From Predictive Analytics to Explainable AI in Higher Education: A Bibliometric Mapping

Ramzi Talmoudi; Jamel Choukir

doi:10.48161/qaj.v6n2a2559

Authors

Ramzi Talmoudi Department of Business Administration, College of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Kingdom of Saudi Arabia. https://orcid.org/0000-0003-3908-8567
Jamel Choukir Department of Business Administration, College of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Kingdom of Saudi Arabia. https://orcid.org/0000-0003-4247-5760

DOI:

https://doi.org/10.48161/qaj.v6n2a2559

Keywords:

Explainable Artificial intelligence, Predictive learning analytics, Student success management, Algorithmic transparency, Bibliometric mapping.

Abstract

As higher education increasingly relies on data-driven decision-making, the use of artificial intelligence (AI) to predict student success and dropout rates is gaining further importance. The primary aim of this study is to understand the evolutionary research trends of data-driven student success management. A PRISMA-based bibliometric analysis of 457 articles is implemented using the R software version 4.5.3 and the bibliometrix package (Version 5.0). Published articles are retrieved from Web of Science Core Collection indexes (SSCI, AHCI, ESCI), covering a three-decade period (1996-2025). The rigorous inclusion/exclusion criteria restricted the dataset to English-language, and peer-reviewed journal articles. Furthermore, co-word analysis and conceptual structure mapping, utilizing the Louvain clustering algorithm, were applied to extract the thematic architecture of the domain. Performance analysis reveals China and U.S. universities as the most influential affiliations shaping the structural volume of this research field, with Applied Sciences emerging as the dominant journal. Additionally, the network analysis indicates an international collaboration rate of 25%. The thematic map reveals that while predictive modeling-based Early Warning Systems (EWS) have matured into Basic Themes, driving Motor Themes included generative artificial intelligence and psychological constructs. Furthermore, Emerging Themes show that explainable artificial intelligence (XAI) is drawing a transformative pathway to the field. Higher education managers should integrate XAI while addressing ethical AI governance. Future higher education data-driven management should focus on building transparent systems leading to the empowerment of human decision-makers.

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