arXiv subjects

Lilan Chen

Publications and source records attributed to Lilan Chen.

2 recordsLinked to original sources

arXiv2025

A Framework for Developing University Policies on Generative AI Governance: A Cross-national Comparative Study

As generative AI (GAI) becomes increasingly embedded in higher education, universities worldwide are developing policies to govern its ethical, pedagogical, and institutional use. However, these policies vary across national and institutional contexts. We undertake a cross-nationalanalysis of GAI guidelines issued by leading universities in the United States, Japan, and China, identifying key policy orientations and proposing a structured framework to support policy development. Using an extended Technology Acceptance Model as an analytical lens, we examine five domains Perceived Usefulness and Perceived Ease of Use, Perceived Risk, Facilitating Conditions, Social Influence, and Self-Efficacy, and identify 20 key themes through thematic coding. Together, these findings inform the development of the University Policy Development Framework for Generative AI (UPDF-GAI). U.S. universities emphasize faculty autonomy, practical application, and policy adaptability, reflecting environments shaped by cutting-edge research and peer collaboration. The Japanese universities analyzed adopt a more government-aligned approach, prioritizing ethics and risk management, but offering comparatively limited guidance on AI implementation and flexibility. The Chinese universities in the sample reflect a centralized, government-led model, focusing on technology application rather than early policy formulation, while actively exploring GAI integration in education and research. Based on these insights, the study proposes the UPDF-GAI, integrating technological, organizational, and social dimensions of policy formation. The framework provides a structured approach for universities to assess policy priorities, navigate tensions between innovation and risk, and strengthen institutional capacity for sustainable GAI governance, contributing to the evolving discourse on AI governance in higher education.

cs.CY↗

arXiv2023

Potential Societal Biases of ChatGPT in Higher Education: A Scoping Review

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.

cs.CY↗