比较生成人工智能平台和护理学生在韩国妇女健康护理考试中的表现:拉什模型方法
Eun Jeong Ko1, Tae Kyung Lee1,2, Geum Hee Jeong1
1School of Nursing and Research Institute in Nursing Science, Hallym University, Chuncheon, Korea.
Journal of educational evaluation for health professions
|September 26, 2025
概括
生成型人工智能 (AI) 平台在护理考试中表现不同,一些与高成绩学生相匹配. 拉什模型有效地评估了未来教育评估的AI能力.
科学领域:
- 护理教育 护理教育
- 人工智能的人工智能
- 心理测量 心理测量 心理测量
背景情况:
- 生成型人工智能 (AI) 越来越多地融入教育环境.
- 使用标准化评估来评估人工智能的表现对于理解人工智能在护理等专业领域的能力至关重要.
研究的目的:
- 为了比较生成人工智能平台对护理学生在妇女健康护理考试的能力参数估计.
- 用拉什模型估计项目难度参数并评估不同难度级别的AI性能.
主要方法:
- 对111名护理学生和6个生成AI平台进行了50项女性健康护理考试.
- 用拉什模型分析了答案,以估计能力和难度参数.
- 使用DETECT证实了单维性,使用R包irtQ和TAM进行了分析.
主要成果:
- 考试表明了单维性,项目难度从 - 3.87 到 1.96 逻辑.
- 顶级的人工智能平台 (GPT-4o,ChatGPT Free,Claude.ai) 超过了学生的平均能力,而其他平台得分更低.
- 测试信息曲线表明适合低到平均能力的受试者.
结论:
- 先进的生成人工智能平台表现出与高成绩护理学生相当的性能,尽管具有变化性.
- 拉什模型在评估护理教育中的AI能力方面被证明是有效的.
- 人工智能绩效评估有可能在未来用于护理教育.
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