Paper Title
AI-Based Personalized Learning System: An Adaptive Educational Framework Using Random Forest Machine Learning

Abstract
The global digital transformation of education has starkly revealed the limitations of traditional one-size-fits-all e- learning systems, which fail to accommodate diverse learning paces and prior knowledge, contributing to high dropout rates. This paper presents a comprehensive AI-based personalized learning system designed to address these challenges by leverag- ingmachinelearningtocreatedynamic,adaptiveeducationalex- periences.OursystememploysarobustRandomForestclassifier, achieving 93.5% accuracy in predicting optimal learning paths, integrated within a Flask-based web framework for real-time interaction. The architecture incorporates sentiment analysis for affect-aware adaptation, an intelligent hybrid chatbot for doubt resolution, and comprehensive progress tracking. Experimental results, including a comparative analysis with baseline models (Support Vector Machine, XGBoost, Multilayer Perceptron), demonstrate the system’s superior effectiveness in providing tailored learning experiences. The implementation directly ad- dresses Sustainable Development Goal 4 (Quality Education) by creating an inclusive, adaptive learning environment suitable for diverse educational contexts. Keywords - Personalized Learning, Artificial Intelligence, Machine Learning, Random Forest, Educational Technology, Adaptive Systems, Flask Framework, Sentiment Analysis