AI-Based Diagnostics and Prediction of Student Learning

Abstract

This study explores the application of Artificial Intelligence (AI) in diagnosing and predicting students’ knowledge levels within modern educational environments. The research focuses on the use of machine learning and deep learning models to analyze students’ cognitive and behavioral data collected from digital learning systems. AI-based diagnostic approaches, particularly knowledge tracing models, enable continuous monitoring of learners’ mastery levels, while predictive models provide early identification of at-risk students. The results demonstrate that AI-driven methods significantly improve the accuracy, timeliness, and personalization of educational assessment compared to traditional approaches. Furthermore, the study highlights the pedagogical implications of integrating AI into competency-based and student-centered learning frameworks. Despite its advantages, challenges related to data quality, model interpretability, and ethical considerations remain critical. The findings suggest that AI has strong potential to transform educational diagnostics and support data-informed decision-making in teaching and learning processes.