基于证据的护理教育的大型语言模型驱动反射AI代理:设计和评估
Shuqi Yang1, Manfei Shi1, Yuhang Qian2
1School of Nursing, Fudan University, Shanghai, China.
Nurse education in practice
|January 11, 2026
概括
一个新的基于证据的护理专家 (EBN-Expert) 系统在护理教育评估中显著优于一般的大型语言模型. 这种人工智能工具增强了反思性学习,并支持护理学生的基于证据的实践培训.
科学领域:
- 教育中的人工智能
- 护理教育 技术 技术
- 域特定的人工智能
背景情况:
- 基于证据的实践 (EBP) 在护理中至关重要,但对学生来说很难掌握.
- 传统的方法努力发展EBP的批判性思维.
- 一般的大型语言模型 (LLM) 为EBP的特定需求提供了有限的支持.
研究的目的:
- 开发和评估基于证据的护理专家 (EBN-Expert),一个专门的AI代理.
- 在以证据为基础的护理教育中支持反思性学习.
- 评估EBN-Expert的业绩与一般的LLM相比.
主要方法:
- 一项使用标准化护理考试问题的比较评估研究.
- 基于基于证据的护理教科书内容开发了EBN-Expert.
- 使用124个测试项目,比较了EBN-Expert与ChatGPT-o1,DeepSeek-R1和Kimi.
主要成果:
- EBN-Expert显著优于一般的LLM (P<0.001),获得了最高分数.
- 在方法论和解释性推理问题上,EBN-Expert表现出色.
- 在真假问题上表现非常准确,在多选题问题上表现强.
结论:
- 像EBN-Expert这样的特定领域的人工智能工具对护理教育显示出希望.
- EBN-Expert的课程调整和准确性支持EBP培训.
- 该系统提供了一个可扩展和值得信赖的方法来推进护理教育.
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