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