知识图谱联合人工智能助教在护理专业生理学课程教学中的应用研究

doi:10.3761/j.issn.1672-9234.2026.09.008

中华护理教育 ›› 2026, Vol. 23 ›› Issue (9): 1073-1079.doi: 10.3761/j.issn.1672-9234.2026.09.008

知识图谱联合人工智能助教在护理专业生理学课程教学中的应用研究

张立雪^*(), 杨战利, 金龙

西北民族大学医学部兰州市 730030

收稿日期:2026-03-16 出版日期:2026-09-15 发布日期:2026-09-15
通讯作者: *张立雪，女，博士，讲师，E-mail：1426379458@qq.com
作者简介:第一联系人：作者贡献声明
研究构思与设计为张立雪、杨战利、金龙，数据收集与处理为杨战利、金龙，论文撰写与英文修订为张立雪、杨战利，论文修改和审校为张立雪、金龙。
基金资助:
中央高校基本科研业务费专项资金项目(31920260144)

Teaching practice of integrating knowledge graph and artificial intelligence teaching assistant to empower Physiology teaching for nursing majors

ZHANG Lixue^*(), YANG Zhanli, JIN Long

School of Medicine， Northwest Minzu University， Lanzhou 730030， China

Received:2026-03-16 Online:2026-09-15 Published:2026-09-15
Contact: *ZHANG Lixue，E-mail：1426379458@qq.com
Supported by:
Fundamental Research Funds for the Central Universities(31920260144)

摘要/Abstract

摘要：

目的探讨知识图谱与人工智能助教赋能的教学在护理专业生理学课程中的应用效果。方法便利选取甘肃省某本科院校2023级护理专业120名学生为对照组，2024级护理专业122名学生为试验组；在生理学课程理论教学中，试验组接受知识图谱与人工智能助教赋能的教学，对照组接受传统线上线下结合教学。通过比较两组课程成绩、结课后深度学习、学习积极主动性水平，以及试验组对教学的满意度，评价教学改革成效。结果最终构建包括8个模块、86个核心知识点、318个子知识点，挂载65个视频、40份拓展资源、500余道阶梯式习题的生理学课程知识图谱。试验组课程成绩、深度学习水平与学习积极主动性水平总得分均高于对照组（P<0.05）；试验组中99%的学生认为此次教学改革切实促进了学习成效的提升。结论知识图谱与人工智能助教赋能的教学可有效提升护理专业生理学课程教学效果，促进学生深度学习与主动学习，获得了学生的认可。

关键词: 知识图谱, 人工智能助教, 生理学, 护理学, 教学改革

Abstract:

Objective To explore the application effect of the teaching supported by AI teaching assistants and knowledge graph in the Physiology course for nursing majors. Methods A total of 120 nursing undergraduates of the 2023 cohort from a university in Gansu Province were selected as the control group，and 122 nursing undergraduates of the 2024 cohort as the experimental group. In the Physiology course，the experimental group received teaching under the AI teaching assistant- and knowledge graph-empowered teaching，while the control group adopted the conventional hybrid online-offline teaching mode. The effectiveness of the teaching reform was evaluated by comparing the course scores，deep learning status and learning initiative of the two groups，as well as the teaching satisfaction of students in the experimental group. Results The final knowledge graph consisted of 8 modules，86 core knowledge points，and 318 sub-knowledge points，and was embedded with 65 videos，40 extended learning resources，and more than 500 hierarchical exercises. The total scores of course performance，deep learning and learning initiative in the experimental group were significantly higher than those in the control group（P<0.05）. 99% of participants in the experimental group believed that this teaching reform effectively improved their learning outcomes. Conclusion The teaching empowered by artificial intelligence teaching assistant and knowledge graph can effectively improve the teaching quality of Physiology for nursing majors，facilitate students’ deep learning and active learning，and achieve high recognition among students.

Key words: Knowledge Graph, Artificial Intelligence Teaching Assistant, Physiology, Nursing, Teaching Reform

张立雪, 杨战利, 金龙. 知识图谱联合人工智能助教在护理专业生理学课程教学中的应用研究[J]. 中华护理教育, 2026, 23(9): 1073-1079.

