在32个大学课程中,对对话式人工智能的感知,性能和检测能力
Hazem Ibrahim1, Fengyuan Liu1, Rohail Asim1
1Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
Scientific reports
|August 24, 2023
概括
像ChatGPT这样的大型语言模型在许多课程中表现与大学生相比. 目前的人工智能检测器很难识别ChatGPT的使用情况,学生越来越多地采用它,而教育工作者则将其视为抄袭.
科学领域:
- 教育中的人工智能
- 学术诚信 学术诚信
- 自然语言处理自然语言处理.
背景情况:
- 像ChatGPT这样的大型语言模型 (LLM) 可以生成类似人类的文本,这引发了对学术完整性的担忧.
- 法学士对学生评价和学生和教育工作者的观点的影响在很大程度上是未知的.
研究的目的:
- 为了比较ChatGPT的表现与各个学科的大学生.
- 评估人工智能分类器对ChatGPT生成的文本的检测能力.
- 调查学生和教育工作者对使用ChatGPT进行学术工作的看法.
主要方法:
- 聊天GPT与32个大学课程的学生的性能比较.
- 评估AI文本分类器检测ChatGPT输出的能力.
- 全球和机构调查学生和教育工作者对ChatGPT使用的态度.
主要成果:
- 在许多课程中,ChatGPT的表现与学生的表现相当或超过.
- 人工智能文本分类器在检测ChatGPT使用时不可靠,具有假阳性和通过文本编辑容易逃避.
- 越来越多的学生赞成使用ChatGPT,而教育工作者越来越多地认为这是抄袭.
结论:
- 聊天GPT对传统的学术诚信框架提出了重大挑战.
- 目前的检测方法不足,需要就AI在教育中的整合进行政策讨论.
- 了解和解决AI在学术界不断变化的局面至关重要.
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