AI-Driven Decision Support System for Multidimensional Academic Performance Prediction in Higher Education

The increasing integration of artificial intelligence (AI) into educational systems has highlighted the limitations of traditional data analysis tools in academic performance assessment. This study proposes a four-level AI-enhanced Decision Support System (DSS) employing Artificial Neural Networks (ANN) to classify and predict student outcomes based on multi-semester academic data and co-curricular attributes. The dataset, comprising information from 300 students, includes academic scores, participation in extracurricular activities, and skill assessments. Data preprocessing and feature selection strategies were implemented to optimize model input. The ANN model achieved high accuracy across three semesters, providing granular and actionable insights for educators. The system further identifies individual and cohort-level trends, supports personalized feedback, and enables proactive intervention strategies. The proposed DSS demonstrates a scalable, interpretable, and effective approach for performance analysis in contemporary educational settings.