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

Abstract

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

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.

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References 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.

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

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