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

doi:10.3761/j.issn.1672-9234.2026.08.006

中华护理教育 ›› 2026, Vol. 23 ›› Issue (8): 938-943.doi: 10.3761/j.issn.1672-9234.2026.08.006

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

于子莹(), 王晓梅, 刘盼, 孟庆慧^*()

山东第二医科大学护理学院山东省潍坊市 261053

收稿日期:2026-01-22 出版日期:2026-08-15 发布日期:2026-08-14
通讯作者: *孟庆慧，E-mail：hui_m12@163.com
作者简介:于子莹：女，本科（硕士在读），E-mail：1425057829@qq.com
第一联系人：作者贡献声明
研究构思与设计为孟庆慧，资料收集为于子莹、刘盼、王晓梅，论文撰写为于子莹，论文修改和审校为于子莹、刘盼、孟庆慧。
基金资助:
山东省本科高校人工智能赋能重点领域教学改革“111计划”项目(D2024011)

Design and practice of intelligent teaching for the Nursing Theory course driven by AI

YU Ziying(), WANG Xiaomei, LIU Pan, MENG Qinghui^*()

School of Nursing, Shandong Second Medical University， Weifang 261053，Shandong Province， China

Received:2026-01-22 Online:2026-08-15 Published:2026-08-14
Contact: *MENG Qinghui，E-mail：hui_m12@163.com
Supported by:
Shandong Province “111 Plan” Project for Teaching Reform in Key Fields Empowered by Artificial Intelligence at Undergraduate Institutions(D2024011)

摘要/Abstract

摘要：

目的设计并验证基于知识图谱与人工智能（artificial intelligence，AI）工具双核驱动的护理理论课程智慧教学方案，并评估其应用效果。方法选取某校2025级护理硕士研究生（n=48）开展护理理论课程教学改革。课前依托图谱与AI工具进行个性化预习与学情诊断；课中组织进阶研讨与AI思辨辩论，打造“师-生-机”深度互动课堂；课后开展AI辅助批改与个性化反馈。通过学习成绩评价知识掌握度，分别在教学前后采用AI素养量表进行测评，课后辅以半结构访谈（n=6）进行教学效果反馈。结果 2025级学生的学习成绩高于2024级学生，教学后学生的AI素养有所提升，差异均有统计学意义（P<0.001）。访谈共提取出4个主题：双核驱动教学深化护理理论结构化理解；双核驱动教学推动评判性思维发展；双核驱动人机互动提升课堂参与度与思维活跃度；小组分工协作培养合作能力同时存在知识覆盖不均衡局限。结论知识图谱与AI工具双核驱动的智慧教学设计有助于学生深度学习护理理论课程，同时能够有效提升护理硕士研究生AI素养，为护理研究生核心课程的智慧化改革提供了实践参考。

关键词: 护理研究生, 护理理论, 知识图谱, 人工智能, 智慧教学

Abstract:

Objective To design and verify a smart teaching scheme for Nursing Theory courses driven by the dual-core of knowledge graph and AI tools，and evaluate its application effect. Methods A total of 48 nursing postgraduate students of class 2025 in a university were selected to participate in the teaching reform of Nursing Theory course. Before class，personalized preview and academic diagnosis were carried out based on knowledge graph and AI tools； During class，advanced discussion and AI critical thinking debate were organized to build an in-depth interactive classroom of “teacher-student-machine”； After class，AI-assisted marking and personalized feedback were carried out. Learning outcomes were evaluated through academic performance. AI literacy was assessed using the AI Literacy Scale before and after teaching，and semistructured interviews （n=6） were conducted after the course for teaching feedback. Results The academic performance of the 2025 class was higher than that of the 2024 class，and the AI literacy of students improved after teaching，with statistically significant differences（P<0.001）. Four themes were extracted from the interviews：Dual-core driven teaching deepens the structured understanding of nursing theory； Dual-core driven teaching promotes the development of critical thinking； Human-computer interaction in dual-core driven teaching improves classroom engagement and thinking activity； and Group division and cooperation cultivates cooperation ability while also presenting limitations in knowledge coverage imbalance. Conclusion The smart teaching design driven by the dual-core of knowledge graph and AI tools helps students conduct in-depth learning of nursing theory courses，and effectively improves AI literacy of nursing postgraduate students，which provides practical reference for the intelligent reform of core nursing postgraduate courses.

Key words: Nursing Postgraduate Students, Nursing Theory, Knowledge Graph, Artifical Intelligence, Smart Teaching

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.

YU Ziying, WANG Xiaomei, LIU Pan, MENG Qinghui. Design and practice of intelligent teaching for the Nursing Theory course driven by AI[J]. Chinese Journal of Nursing Education, 2026, 23(8): 938-943.

图/表 1

参考文献 12

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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

Design and practice of intelligent teaching for the Nursing Theory course driven by AI

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