Predicting Preparatory Programs Using Explainable AI

This study explores the use of explainable AI models to predict and improve student performance in tertiary preparatory programs, particularly for students from low socioeconomic backgrounds. By leveraging machine learning algorithms, the research aims to identify at-risk students early, enabling targeted academic interventions and promoting equity in education. The findings emphasize the importance of transparency and interpretability in AI models to ensure fair, data-driven support for diverse student groups.

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