多学科交叉背景下人工智能融入“人体寄生虫学”课程的教学改革与探索^*

Teaching reform and exploration of integrating artificial intelligence into the “Human Parasitology” course in the context of multidisciplinary integration

摘要

目的 探索多学科交叉背景下将人工智能融入“人体寄生虫学”课程教学的应用效果。方法 选取2021级和2022级临床医学专业本科生为研究对象,其中79例2021级本科生为对照组,81例2022级本科生为试验组。对照组采用传统教学模式,试验组采用多学科交叉背景下人工智能融入“人体寄生虫学”课程教学模式,比较两组学生完成课程教学后的期末考试总成绩、理论课总成绩(包括期末考试成绩和平时成绩)、实践操作成绩差异,并统计试验组问卷调查情况。结果 试验组期末总成绩、理论课总成绩、平时成绩以及实践操作成绩均高于对照组(P＜0.05),期末考试成绩略低于对照组(P＜0.05)。调查问卷结果表明,学生对人工智能教学的成效给予了积极评价。在教学效果层面,学生普遍认为人工智能教学有助于激发学习兴趣,深化知识的理解与记忆,同时显著提升学习效率;在人才培养层面,人工智能教学被证实能够有效锻炼学生的逻辑思维,增强自主学习能力与团队协作能力;除此之外,学生还反馈人工智能教学对提升自主复习能力与综合素养具有积极作用。结论 在多学科交叉的背景下,将人工智能融入“人体寄生虫学”课程教学方法能够显著提升学生的学习成绩,同时有助于改善教学效果、促进人才培养以及提高学生的综合能力,为医学教育模式的创新提供了可行路径。该教学方法的效果优于传统教学方法。

Abstract

Objective To explore the application effect of integrating artificial intelligence into the teaching of “Human Parasitology” in the context of multidisciplinary integration. Methods Undergraduates majoring in clinical medicine from the 2021 and 2022 cohorts were selected as the study subjects, with 79 undergraduates from the 2021 cohort serving as the control group and 81 undergraduates from the 2022 cohort as the experimental group. The control group was taught using the traditional teaching mode, while the experimental group was taught using the teaching mode of integrating artificial intelligence into “Human Parasitology” in the context of multidisciplinary integration. The differences in total final exam scores, total theoretical course scores (including final exam scores and regular course scores), practical operation scores between the two groups of students after completing the course teaching were compared,and statistics were compiled on the questionnaire survey results of the experimental group. Results The experimental group had higher total final exam scores, total theoretical course scores, regular course scores, and practical operation scores than the control group (P<0.05), while their final exam scores were slightly lower than those of the control group (P<0.05). The questionnaire survey results indicated that students gave positive evaluations to the effectiveness of artificial intelligence teaching. In terms of teaching effectiveness, students generally believed that artificial intelligence teaching helped stimulate learning interest, deepen understanding and memory of knowledge, and significantly improve learning efficiency; in terms of talent cultivation, artificial intelligence teaching was proven to effectively exercise students' logical thinking, enhance autonomous learning ability, and team collaboration ability; in addition, students also reported that artificial intelligence teaching had a positive effect on improving autonomous review ability and comprehensive literacy. Conclusion Integrating artificial intelligence into the teaching of “Human Parasitology” within the context of multidisciplinary integration can significantly improve students' academic performance, while also contributing to the improvement of teaching effectiveness, talent cultivation, and the enhancement of students’ comprehensive abilities. This provides a feasible path for the innovation of medical education models. The effectiveness of this teaching method surpasses that of traditional teaching methods.

