Exploring AI’s Role in Educational Data Mining: A Bibliometric Review of Applications in Learning Analytics
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Abstract
The paper explores how Artificial Intelligence (AI) is influencing educational data mining (EDM) and learning analytics (LA), tracing the trend of publications from the year 2005 to 2025. The study identifies 1,006 academic articles through co-citation and co-word methods to map the intellectual space of the field using VOSviewer and Scopus. The rise of machine learning, predictive modelling, and personalized learning as a research emphasis has been noted by a significant rise in AI-EDM publications since 2020 that point to the digital learning shift during the pandemic. Analysis of co-citation shows that the EDM work of Romero and Ventura intersects the machine learning work of Breiman, and in this context, the evolution of explainable AI, predictive analytics, and early intervention techniques. Co-word analysis reveals three main themes, such as students, data mining, and machine learning, which characterize AI-based educational practices. Thematic clusters refer to the increased attention to adaptive systems, student performance prediction, and AI ethics. The study highlights how a combined strategy of real-time education and ethical teaching methods can assist AI in helping customize learning and inclusivity among students. Nevertheless, the Scopus database and publications in the English language are the limitations of the study, which can omit the global innovations. Future studies are supposed to promote multidisciplinary teams and create emotional AI algorithms with high ethical codes to overcome privacy and equality issues. This paper offers a detailed evaluation of AI as an agent of change in education and will be useful to decision-makers interested in building equitable and data-driven educational systems.
Manuscript received: 24 Apr 2025 | Revised: 26 Feb 2026 | Accepted: 3 Mar 2026 | Published: 31 Mar 2026
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