Purpose:Generative Artificial Intelligence (GAI) models, such as ChatGPT, may inherit or amplify societal biases due to their training on extensive datasets. With the increasing usage of GAI by students, faculty, and staff in higher education institutions (HEIs), it is urgent to examine the ethical issues and potential biases associated with these technologies. Design/Approach/Methods:This scoping review aims to elucidate how biases related to GAI in HEIs have been researched and discussed in recent academic publications. We categorized the potential societal biases that GAI might cause in the field of higher education. Our review includes articles written in English, Chinese, and Japanese across four main databases, focusing on GAI usage in higher education and bias. Findings:Our findings reveal that while there is meaningful scholarly discussion around bias and discrimination concerning LLMs in the AI field, most articles addressing higher education approach the issue superficially. Few articles identify specific types of bias under different circumstances, and there is a notable lack of empirical research. Most papers in our review focus primarily on educational and research fields related to medicine and engineering, with some addressing English education. However, there is almost no discussion regarding the humanities and social sciences. Additionally, a significant portion of the current discourse is in English and primarily addresses English-speaking contexts. Originality/Value:To the best of our knowledge, our study is the first to summarize the potential societal biases in higher education. This review highlights the need for more in-depth studies and empirical work to understand the specific biases that GAI might introduce or amplify in educational settings, guiding the development of more ethical AI applications in higher education.

翻译：目的：生成式人工智能模型（如ChatGPT）因其在广泛数据集上的训练，可能继承或放大社会偏见。随着高等教育机构中的学生、教职员工对GAI的使用日益增加，审视与这些技术相关的伦理问题及潜在偏见已刻不容缓。设计/方法/途径：本范围综述旨在阐明近期学术出版物中如何研究和讨论高等教育机构中与GAI相关的偏见。我们对GAI在高等教育领域可能引发的潜在社会偏见进行了分类。综述涵盖四个主要数据库中英文、中文和日文撰写的文章，重点关注高等教育中的GAI使用与偏见问题。研究发现：我们的研究显示，尽管人工智能领域围绕大语言模型的偏见与歧视存在有意义的学术讨论，但涉及高等教育的文章大多仅浅层探讨该问题。极少文章能识别不同情境下的具体偏见类型，且实证研究明显不足。综述中的论文主要聚焦医学与工程相关的教育和研究领域，部分涉及英语教育，但几乎未涵盖人文社会科学。此外，当前讨论大多以英语进行且主要针对英语语境。原创性/价值：据我们所知，本研究首次系统总结了高等教育中的潜在社会偏见。本综述强调需要开展更深入的实证研究，以理解GAI在教育环境中可能引入或放大的具体偏见，从而指导高等教育领域开发更符合伦理的人工智能应用。