caixia1, Li Jiao2, Wang Juan3, Liu Yuhong4, Li Siguang3, Zhang Ling5, Cao Jinfeng6, Zhang Xiaoqing7, Lyu Lixia1   

  1. 1Department of Biochemistry and Molecular Biology, Tongji University School of Medicine, Shanghai 200331, China;
    2Experimental Teaching Center, Tongji University School of Medicine, Shanghai 200331, China;
    3Department of Cell and Genetics, Tongji University School of Medicine, Shanghai 200331, China;
    4Information Literacy Education and Research Department, Tongji University Library, Shanghai 200092, China;
    5Department of Human Anatomy and Histoembryology, Tongji University School of Medicine, Shanghai 200331, China;
    6Undergraduate Teaching Office, Tongji University School of Medicine, Shanghai 200331, China;
    7Stem Cell Center, Tongji University School of Medicine, Shanghai 200331, China
  • Received:2025-09-03 Published:2026-07-29
  • Contact: Lyu Lixia, Email: lulixia@tongji.edu.cn
  • Supported by:
    2023 Shanghai Municipal Key Course for Higher Education Institutions (1500104273); Tongji University 2025 Undergraduate Smart Course Construction (4250152009/144, 4250152009/145)

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摘要： 人工智能(artificial intelligence, AI)技术的飞速发展推动着医学教育向数据驱动模式转型,为医学生科研训练带来新的机遇。当前,医学生科研训练教学普遍面临科研入门门槛高、学生难以独立完成从选题到方案设计完整流程的问题。为此,本研究基于“以学生为中心”和“一致性建构”原则,构建了“任务引擎”驱动的“目标－活动－评价”一体化智慧教学模式,通过序列化任务将AI工具深度融入虚拟选题、科研设计与成果评价等核心教学环节,并以同济大学医学院2023级临床医学(“5+3”一体化)专业135名学生为对象开展教学实践,采用问卷调查方法评估教学效果。结果显示,课程有效提升了医学生的AI素养:在AI基础能力方面,完全无AI基础的医学生比例从课前10.4%(14/135)下降至课后0.8%(1/131);在概念认知方面,了解机器学习、深度学习、自然语言处理的医学生比例分别从75.6%(102/135)、68.9%(93/135)、43.0%(58/135)提升至91.6%(120/131)、88.5%(116/131)、70.2%(92/131);在工具应用方面,每天多次使用AI工具的医学生比例从3.7%(5/135)升至55.0%(72/131),认为AI显著提升科研效率的医学生比例从20.0%(27/135)升至68.7%(90/131);在批判性思维方面,主动验证AI信息可靠性的医学生比例从36.3%(49/135)升至55.0%(72/131),对AI结论质疑并验证的医学生比例从33.3%(45/135)升至48.1%(63/131)。研究结果表明,以任务引擎系统化融入AI的教学模式可以有效提升医学生的AI概念认知和工具应用能力,显著提高科研训练效率,但在批判性思维能力培养上效果相对有限,这提示AI赋能教学需要在实施中防范技术依赖,通过嵌入强制性验证与反思环节等结构化设计,促进医学生高阶思维的同步发展。

关键词: 学生,医科, AI赋能, 以学生为中心, 一致性建构, 任务引擎, 科研训练

Abstract: The rapid advancement of artificial intelligence (AI) is driving the transformation of medical education toward a data-driven model, creating new opportunities for scientific research training among medical students. Currently, scientific research training in medical education generally faces challenges such as high entry barriers and difficulties for students to independently complete the entire process from topic selection to research design. To address these issues, this study developed an integrated smart teaching model of ″objectives-activities-evaluation″ driven by a ″task engine″ based on the principles of ″learner-centered″ and ″constructive alignment″. Through sequenced tasks, AI tools were deeply integrated into core teaching components, including virtual topic selection, research design, and outcome evaluation. Teaching practices were conducted with 135 students enrolled in the clinical medicine (″5+3″ integration) program, class of 2023, at Tongji University School of Medicine, and the teaching effectiveness was evaluated via questionnaires. The results showed that the course significantly enhanced medical students′ AI literacy: in terms of basic AI capabilities, the proportion of medical students with no prior AI knowledge decreased from 10.4% (14/135) before the course to 0.8% (1/131) after the course; in conceptual understanding, the percentages of medical students familiar with machine learning, deep learning, and natural language processing increased from 75.6% (102/135), 68.9% (93/135), and 43.0% (58/135) to 91.6% (120/131), 88.5% (116/131) and 70.2% (92/131), respectively; in tool application, the proportion of medical students using AI tools multiple times daily rose from 3.7% (5/135) to 55.0% (72/131), and those believing AI significantly improves research efficiency increased from 20.0% (27/135) to 68.7% (90/131); in critical thinking, the percentage of meidical students actively verifying the reliability of AI-generated information increased from 36.3% (49/135) to 55.0% (72/131), and those questioning and validating AI conclusions rose from 33.3% (45/135) to 48.1% (63/131). The study demonstrates that systematically incorporating AI via a task engine can effectively enhance medical students′ conceptual understanding and tool application of AI, significantly improving the efficiency of research training. However, its effect on cultivating critical thinking skills was relatively limited, suggesting that AI-empowered teaching should guard against technology dependence during implementation and promote the simultaneous development of higher-order thinking through structured designs such as mandatory verification and reflective exercises.

Key words: Students, medical, AI empowered, Learner-centered, Constructive alignment, Task engine, Research training

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