Edupulse: An AI-Driven Emotion And Engagement Analytics System For Smart Classroom Learning

Abstract

The increasing popularity of digital educational platforms requires intelligent systems which can track student participation and emotional states during learning sessions. The research presents EduPulse an AI-powered educational system which tracks student emotional states and their learning engagement in real time to improve teaching effectiveness and student achievement. The system implements computer vision technology with deep learning algorithms to analyze facial expressions and body movements and eye contact through camera surveillance. The system detects six different emotional states which include confusion and boredom and interest and attentiveness based on the observed behavior patterns during the classroom lectures. The system processes collected information through machine learning methods which the interactive analytics dashboard utilizes to present teachers with data about general student participation and their focus levels and academic challenges. The system produces automatic recommendations which assist teachers in adjusting their teaching methods through two main approaches. The platform includes privacy protection features which safeguard educational data and ensure responsible artificial intelligence practices in schools. The proposed conceptual framework demonstrates how artificial intelligence and emotion recognition technologies can transform traditional classrooms into intelligent learning environments. EduPulse provides educational institutions with real-time student engagement metrics which lead to better teaching results and increased student involvement and customized learning pathways through its interactive learning environment.