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Updated: Sep 10, 2025

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Using Learning Outcome Measures to assess Doctoral Nursing Education
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在护理教育主题分析中比较人类洞察力和人工智能
Alison H Davis1, Gloria Giarratano1, Tina Gunaldo2
1From School of Nursing, Louisiana State University Health New Orleans, New Orleans, Louisiana.
The Journal of nursing education
|August 22, 2025
概括
大型语言模型可以有效地分析跨专业教育的反思, 但人类的监督至关重要. 目前的人工智能产出与人类分析不同,需要综合评估方法来评估学习.
科学领域:
- 医疗专业教育
- 教育中的人工智能
- 定性研究方法
背景情况:
- 专业间教育 (IPE) 的进步受到资源密集型学生学习评估的阻碍.
- 在IPE中,反思性任务鼓励学习的自我检查和未来的专业间合作.
- 手动分析反射分配是耗时的,限制了可扩展性.
研究的目的:
- 将人类主导的编码和主题与大型语言模型 (LLM) 产生的输出进行比较,以评估跨专业的反思.
- 评估LLM在分析IPE反射分配的定性数据中的有效性和局限性.
主要方法:
- 定性描述性研究设计.
- 传统的人类编码和主题与LLM生成的编码和主题的比较.
- 评估的重点是学生的专业间反思.
主要成果:
- 目前,LLM产生的反射主题定性分析的输出与人类团队产生的输出不相同.
- 在教育研究中,LLM显示了对大数据集的有效审查的潜力.
结论:
- 在教育环境中,LLM提供了一种高效的大规模数据审查的方法.
- 目前的LLM具有局限性,不应仅仅取代人类分析.
- 建议将LLM与其他评估方法相结合,以全面评估跨专业反思.
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