ZHANG Lixue, YANG Zhanli, JIN Long. Teaching practice of integrating knowledge graph and artificial intelligence teaching assistant to empower Physiology teaching for nursing majors[J]. Chinese Journal of Nursing Education, 2026, 23(9): 1073-1079.

图/表 4

参考文献 11

[1]	徐佳, 张俊芳, 徐淑君, 等. 我国生理学教学改革的热点与前沿趋势:基于1990-2020年CNKI的文献计量分析[J]. 中国高等医学教育, 2023(10):64-65.
	Xu J, Zhang JF, Xu SJ, et al. Hotspots and frontiers of physiology teaching reform in China:a bibliometric analysis based on CNKI literature from 1990 to 2020[J]. China High Med Educ, 2023(10):64-65.
[2]	张吉祥, 张祥森, 武长旭, 等. 知识图谱构建技术综述[J]. 计算机工程, 2022, 48(3):23-37. doi: 10.19678/j.issn.1000-3428.0061803
	Zhang JX, Zhang XS, Wu CX, et al. Survey of knowledge graph construction techniques[J]. Comput Eng, 2022, 48(3):23-37. doi: 10.19678/j.issn.1000-3428.0061803
[3]	朱永海, 张佳鑫, 韩锡斌. 基于生成式人工智能的个性化学习新形态[J]. 电化教育研究, 2025, 46(4):58-64.
	Zhu YH, Zhang JX, Han XB. A new form of personalized learning based on artificial intelligence generated content[J]. E Educ Res, 2025, 46(4):58-64.
[4]	李志河, 李思哲, 王元臣, 等. 具身认知环境下大学生深度学习评价量表设计与核验[J]. 电化教育研究, 2020, 41(12):92-98.
	Li ZH, Li SZ, Wang YC, et al. Design and verification of college students’ deep learning evaluation scale under embodied cognition environment[J]. E Educ Res, 2020, 41(12):92-98.
[5]	臧渝梨, 娄凤兰, 陈满辉, 等. 医学及其相关专业大学生学习积极主动性测量工具的研制[J]. 中华护理教育, 2006, 3(1):14-17.
	Zang YL, Lou FL, Chen MH, et al. The measure of active learning state among healthcare undergraduates[J]. Chin J Nurs Educ, 2006, 3(1):14-17.
[6]	饶柳, 王天兰, 曾苹, 等. 知识图谱结合人工智能助教在康复护理学教学中的应用[J]. 护理学杂志, 2025, 40(23):71-75.
	Rao L, Wang TL, Zeng P, et al. Application of knowledge graph combined with artificial intelligence teaching assistant in rehabilitation nursing education[J]. J Nurs Sci, 2025, 40(23):71-75.
[7]	罗媛慧, 毛婷, 李娟, 等. 内科护理学课程知识图谱的构建及应用研究[J]. 中华护理教育, 2025, 22(8):943-948.
	Luo YH, Mao T, Li J, et al. Construction and application of knowledge graph for Internal Medicine Nursing course[J]. Chin J Nurs Educ, 2025, 22(8):943-948.
[8]	宋莉娟, 杜苗, 蔡妤珂, 等. 护理学导论课程知识图谱的构建与应用研究[J]. 中华护理教育, 2024, 21(8):950-955.
	Song LJ, Du M, Cai YK, et al. Design and preliminary application of knowledge graph for the Introduction to Nursing course[J]. Chin J Nurs Educ, 2024, 21(8):950-955.
[9]	张山, 吴瑛. 认知负荷理论在医学教育领域中的应用进展[J]. 中华护理教育, 2023, 20(2):240-244.
	Zhang S, Wu Y. Application of cognitive load theory in teaching design of medical curriculum[J]. Chin J Nurs Educ, 2023, 20(2):240-244.
[10]	Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation,social development,and well-being[J]. Am Psychol, 2000, 55(1):68-78. doi: 10.1037//0003-066x.55.1.68 pmid: 11392867
[11]	柳琴, 张银玲, 张立华, 等. 自我调节学习在本科生护理与法课程设计与教学实践中的应用研究[J]. 中华护理教育, 2025, 22(8):949-955.
	Liu Q, Zhang YL, Zhang LH, et al. Application of self-regulated learning in the curriculum design and teaching practice of Nursing and Law courses for undergraduate nursing students[J]. Chin J Nurs Educ, 2025, 22(8):949-955.

