The Evolving Role of AI in Higher Education Technology:A Research Mapping through Bibliometrics

Abstract

This study aims to examine the domain of Artificial Intelligence and Higher Education (AI-HE) through an analysis of papers and documents indexed in Scopus from 2000 to 2023. Consequently, we examined the publication trends, identified particularly significant articles, and analyzed the most active players and funding institutions in the domain of AI-HE research. A bibliometric analysis was conducted in the domain of artificial intelligence and higher education research to examine the network of co-authorships, keywords, and citations. A total of 2,736 published documents related to AI-HE were identified from the search conducted on Scopus. The analysis of trends indicated that the volume of publications surged by around 42,450% from 2000 to 2023. Conference papers are the most commonly utilized in documents, comprising 1,430 publications, or 52.3% of the total. The Lecture Notes in Computer Science, Including Subseries The primary source for published materials is Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, which accounts for 116 works on the topic. Salas Rueda is the preeminent researcher in artificial intelligence within higher education, having authored nine papers. Tecnologico de Monterrey is the institution that has generated the highest volume of published documents. Funders, notably the National Science Foundation, the foremost funding institution in the United States, which has contributed to 42 publications, are primarily accountable for the creation of the nation actively involved in the subject area. A comprehensive analysis of keyword co-occurrence revealed six principal study fields that contain the essential tools, concepts, approaches, and the socioeconomic and financial aspects of artificial intelligence in higher education (AI-HE). Future research in artificial intelligence within higher education will concentrate on deep learning, machine learning, and neural network algorithms. The objective of this study is to precisely forecast the learning patterns of students.

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