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Updated: Jun 19, 2025

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数字墨水和外科梦:在住院申请中对人工智能生成的论文的感知
Loralai M Crawford1, Peter Hendzlik2, Justine Lam1
1Department of Biomedical Engineering, University of Rochester, Rochester, New York.
The Journal of surgical research
|July 23, 2024
概括
一般外科的教师无法可靠地区分人工智能 (AI) 产生的文章和人类写的文章. 尽管质量相似,但假定的AI论文得分较低,这表明学术评估可能存在偏见.
科学领域:
- 医学教育 医学教育
- 在学术界的人工智能.
- 学术诚信 学术诚信
背景情况:
- 像ChatGPT这样的大型语言模型 (LLM) 在学术写作中越来越多地被使用.
- 教师们对人工智能产生的内容的使用存在担忧,有些人将其视为学术不诚实.
- 这项研究调查了一般外科住院教师区分人工智能生成和人类写作响应的能力.
研究的目的:
- 为了确定一般外科系是否可以可靠地检测人工智能生成的文本.
- 评估教师是否能够区分人类和人工智能撰写的论文.
- 在教师对人工智能产生的内容的评估中识别潜在的偏见.
主要方法:
- 使用通用外科住院申请提示符生成了10篇文章 (5篇由当前实习生,5篇由ChatGPT).
- 十个盲目的教师评审员以10点的利克特级别对面试的可取性,相关性和整体印象进行了评估.
- 评论员还试图将文章识别为人工智能或人类生成的文章,并比较了错误率和分数.
主要成果:
- 在人工智能生成和人类撰写的论文之间,总体得分没有显著差异.
- 教师审查员无法可靠地区分人工智能和人类撰写的论文,两者的识别错误率都很高.
- 假定是人工智能生成的文章总体印象得分较低,这表明潜在的偏见.
结论:
- 人类和人工智能生成的论文具有可比的质量,挑战了人工智能本质上是劣等的概念.
- 教师们在努力可靠地检测人工智能生成的内容,这表明目前的检测方法是不够的.
- 对被认为是人工智能产生的工作的偏见可能会影响评估,需要重新评估人工智能在学术环境中的作用和潜在的学术不诚实政策.
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