十二个技巧,以利用人工智能高效和有效的医疗问题生成:教师使用聊天GPT使用的指南
Inthrani Raja Indran1, Priya Paranthaman1, Neelima Gupta1
1Department of Pharmacology, National University of Singapore, Yong Loo Lin School of Medicine, Singapore, Singapore.
Medical teacher
|December 26, 2023
概括
像ChatGPT这样的大型语言模型可以生成医学评估问题,提高教师的生产力和学生的学习. 人类专业知识对于确保人工智能产生的医学教育内容的准确性和质量至关重要.
科学领域:
- 医学教育 医学教育
- 人工智能的人工智能
背景情况:
- 医学教育需要高质量的评估问题,这是一个耗时且依赖于专业知识的过程.
- 大型语言模型 (LLM) 为产生这些问题提供了潜在的创新解决方案.
研究的目的:
- 探索ChatGPT在生成多样化,高质量的医疗评估问题,特别是多项选择题 (MCQ) 中的实用性.
- 通过人工智能辅助的问题生成,提高教育工作者的生产力,并促进学生的自主学习.
主要方法:
- 使用了12个策略来指导ChatGPT生成评估问题.
- 确保与布鲁姆的分类学和核心知识领域保持一致.
- 在评估设计中推广最佳实践.
主要成果:
- 证明了ChatGPT能够生成与教育目标一致的评估问题的能力.
- 展示了人工智能在医学评估中增强,而不是取代人类专业知识的潜力.
结论:
- 像ChatGPT这样的LLM工具可以帮助创建高标准的医疗评估问题.
- 主题专家对于确保医疗教育中人工智能产生的内容的最终质量和准确性至关重要.
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