A Scientometric Study of AI Applications in Higher Education: Trends and Computational Challenges

Abstract

This study looks into the subject of Artificial Intelligence and Higher Education (AI-HE) by looking at the articles and documents that have been indexed in Scopus from 2000 to 2023. So, we looked into the patterns of publication, the articles that stood out as being particularly important, and the participants and funding institutions that were most engaged in the field of AI-HE research. A bibliometric study was done in the topic of artificial intelligence and higher education research to look at the connections between co-authorships, keywords, and citations. The search on Scopus turned up 2736 published documents that had to do with AI-HE. Also, looking at the patterns showed that the number of publications had gone up by more than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$42,450 {\%}$</tex> between 2000 and 2023. Conference papers are the most common type of document, with 1430 publications, or 52.3% of the total. The Computer Science Lecture Notes, with Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics are the two most prominent sources for published content. They are responsible for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 1 6}$</tex> papers on the subject. Salas Rueda has published nine studies, making him the most productive researcher in the field of artificial intelligence in higher education. Tecnologico de Monterrey has also published the most documents. Funders, especially the National Science Foundation, which is the largest funding organization in the United States and has helped with 42 publications, are mostly responsible for the production of the country that is actively involved in the subject. A thorough study of the co-occurrence of keywords revealed that there are six main research areas that cover the basic tools, ideas, techniques, and socioeconomic and financial aspects of artificial intelligence in higher education (AI-HE). Researchers will look at deep learning, machine learning, and neural network algorithms in the future when it comes to artificial intelligence in higher education. The objective of this study will be to make accurate guesses about how students learn.

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