关键词

人体寄生虫学 / 多学科交叉 / 人工智能

Key words

Human parasitology / Multidisciplinary / Artificial intelligence

[1] 吴忠道, 吕志跃, 吴瑜, 等. 推进课程思政建设、迎接 “AI+教育” 机遇, 发挥人体寄生虫学课程在高质量医学人才培养中的独特作用——第十六届全国人体寄生虫学教学改革与课程建设研讨会纪要[J]. 基础医学教育, 2023, 25(8): F0003.
Wu Z D, Lv Z Y, Wu Y, et al. Promoting ideological and political construction of courses, meeting the opportunity of “AI+ education” and giving full play to the unique role of human parasitology courses in training high-quality medical talents—summary of the 16th National Seminar on Teaching Reform and Course Construction of Human Parasitology[J]. Basic Med Educ, 2023, 25(8): F0003.
[2] 李怀丰, 孙瑜阳. 人工智能自适应学习系统赋能高校教学评价改革的路径研究[J]. 黑龙江教师发展学院学报, 2025, 44(9): 65-68.
Li H F, Sun Y Y. Research on the path of teaching evaluation reform in colleges and universities empowered by artificial intelligence adaptive learning system[J]. J HeiLongJiang Inst Teach Dev, 2025, 44(9): 65-68.
[3] 涂增, 邹晓毅, 卢楠, 等. “一境三脉” 模式在病原生物学实验课程中的应用[J]. 基础医学教育, 2025, 27(9): 835-838.
Tu Z, Zou X Y, Lu N, et al. Application of “one environment and three veins” model in experimental course of pathogenic biology[J]. Basic Med Educ, 2025, 27(9): 835-838.
[4] 周汨, 尹松, 李额, 等. 人工智能技术背景下高职基础医学数字化教育的实践探索[J]. 科教文汇, 2025(17): 159-162.
Zhou M, Yin S, Li E, et al. Practical exploration of digital education of basic medicine in higher vocational colleges under the background of artificial intelligence technology[J]. Sci Educ Article Cult, 2025(17): 159-162.
[5] 王晶, 何学智. 智能技术赋能 “三阶沉浸式” 解剖学教学模式构建[J]. 基础医学教育, 2025, 27(9): 857-861.
Wang J, He X Z. Construction of “three-step immersion” anatomy teaching mode empowered by intelligent technology[J]. Basic Med Educ, 2025, 27(9): 857-861.
[6] 高晴, 袁艺标, 谢利平. 基于人工智能的 “三位一体” 机能实验教学模式研究与探索[J]. 基础医学教育, 2025, 27(9): 877-881.
Gao Q, Yuan Y B, Xie L P. Research and exploration of “trinity” functional experiment teaching mode based on artificial intelligence [J]. Basic Med Educ, 2025, 27(9): 877-881.
[7] 魏嵬, 徐子岳, 陈帅, 等. 不同数智技术在医学教学中的应用研究进展[J]. 实用心电与临床诊疗, 2025, 34(4): 572-580.
Wei W, Xu Z Y, Chen S, et al. Research advances of diverse digital-intelligent technologies applied in medical education [J]. Pract Electrocardiol Clin Treat, 2025, 34(4): 572-580.
[8] 霍宇, 覃丹, 汪磊, 等. 人工智能赋能药理学教学的创新路径与挑战[J]. 医学理论与实践, 2025, 38(16): 2866-2869.
Huo Y, Qin D, Wang L, et al. Innovative path and challenge of artificial intelligence empowering pharmacology teaching [J]. J Med Theory Pract, 2025, 38(16): 2866-2869.
[9] 石立莹, 闫景瑞, 李咏梅. 基于基础医学知识图谱的自主学习教学设计[J]. 基础医学教育, 2023, 25(10): 862-865.
Shi L Y, Yan J R, Li Y M. Teaching design of autonomous learning based on knowledge graph of basic medical sciences [J]. Basic Med Educ, 2023, 25(10): 862-865.
[10]鲍康宁, 淮盼盼, 金瑞华, 等. 社区护理学课程知识图谱的构建及应用研究[J]. 中华护理教育, 2024, 21(11): 1322-1328.
Bao K N, Huai P P, Jin R H, et al. Study on the construction and application of knowledge map of community nursing course [J]. Chin J Nurs Educ, 2024, 21(11): 1322-1328.
[11]曹得萍, 李大宇, 陈根, 等. 新医科背景下人体寄生虫学SBE混合式教学设计与实践[J]. 中国高等医学教育, 2024(10): 69-70.
Cao D P, Li D Y, Chen G, et al. Design and practice of a blended SBE teaching model in human parasitology under the new medicine curriculum [J]. China High Med Educ, 2024(10): 69-70.
[12]齐艳伟, 张玉红, 马长玲. 人工智能在“人体寄生虫学”教学中的运用[J]. 医学教育研究与实践, 2025(2): 264-268.
Qi Y W, Zhang Y H, Ma C L. Application of artificial intelligence in“Human Parasitology”teaching[J]. Med Educ Res Pract, 2025(2): 264-268.
[13]邹节新, 张李鄢洋, 沈海默, 等. 基于国家寄生虫资源库对寄生虫学实验考试的改革探索[J]. 生物学杂志, 2022, 39(6): 122-126.
Zou J X, Zhang L, Shen H M, et al. Exploration on the reform of experimental examination of parasitology based on the national parasitic resource center of China[J]. J Biol, 2022, 39(6): 122-126.
[14]谭百宏, 王艳玲, 郑敬彤. 人体寄生虫学教学新时代的挑战和发展方向[J]. 中国寄生虫学与寄生虫病杂志, 2022, 40(5): 635-641.
Tan B H, Wang Y L, Zheng J T. Challenges and development direction of human parasitology teaching in the new era [J]. Chin J Parasitol Parasit Dis, 2022, 40(5): 635-641.
[15]李成, 庞维, 曹妍, 等. 免疫学思维的思考及在教学中的应用[J]. 中国免疫学杂志, 2022, 38(24): 3046-3049, 3054.
Li C, Pang W, Cao Y, et al. Immunological thinking and its application in teaching[J]. Chin J Immunol, 2022, 38(24): 3046-3049, 3054.
[16]赵东宇, 王宪. 人工智能与生物医学[J]. 生理科学进展, 2025, 56(3): 199-201.
Zhao D Y, Wang X. Artificial intelligence and biomedicine[J]. Prog Physiol Sci, 2025, 56(3): 199-201.
[17]朱雪波, 刘建明. 人工智能时代医学人文面临的挑战与机遇[J]. 浙江社会科学, 2025(7): 113-116, 158.
Zhu X B, Liu J M. The challenges and opportunities facing medical humanities in the AI era[J]. Zhejiang Soc Sci, 2025(7): 113-116, 158.

基金资助

^*吉林大学白求恩医学部教育教学改革建设项目(YXB2025JGY033);吉林大学研究生教学改革项目(2024JGZ027);吉林大学教学改革项目(24AI087Y)