Measuring Artificial Intelligence Integration in Higher Education: A Bibliometric Analysis of Quantitative Studies

Abstract

This study explores the current state of Artificial Intelligence (AI) adoption in higher education, evaluating its scope via bibliometric methods. The research builds upon the knowledge acquired from quantitative studies and establishes guidance for future studies. A total of 24 publications from the combined database of Scupos and Web of Science (WOS) were collected and used as the resource for the bibliometric analysis. The bibliometric analysis using Biblioshiny identified seven indicators, including annual publications, the top 10 contributing countries, the most relevant sources, a thematic map, motor and niche themes, emerging or declining themes, and basic themes. In addition, for the keyword analysis, the authors used the VOSviewer, which identified three clusters: pedagogy, AI tools, and ethics. As a result, the paper provides an improved understanding of AI adoption in education and a framework that includes both students’ and educators’ perspectives on the measures and quantitative research in AI utilization in education. Such knowledge not only provides significant information on the current state of literature and trends but also implications for educators, administrators, and educational technology (EduTech) suppliers.

Keywords

• AI Adoption
• Education
• Students
• Educators
• Bibliometric Analysis

References

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