Artificial intelligence in student management systems to enhance academic performance monitoring and intervention.

In recent years, the integration of artificial intelligence (AI) in student management systems (SMS) has gained significant attention, particularly for monitoring academic performance and predicting at-risk students. Traditional approaches often lack the necessary adaptability and predictive accuracy across different learning environments. A hybrid AI-based model is proposed to enhance academic performance monitoring and intervention strategies by integrating decision trees (DT), random forests (RF), support vector machines (SVM), and artificial neural networks (ANN). The objective is to assess the effectiveness of the hybrid approach across multiple datasets, including UCI student performance, open university learning analytics dataset (OULAD), and national educational longitudinal study (NELS:88). The hybrid model was trained using a combination of preprocessing techniques, including missing data imputation, feature selection, and data normalization. The performance of the hybrid model was compared to individual base models using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. The hybrid model achieved outstanding results, with an accuracy of 98.8% on the UCI dataset, surpassing the performance of individual models. The hybrid model consistently outperformed the base models across all datasets, reducing error rates by over 5%. The proposed hybrid AI model provides a robust, scalable solution for academic performance monitoring and early intervention, demonstrating its potential for deployment in diverse educational contexts to support at-risk students proactively.