Artificial Intelligence in Student Profiling: A Bibliometric Review

Abstract

This study, based on a 17-year dataset (2008-2025) collected from the Scopus database, provides a comprehensive bibliometric analysis of the intellectual structure of research on the use of artificial intelligence in student profiling. Using bibliographic coupling as the primary method, the analysis screened the titles, abstracts, keywords, frameworks, and headings of 661 articles relevant to the field. The study examines the temporal distribution of research outputs, with particular emphasis on trends from the last decade. To visualize the scientific landscape, VOSviewer was employed for mapping co-authorship, keyword co-occurrence, and citation networks. The analysis highlights the most prolific journals, influential authors, dominant subject areas, and frequently used keywords. It also identifies the leading countries and educational institutions contributing to this research domain. The findings provide a detailed overview of the field's development and intellectual trends, offering insights and recommendations for future research directions.