[1]	刘鑫鑫, 赵学荣, 覃芹丹, 范晶晶, 王梦瑶, 李芳, 胡阳, 颜萍. 本科外科护理学智慧课程建设及教学成效研究[J]. 中华护理教育, 2026, 23(9): 1065-1072.
[2]	俞静萱, 冯亚萍, 金莹莹, 陈建萍. 知识图谱赋能的情境化急危重症护理培训平台构建及应用研究[J]. 中华护理教育, 2026, 23(9): 1093-1100.
[3]	于子莹, 王晓梅, 刘盼, 孟庆慧. 人工智能驱动的硕士研究生护理理论课程教学设计与实践研究[J]. 中华护理教育, 2026, 23(8): 938-943.
[4]	张聪, 林汉, 沈月娥, 刘晶晶, 孙若晗, 陈慧楠, 崔莹. “纵横整合·循阶环升”医学教学创新实践[J]. 中华护理教育, 2026, 23(7): 773-777.
[5]	高云, 苏茜, 杜志敏, 周英, 唐秋碧, 廖晓艳. 人工智能赋能护理心理学课程思政教学改革与实践[J]. 中华护理教育, 2026, 23(7): 778-783.
[6]	周莉, 孙圣博, 张松灵, 朴美花, 伦汪洋, 周越洋. 新医科背景下妇产科学课程教学创新实践及启示[J]. 中华护理教育, 2026, 23(7): 784-789.
[7]	谌绍林, 曾芬莲, 陈硕贞, 邵星, 缪小菊, 李金, 杨莉, 宋凌霞, 邓仁丽. “认知链-决策链-价值链”三链协同的外科护理学教学改革与实践[J]. 中华护理教育, 2026, 23(7): 790-796.
[8]	赵雨嫣, 杨卓婧, 赵倩, 牛丽, 曹潇毅, 王菊子. 知识图谱在产科护理临床教学中的构建与应用研究[J]. 中华护理教育, 2026, 23(7): 842-849.
[9]	叶蕾, 庄梦怡, 何瑛, 郭巧红, 喻惠丹, 梁芳, 吕琴, 杨冰香, 郭佳. 护理伦理学智慧课程的建设与教学实践[J]. 中华护理教育, 2026, 23(6): 683-689.
[10]	谢小凤, 李卡, 姚黎明, 卿平, 崔金波, 朱玲, 张凤英. “护理学+管理学”融合课程体系建设及教学实践[J]. 中华护理教育, 2026, 23(6): 713-718.
[11]	王琳, 周滢, 陶幸娟. 场景驱动的老年护理学模拟体验教学课程思政设计与实践[J]. 中华护理教育, 2026, 23(6): 718-723.
[12]	龚媛媛, 黎琼, 黄棋, 欧阳林奇, 高彬. 融入中医药科普实践的中医护理课程改革实践与效果评价[J]. 中华护理教育, 2026, 23(6): 724-731.
[13]	王慧莹, 刘铭萱, 吴芳琴, 赵宇涵, 苗逸群, 郭蓉蓉, 罗园, 吴瑛. 临床护理思维与决策模型在成人护理学课程教学中的应用及成效[J]. 中华护理教育, 2026, 23(5): 517-523.
[14]	郭俊晨, 刘翔宇, 刘高明, 雷欣瑶, 李丽, 陈胜利, 谌永毅. 安宁疗护高级实践护士发展现状及启示[J]. 中华护理教育, 2026, 23(5): 547-553.
[15]	邢秋燕, 李雪洁, 张洪, 孙晓晖, 王丽萍, 王芳. 护理教育学课程思政教学改革与实践[J]. 中华护理教育, 2026, 23(5): 575-580.

知识图谱联合人工智能助教在护理专业生理学课程教学中的应用研究

Teaching practice of integrating knowledge graph and artificial intelligence teaching assistant to empower Physiology teaching for nursing majors

HTML阅读

PDF下载

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 4

参考文献 11

相关文章 15

Metrics

本文评价

推荐阅读 0