A Bibliometric Analysis of AI-Driven Performance Prediction in Higher Education

Abstract

This study presents a comprehensive bibliometric analysis of research publications on artificial intelligence (AI) applications in higher education, with a particular focus on student performance-related studies. Drawing on 1431 documents retrieved from the Web of Science (WoS) Core Collections, advanced bibliometric tools, such as VOSViewer (1.6.19) and Biblioshiny, were used to explore research trends, citation networks, co-authorship patterns, and keyword co-occurrences. The results revealed an increase in AI-related educational research during the COVID-19 pandemic, reflecting the rise in the reliance on AI for enhancing learning outcomes in times of educational disruption. The findings also indicated that the research focus is gradually moving towards using AI for educational assessment, emphasizing the importance of accurate and data-driven evaluation of student performance. The co-occurrence of keywords and citation analyses confirmed that machine learning, deep learning, and predictive modeling are among the dominant AI techniques applied to assess and predict student outcomes. Furthermore, the study highlights AI’s potential in identifying learning gaps and enabling personalized interventions, allowing educators to address students’ specific needs more effectively. This new trend suggests a growing recognition of AI’s role in refining educational methodologies and improving performance evaluations at the tertiary level.

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