在一个大型的介绍生物医学和健康信息学课程中,对生成人工智能的结果和影响
William Hersh1, Kate Fultz Hollis2
1Department of Medical Informatics & Clinical Epidemiology, School of Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd. BICC, Portland, OR, USA. hersh@ohsu.edu.
NPJ digital medicine
|September 13, 2024
概括
生成型人工智能 (AI) 系统或大型语言模型 (LLM) 的性能与生物医学信息学课程的学生相比较. 他们的分数在第50至第75百分位之间,影响了教育评估策略.
科学领域:
- 生物医学和健康信息学
- 教育中的人工智能
- 高等教育评估 高等教育评估
背景情况:
- 生成型人工智能 (AI) 在各种生物医学任务中表现出熟练.
- 关于直接比较人工智能表现与高等教育学生的研究有限.
- 评估AI在学术环境中的作用对于不断发展的教育实践至关重要.
研究的目的:
- 评估最先进的大型语言模型 (LLM) 与学生知识评估分数的性能.
- 为了比较LLM的表现与学生在一个入门的生物医学和健康信息学课程.
- 分析LLM绩效对在线知识型课程中传统学生评估方法的影响.
主要方法:
- 六个领先的LLM系统被要求回答多项选择题 (MCQ) 和最终考试问题.
- 收集了139名学生 (研究生,继续教育和医学学生) 的学生分数.
- 在统计学上,LLM产生的分数与学生分数的分布进行了比较.
主要成果:
- 所有评估的LLM都取得了学生成绩的第50至75百分位之间.
- 这种表现在MCQ和最终考试问题中是一致的.
- 法学士的知识水平与该课程中学生的中等水平相当.
结论:
- 法学士的表现表明,需要重新评估高等教育当前的学生评估策略.
- 基于知识的和在线课程特别容易受到人工智能驱动工具的影响.
- 进一步的研究是有必要的,以开发强大的评估方法在先进的人工智能时代.
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