[论文解读] Waiting, Banning, and Embracing: An Empirical Analysis of Adapting Policies for Generative AI in Higher Education

Ping Xiao, Y. Chen+1 more|arXiv (Cornell University)|May 25, 2023

Artificial Intelligence in Healthcare and Education被引用 11

一句话总结

该论文实证分析全球顶尖大学如何建立关于生成式人工智能（如 ChatGPT）的政策，揭示政策的普及程度、内容及驱动因素。

ABSTRACT

Generative AI tools such as ChatGPT have recently gained significant attention in higher education. This study aims to understand how universities establish policies regarding the use of AI tools and explore the factors that influence their decisions. Our study examines ChatGPT policies implemented at universities around the world, including their existence, content, and issuance dates. Specifically, we analyzed the top 500 universities according to the 2022 QS World University Rankings. Our findings indicate that there is significant variation in university policies. Less than one-third of the universities included in the study had implemented ChatGPT policies. Of the universities with ChatGPT policies, approximately 67 percent embraced ChatGPT in teaching and learning, more than twice the number of universities that banned it. The majority of the universities that ban the use of ChatGPT in assessments allow individual instructors to deviate from this restrictive policy. Our empirical analysis identifies several factors that are significantly and positively correlated with a university's likelihood of having a ChatGPT policy, including the university's academic reputation score, being in an English-speaking country, and the general public attitudes toward ChatGPT. In addition, we found that a university's likelihood of having a ban policy is positively associated with faculty student ratio, citations, and the English-speaking country dummy, while negatively associated with the number of peer universities within the same country that have banned ChatGPT. We discuss the challenges faced by universities based our empirical findings.

研究动机与目标

• 调查高校如何就ChatGPT等生成式人工智能工具建立政策。
• 评估全球顶尖高校中 ChatGPT 政策的存在、内容和颁布日期。
• 识别与高校采用或禁止ChatGPT 政策相关的因素。

提出的方法

• 构建了基于2022 QS 排名前500所大学的 ChatGPT 政策存在的经验数据集。
• 对政策内容和颁布日期进行编码手册分析。
• 进行统计分析以识别与政策采用和禁止决策相关的因素。
• 基于回归的评估，分析学术声誉、语言环境与公众态度等因素的关联。

实验结果

研究问题

• RQ1 全球顶尖高校中有多少比例已经制定了 ChatGPT 政策？
• RQ2 常见的政策内容是什么，以及政策是在教学中禁止还是支持 ChatGPT？
• RQ3 哪些高校特征与采用或禁止 ChatGPT 政策相关？

主要发现

• 所研究的高校中不到三分之一制定了 ChatGPT 政策。
• 在有政策的高校中，约 67% 在教学中采纳 ChatGPT，而禁止者数量超过两倍于此。
• 大多数禁止方的高校在实践中允许单个教师偏离禁令。
• 政策采用与学术声誉、英语为主的国家以及公众对 ChatGPT 的态度呈正相关。
• 禁止的可能性与师生比、引用次数、英语为主的国家呈正相关；与同一国家内已经禁止 ChatGPT 的同行高校数量呈负相关。