  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.
