拥抱解释深度的幻觉:使用代提示的战略框架,在医疗保健教育中整合大型语言模型
1Cleveland Clinic Lerner College of Medicine of Case Western Reserve University.
Medical teacher
|July 26, 2024
概括
医疗保健教育工作者可以使用大型语言模型 (LLM) 的代提示来培养批判性思维. 这种方法有助于学生积极参与AI,防止过度依赖并揭示LLM的局限性.
科学领域:
- 教育技术的教育技术
- 认知科学 认知科学
- 教育中的人工智能
背景情况:
- 医疗保健教育工作者正在将大型语言模型 (LLM) 整合到课程中.
- 有关LLM对学生认知发展和批判性思维能力的潜在负面影响存在担忧.
- 解释性深度错觉 (IoED) 描述了人们高估自己理解的倾向,这种倾向往往因依赖外部信息来源而加剧.
研究的目的:
- 提出将LLMs纳入医疗保健教育的方法,以减轻学生过度依赖的风险.
- 利用IoED的认知偏见和代提示来增强积极的学习和对LLM成果的批判性评估.
- 鼓励学生积极参与法学士课程,识别其缺点,并发展更深入的内化知识.
主要方法:
- 利用代提示作为学生与LLMs互动的策略.
- 实施基于模板的分配结构,鼓励学生对LLM互动进行反思.
- 应用解释性深度幻觉的原则来指导学习过程.
主要成果:
- 代提示可以暴露LLM的不准确和不一致.
- 学生可以通过批判性地评估人工智能生成的内容来更现实地了解他们的知识深度.
- 拟议的方法支持主动学习,并降低了被动接受LLM答案的风险.
结论:
- 将LLM纳入医疗保健教育是可行的,使用促进关键参与的策略.
- 由IoED提供的代提示提供了一种可行的方法,以减轻与LLM使用相关的认知风险.
- 这种教学策略使学生成为积极的学习者,批判性地评估人工智能工具并加深他们自己的理解.
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