A Predictive Framework Combining AI and Learning Analytics for Real-Time Monitoring and Early Intervention in Student Outcomes

Combining artificial intelligence (AI) and learning analytics (LA) has emerged as a ground-breaking method of improving educational outcomes through data utilization and early decision-making. This paper suggests a Predictive Framework of AI and Learning Analytics to Real-Time Monitoring and Early Intervention in Student Outcomes. The framework uses machine learning algorithms such as gradient boosting and deep learning models to evaluate dynamic data streams of learning management systems, assessments and behavioural indicators. The engagement metrics, academic performance patterns, and patterns of interactions are included among the key predictor characteristics that jointly allow identifying at-risk students in advance. Model performance is validated using statistical parameters that have been identified as AUC-ROC, F1 score, sensitivity, specificity, and calibration measurements, which are accuracy and interpretability. This study is relevant to the development of personalised learning ecosystems, maximisation of the allocation of institutional resources, student retention, and well-being when using digital educational settings.