A conceptual review by Raji and colleagues examines how artificial intelligence (AI) and predictive analytics could support student achievement and retention through intelligent tutoring systems (ITS). Drawing on published literature, the authors explore personalised learning, early intervention and responsible implementation in school and higher education settings. They propose a framework that brings these functions together to support students with different learning needs.
The review discusses three main AI techniques: machine learning to predict performance and identify students at risk, natural language processing to interpret responses and provide conversational feedback, and deep learning to analyse complex learning patterns and adapt content. Information such as attendance, assessment results and online activity can inform these systems. The authors also connect ITS design with learning theories that emphasise knowledge construction, manageable cognitive demands and self-regulated learning.
The authors highlight the potential of immediate feedback, adaptive learning pathways and early warning systems to identify knowledge gaps and provide timely support. Their proposed framework combines learner profiles, predictive analytics, adaptive instruction and intelligent feedback, with explainable AI intended to make recommendations understandable to teachers and students. Implementation includes curriculum alignment, infrastructure preparation, pilot testing and ongoing evaluation. Expected benefits include stronger engagement, improved learning and retention, and more equitable access to support.
The article identifies data privacy, algorithmic bias and unequal access to devices, connectivity and digital skills as major challenges. It recommends clear guidelines, secure data practices, teacher development and collaboration among educators, policymakers and developers. Testing with diverse learners and explaining system decisions are also priorities. However, this is a conceptual synthesis: it presents no new student data, pooled effect sizes or experimental evaluation of the proposed framework. Its anticipated benefits should therefore be interpreted as possibilities requiring further evaluation.
Source (Open Access): Raji, B., Iyer, S. S., & Yaseen, M. M. N. M. (2026). AI-enabled predictive analytics in education: Enhancing student success and retention through intelligent tutoring systems. Journal of Digital Educational Technology, 6(1), 1-15.