知識グラフと大規模言語モデルを用いた看護研究教育エージェントの開発:概念実証研究
Yingchun Zeng1, Hongxia Xie, Xiaofeng Zhou
1Author Affiliations: Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (Drs Zeng, Jiang, and Lau); School of Computers and Computing Sciences, Hangzhou City University, Hangzhou, China (Ms Xie and Dr Xu); and School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China (Mr Zhou).
Background:
Integration of knowledge graphs (KGs) and large language models (LLMs) holds transformative potential for nursing education, particularly in research methodology and statistical literacy. This proof-of-concept study developed a Nursing Research Education Agent to support learners in understanding research designs and statistical concepts.
Purpose:
To evaluate the agent's feasibility, pedagogical alignment, and AI performance using validated instruments in a nursing education context.
Methods:
The agent combined structured KGs of nursing research knowledge with LLMs for interactive, natural-language responses. Ten nursing educators assessed it using the 10-item Pedagogical Fit Evaluation Scale and 10-item AI Performance Evaluation Scale.
Results:
Educators rated pedagogical fit highly (M = 4.20, SD = 0.63) and AI performance strongly (M = 4.10, SD = 0.56), praising clinical relevance, accuracy, and promotion of critical thinking. Integration into curricula was deemed feasible.
Conclusions:
KG-LLM-integrated agents show strong promise for nursing research education. Further development and larger-scale trials are recommended.
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