AI-Driven Real-Time Feedback System for Enhanced Student Support: Leveraging Sentiment Analysis and Machine Learning Algorithms

Abstract

The rapid evolution of educational technologies has led to a shift toward personalized and adaptive learning experiences. A critical component of such systems is the ability to provide timely and relevant feedback to students. This paper presents an AI-driven real-time feedback system designed to enhance student support through the integration of sentiment analysis and machine learning algorithms. The system leverages sentiment analysis to gauge the emotional tone of student interactions, such as forum posts, assignment submissions, and feedback. Machine learning algorithms, including decision trees, support vector machines (SVM), and deep learning models, are used to analyze and predict student engagement, performance, and emotional states. By combining both cognitive and emotional insights, the system delivers personalized, context-sensitive feedback that helps students overcome learning challenges and improve academic outcomes. The effectiveness of the system is evaluated using multiple datasets, showing significant improvements in student engagement, satisfaction, and performance.