An AI-Based Early Warning System for Identifying at- Risk Students in Higher Education

Abstract

Student dropout and academic underperformance remain persistent challenges in higher education, often caused by factors such as academic difficulty, lack of engagement, and socio-economic conditions. Traditional monitoring approaches are mostly reactive and fail to identify at-risk students at an early stage, limiting the effectiveness of timely intervention. With the growing availability of educational data, Artificial Intelligence (AI) and machine learning techniques have been increasingly used to develop Early Warning Systems (EWS) for predicting student risk. This paper reviews and analyses various AI-based approaches that utilize data such as academic performance, attendance, and behavioural patterns to identify students who may require support. Different machine learning models, including decision trees, logistic regression, support vector machines, and ensemble methods such as Random Forest and XGBoost, are examined in terms of their effectiveness in prediction. The study also highlights the importance of integrating predictive systems with timely and personalized intervention strategies to improve student outcomes. In addition, key challenges such as data privacy, model interpretability, and ethical concerns are discussed. Overall, the paper emphasizes that AI-driven Early Warning Systems can significantly enhance student retention and academic success when implemented responsibly and supported by effective institutional practices.