A Bibliometric Analysis of Artificial Intelligence in Admissions and Administrative Processes in Higher Education

Abstract

The growing demand for university placements has led to inefficiencies and lack of transparency in existing methods. The study aims to explore the underutilization of AI in these processes to improve efficiency, transparency, and accessibility. The methodology involves extracting and cleaning publication data from Scopus, preparing it for analysis using Google Colab, and visualizing relationships between keywords with VOS viewer. Key findings reveal significant research areas, keyword co-occurrence, and collaborative authorship trends in AI applications within HE admissions and administrative processes. The study highlights the importance of AI applications in university management, human factors, employment outcomes, big data utilization, decision support systems, and educational computing infrastructure. The study highlights gaps in the current literature and calls for ethical and methodological rigor, interdisciplinary approaches, and robust AI systems for fairness and transparency. Future research should incorporate diverse data sources, qualitative analysis, and extend the timeframe to capture ongoing developments in AI applications