AI-Based Early Warning Systems for Academic Dropout Prevention

Abstract

Academic dropout -- the premature departure of students from higher education programmes -- represents a significant personal, institutional, and societal cost, with European dropout rates averaging 32% for bachelor programmes and MOOC dropout frequently exceeding 50%. AI-based early warning systems (EWS) that identify at-risk students weeks before dropout -- enabling timely advisor outreach, peer support, and resource allocation -- are among the highest-impact applications of educational AI. This paper proposes the AI Early Warning System Framework (AIEWSF), a systematic evaluation of the full EWS pipeline from prediction to intervention effectiveness across five EWS architectures -- threshold-based rules, machine learning classifier (XGBoost), deep learning sequence model (LSTM), ensemble stacking, and LLM-augmented EWS -- deployed in three real higher education institutions over two academic years. AIEWSF evaluates not only prediction accuracy but the complete EWS effectiveness chain: alert generation, advisor response rate, intervention completion, and actual dropout reduction. Key results: ensemble stacking achieves the highest dropout prediction AUC (0.938); LLM-augmented EWS achieves the highest advisor response rate (84.2%) via personalised alert narratives; actual dropout reduction attributable to EWS intervention reaches 18.4% (from 28.6% to 23.3% dropout rate). The framework introduces the EWS Effectiveness Score (EWSEFF) and provides a complete end-to-end EWS deployment blueprint.