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Related Experiment Video

Updated: Jan 13, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Developing a Nursing Research Education Agent Using Knowledge Graphs and Large Language Models: A Proof-of-Concept

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).

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PubMed
Summary

A new Nursing Research Education Agent, combining knowledge graphs (KGs) and large language models (LLMs), shows promise for improving nursing students' research and statistical skills. Educators found it pedagogically sound and effective.

Keywords:
AI agententrustable professional activitiesknowledge graphlarge language modelnursing research

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Area of Science:

  • Nursing Education
  • Artificial Intelligence in Healthcare
  • Educational Technology

Background:

  • Knowledge graphs (KGs) and large language models (LLMs) offer significant potential for advancing nursing education, particularly in research methodology and statistical literacy.
  • A proof-of-concept study developed a specialized agent to aid nursing learners in grasping research designs and statistical concepts.

Purpose of the Study:

  • To assess the feasibility and pedagogical suitability of the developed agent within a nursing education setting.
  • To evaluate the artificial intelligence (AI) performance of the agent using established assessment tools.

Main Methods:

  • The agent integrated structured KGs of nursing research knowledge with LLMs to provide interactive, natural language responses.
  • Ten nursing educators evaluated the agent using the Pedagogical Fit Evaluation Scale and the AI Performance Evaluation Scale.

Main Results:

  • Educators reported high ratings for pedagogical fit (M=4.20, SD=0.63) and AI performance (M=4.10, SD=0.56).
  • Positive feedback highlighted the agent's clinical relevance, accuracy, and its ability to foster critical thinking skills among students.
  • The integration of the agent into nursing curricula was considered feasible.

Conclusions:

  • Agents that integrate KGs and LLMs demonstrate considerable potential for enhancing nursing research education.
  • Further research and large-scale trials are recommended to fully explore the capabilities and impact of these integrated agents.