评估聊天GPT在解决基于生理学核心概念的问题上的有效性
Arijita Banerjee1, Aquil Ahmad2, Payal Bhalla2
1Physiology, Indian Institute of Technology Kharagpur, Kharagpur, IND.
Cureus
|September 13, 2023
概括
在医学生理学推理问题中,ChatGPT表现不同,这表明需要进一步评估用于教育转移学习的AI工具.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 生理学学习 学习生理学学习
背景情况:
- 像ChatGPT这样的大型语言模型 (LLM) 提供类似人类语言技能,但对于复杂的医疗概念可能缺乏专家级的深度.
- 人工智能在医学教育中促进学习转移的能力存在担忧,原因是表面层面的理解.
- 这项研究调查了ChatGPT在解决生理学模块中的核心概念方面的有效性.
研究的目的:
- 探索ChatGPT在解决不同生理学模块的推理问题的有效性.
- 根据生理学的五个核心概念来评估ChatGPT的性能.
- 识别与特定模块和核心概念相关的AI性能变化.
主要方法:
- 82个推理类型的问题由学科专家在六个生理学模块和五个核心概念中开发.
- 聊天GPT对这些问题的答案在第一阶段被收集并得分.
- 克鲁斯卡尔-瓦利斯测试和后期分析被用来比较模块和概念的分数.
主要成果:
- 模块的总体平均得分为3.72 ±0.26,核心概念的平均得分为3.68 ±0.30.
- 在ChatGPT的性能中发现了统计学上显著的差异 (模块p=0.05,核心概念p=0.024).
- 不同的模块和核心概念之间的性能差异很大.
结论:
- 在ChatGPT的性能显著变化突出其不一致的执行跨不同的教育内容.
- 进一步评估人工智能支持的学习应用程序对于加强本科医学教育中的学习转移至关重要.
- 这些发现强调了需要仔细考虑AI工具能力在专业学术领域的需要.
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