Predicting Student Performance with Artificial Intelligence: A Comprehensive Research Study

Accurate prediction of student performance has emerged as a critical component of modern educational systems. Artificial Intelligence (AI) and machine learning (ML) models have demonstrated remarkable capabilities in analyzing academic, demographic, behavioral, and digital learning data to forecast student outcomes. The purpose of this research is to explore, compare, and analyze AI techniques used for performance prediction while emphasizing their advantages, limitations, and ethical implications. This study reviews prominent ML and deep learning models, presents an expanded discussion on data preprocessing and feature engineering, and highlights real-world applications in adaptive learning and early intervention systems. The paper concludes with future research directions focusing on explainable AI, fairness, and integration with large-scale educational infrastructures.