EXPLAINABLE AI FOR EARLY PREDICTION OF STUDENT ACADEMIC RISK USING MACHINE LEARNING

Student academic risk prediction aims to identify learners who may need timely academic support before final outcomes are known.Machine learning can analyse historical indicators such as attendance, assessment performance, assignment completion, andlearning-platform engagement to estimate the likelihood of academic difficulty. However, a prediction is more useful to educatorswhen its basis can be understood. This paper proposes an explainable machine-learning framework that combines early riskestimation with interpretable explanations for students, faculty, and academic advisors. The workflow includes data governance,preprocessing, model training, validation, risk scoring, local and global explanations, and human review. Candidate models includelogistic regression and decision trees as interpretable baselines, alongside more complex models where justified. Explanationmethods such as feature importance and SHAP-style contribution analysis can help communicate which recorded indicatorsinfluenced a prediction. The system is designed to support—not replace—professional judgement, and it should not automaticallypenalise students. Fairness, privacy, data quality, and clear communication are treated as core design requirements. The proposedarchitecture and evaluation plan are presented for an academic prototype; no experimental performance results are claimed.