循证护理课程智慧化建设的实践与成效

doi:10.3761/j.issn.1672-9234.2026.10.010

中华护理教育 ›› 2026, Vol. 23 ›› Issue (10): 1208-1214.doi: 10.3761/j.issn.1672-9234.2026.10.010

循证护理课程智慧化建设的实践与成效

陈琦蓉¹(), 赵爽¹, 张君汝², 孙玫¹, 陈佳睿¹, 黄晓婷¹, 郭佳¹^,^*()

¹ 中南大学湘雅护理学院长沙市 410013
² 新疆医科大学护理学院乌鲁木齐市 830017

收稿日期:2026-06-26 出版日期:2026-10-15 发布日期:2026-10-15
通讯作者: *郭佳，E-mail：guojia621@163.com
作者简介:陈琦蓉：女，博士，副教授，E-mail：qirong.chen@csu.edu.cn
作者贡献声明
研究构思与设计为郭佳、陈琦蓉，数据收集与处理为赵爽、张君汝、孙玫、陈佳睿、黄晓婷，论文撰写为陈琦蓉、赵爽，论文修改和审校为郭佳、张君汝、孙玫、陈佳睿、黄晓婷。
基金资助:
中南大学研究生在线课程建设项目(2025YJSMOOC04)

Development and effectiveness of an AI-enabled course in Evidence-Based Nursing

CHEN Qirong¹(), ZHAO Shuang¹, ZHANG Junru², SUN Mei¹, CHEN Jiarui¹, HUANG Xiaoting¹, GUO Jia¹^,^*()

¹ Xiangya School of Nursing， Central South University， Changsha 410013， China
² School of Nursing， Xinjiang Medical University， Urumqi 830017， China

Received:2026-06-26 Online:2026-10-15 Published:2026-10-15
Contact: *GUO Jia，E-mail：guojia621@163.com
Supported by:
Graduate Online Course Construction Project of Central South University(2025YJSMOOC04)

摘要/Abstract

摘要：

目的建设人工智能（artificial intelligence，AI）赋能的循证护理理论与实践智慧课程，并探讨其初步运行成效。方法按“通用知识点+加强知识点”的分层化教学内容体系，建立课程知识图谱，在此基础上，融入问题链、能力目标及课程思政，并整合微课视频、AI助教等教学资源，搭建循证护理理论与实践智慧课程平台。采用便利抽样法，选取湖南省某高校2025级全体（n=188）护理硕士生作为教学对象，对其实施“课前知识图谱导学—课中重难点强化与项目研讨—课后AI实践任务巩固”的混合式教学。通过学生学习行为数据、课程总评成绩等评价教学效果。结果课程知识图谱共172个节点，关联资源184份，其中视频41个（共391 min）、文档73份、题目70道。以学术型护理硕士生（n=64）为例，任务点完成率达93.8%，视频观看时长均值为317 min；中位课程总评成绩为91.00分。结论循证护理理论与实践智慧课程的建设与运行有助于保障学生的在线学习主动积极性，助力学生循证护理知识的掌握，可为护理研究生科研方法学课程的数字化建设提供实践参考。

关键词: 循证护理学, 研究生教育, 智慧课程, 知识图谱, 人工智能助教

Abstract:

Objective To develop an artificial intelligence（AI）-enabled course in “Evidence-Based Nursing：Theory and Practice” and explore its preliminary operational effectiveness. Methods Following a hierarchical teaching content system of “general knowledge points + enhanced knowledge points”，a course knowledge graph was constructed. On this basis，problem chains，competency goals，and ideological and political education were integrated，along with teaching resources such as micro-lecture videos and AI teaching assistants，to build a platform for the AI-enabled course of “Evidence-Based Nursing：Theory and Practice”. Using convenience sampling，all 188 nursing master’s students of the class of 2025 from a university were selected as participants and received a blended teaching approach of “pre-class knowledge graph-guided learning，in-class reinforcement of key and difficult points together with project-based seminar，post-class consolidation via AI practical assignments”. Teaching effectiveness was evaluated through students’ learning behavior data and overall course grades performance. Results The course knowledge graph included 172 nodes and 184 associated resources，including 41 videos（totaling 391 minutes），73 documents，and 70 test questions. Taking academic nursing master’s students（n=64） as an example，the task completion rate was 93.8%，the mean video viewing time was 317 minutes，and the median overall course grade was 91.00 points. Conclusion The construction and operation of the AI-enabled course of “Evidence-Based Nursing：Theory and Practice” help ensure students’ active engagement in online learning，facilitate mastery of evidence-based nursing knowledge，and provide practical references for the digital development of nursing graduate methodology courses.

Key words: Evidence-Based Nursing, Postgraduate Education, Artificial-enabled Course, Knowledge Graph, Artificial Intelligence Teaching Assistant

陈琦蓉, 赵爽, 张君汝, 孙玫, 陈佳睿, 黄晓婷, 郭佳. 循证护理课程智慧化建设的实践与成效[J]. 中华护理教育, 2026, 23(10): 1208-1214.

CHEN Qirong, ZHAO Shuang, ZHANG Junru, SUN Mei, CHEN Jiarui, HUANG Xiaoting, GUO Jia. Development and effectiveness of an AI-enabled course in Evidence-Based Nursing[J]. Chinese Journal of Nursing Education, 2026, 23(10): 1208-1214.

图/表 2

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循证护理课程智慧化建设的实践与成效

Development and effectiveness of an AI-enabled course in Evidence-Based Nursing

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