AI in higher education: A bibliometric analysis, synthesis, and a critique of research

Abstract

To better characterize and understand AI in higher education and its role in relation to educational disparities and inclusivity, this paper presents a comprehensive bibliometric assessment of research on AI in higher education. Using quantitative topic modeling and qualitative analysis methods, this study describes: (1) the research landscape of AI in higher education and (2) the common topics of AI in higher education research, including topics related to inclusive education. Based on these descriptions, this study offers a synthesis and critique of research on AI in higher education on the following issues: (a) the use of AI to address educational disparities and foster inclusivity, (b) the ethics of AI-powered large language learning models and translation tools, and (c) AI literacy. The findings of this study call on higher education scholars/researchers to reaffirm higher education research and educational mission, and the standards of rigorous research to lead the knowledge on AI. • A comprehensive bibliometric assessment of the research on AI in higher education, beyond the classic design of a bibliometric analysis. • A synthesis and critique of research on AI in higher education on the following issues: (a) the use of AI to address educational disparities and foster inclusivity, (b) the ethics of AI-powered large language learning models and translation tools, and (c) AI literacy. • A call to action for higher education scholars to reaffirm higher education research and educational mission, and the standards of rigorous research to lead the knowledge on AI.

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