EDUCATIONAL TRANSFORMATION: A BIBLIOMETRIC AND SCIENTOMETRIC ANALYSIS OF AI IN HIGHER EDUCATION

Abstract

The usage AI in postsecondary educational settings has attracted a lot of scholarly interest recently. Through bibliometric and scientometric analysis, this study investigates this expanding subject by looking at publication trends, well-known authors and sources, and thematic landscapes. It's possible that the COVID-19 pandemic and the move toward online learning settings hastened the investigation of AI in the classroom. The main aim of this research is to evaluate the current knowledge state in this field. We conducted a bibliometric analysis on papers that we obtained from the extensive academic database Scopus. Relevant search terms like "artificial intelligence," "e-learning," "higher education," and possibly "covid-19" were used in the search strings. After extracting and cleansing the data, co-citation patterns, author productivity, and publishing trends were examined using scientometric approaches. To further illustrate the study landscape and pinpoint burgeoning or waning themes, thematic maps were also created. According to the analysis, there has been a remarkable upsurge in publications on AI in higher education starting in 2020. Keyword research revealed a concentration on the terms "artificial intelligence," "e-learning," "higher education," and "covid-19," indicating a focus on the use of AI in facilitating online learning during the pandemic. A power law distribution compatible with Lotka's Law was found in the authorship study, showing a bigger group of writers with fewer publications alongside a small number of extremely productive authors. The authors and publications that received the most citations were determined, offering insights into significant studies conducted in the area. Bradford's Law was further supported by the study, which identified a core set of journals that publish a high concentration of pertinent articles. Four major thematic clusters were found by thematic map analysis: niche themes (specialized subjects), core themes (important fields like artificial intelligence and higher education), motor themes (advancing research), and perhaps dwindling or developing themes.