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