评估一个大型语言模型的合成健康科学士论文的能力:案例研究
Pål Joranger1, Sara Rivenes Lafontan1, Asgeir Brevik1
1Department of Nursing and Health Promotion, Faculty of Health Sciences, OsloMet - Oslo Metropolitan University, P.O. Box 4 St. Olavs plass, Oslo, N-0130, Norway, 47 67236520.
JMIR formative research
|July 3, 2025
概括
大型语言模型 (LLM) 可以快速生成可信的士论文,挑战学术完整性. 大学应该考虑口头考试来评估学生的真正学习,并防止人工智能产生的工作破坏文凭.
科学领域:
- 卫生科学 卫生科学 卫生科学
- 人工智能的人工智能
- 学术诚信 学术诚信
背景情况:
- 大型语言模型 (LLM) 通过模仿学术写作,对教育机构构成挑战,使学生的工作与人工智能生成的内容难以区分.
- 传统的士论文,作为更高层次学习的证明,受到LLMs生产专家级学术写作能力的威胁.
- 由于LLM的广泛可用性,人们对论文在验证学科理解和学术能力方面的可靠性感到担忧.
研究的目的:
- 评估由大型语言模型 (LLM) 产生的机器生成论文的质量,与健康科学士课程的标准相比.
- 探索法学士在合成研究数据和生成适合士级毕业的学术论文方面的能力.
主要方法:
- 一个探索性案例研究利用ChatGPT (OpenAI) 创建了两个模拟定性和定量健康科学项目的研究论文.
- 该过程涉及促使ChatGPT合成可信的数据集,然后生成论文,需要代对话和快速优化.
- 时间投资包括开发合成数据集 (1.5小时用于定性,16小时用于定量) 和生成论文 (2.5小时用于定性,3.5小时用于定量).
主要成果:
- 在短时间内,ChatGPT成功生成了两个可信的研究论文和相关数据集,模拟了中等质量的士论文.
- 该LLM在合成数据,进行分析和制作能够通过课程标准的精心撰写的学术论文方面表现出熟练.
- 这项研究强调了法学士可以轻松快速地完成与士生相当的学术工作.
结论:
- 由LLM生成的士论文不再可以被认为是学生广泛学习或主题掌握的最终证据.
- 学术标准的完整性和大学文凭的价值受到LLMs复杂产出的挑战.
- 士课程应优先考虑其他评估方法,如口头考试和学校考试,以确保严格评估更高层次的学习.
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