Artificial Intelligence in the Era of Educational Big data: A Systematic Review

In this paper, a systematic review of the latest research on the deployment of AI in educational Big data analytics is provided including its applications, challenges and future research possibilities. The databases IEEE Xplore, ScienceDirect, SpringerLink, Scopus, and Web of Science were systematically searched to find peer-reviewed journal articles written by Published by Elsevier B.V.8676,during 2020–2025. Fifty studies are included in the qualitative synthesis according to pre-designed inclusion/exclusion criteria. The results highlight four key themes for the application of AI: predictive analytics for predicting academic performance, personalised and adaptive learning systems, learning analytics for supporting teacher and institution decision making and institutional decision support using educational data. Challenges, including technical limitations, institutional readiness, and ethical considerations in privacy, bias, and transparency are also discussed.In addition, this analysis points to gaps in literature and paves ways for researchers to extend the current frontiers of research, including the design of interpretable AI models, multi-language tools, and privacy-preserving approaches. The findings offer important implications for researchers, educators, and policymakers striving to harness AI to design better and fairer educational systems.