使用知识图表和大型语言模型开发护理研究教育代理:一项概念验证研究
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).
Nurse educator
|January 12, 2026
概括
一个新的护理研究教育代理,结合知识图 (KGs) 和大语言模型 (LLMs),显示了改善护理学生的研究和统计技能的希望. 教育工作者发现它在教学上是合理和有效的.
科学领域:
- 护理教育 护理教育
- 医疗保健中的人工智能
- 教育技术的教育技术
背景情况:
- 知识图 (KG) 和大型语言模型 (LLM) 为推进护理教育提供了巨大的潜力,特别是在研究方法和统计素养方面.
- 一项概念验证研究开发了一种专门的代理来帮助护理学习者掌握研究设计和统计概念.
研究的目的:
- 评估开发的代理在护理教育环境中的可行性和教学适用性.
- 使用已建立的评估工具评估代理人的人工智能 (AI) 性能.
主要方法:
- 代理人将护理研究知识的结构化KG与LLM集成在一起,以提供交互式的自然语言响应.
- 十名护理教育工作者使用教学适应性评估量表和人工智能绩效评估量表来评估该药物.
主要成果:
- 教师报告了教学适应性 (M=4.20,SD=0.63) 和人工智能表现 (M=4.10,SD=0.56) 的高评分.
- 积极的反强调了该药物的临床相关性,准确性,以及它在学生中培养批判性思维能力的能力.
- 将该药物整合到护理课程中被认为是可行的.
结论:
- 整合KG和LLM的代理人证明了提高护理研究教育的巨大潜力.
- 建议进行进一步的研究和大规模试验,以充分探索这些综合剂的功能和影响.
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