AI-Powered Interactive System for Visual Learning and Student Engagement

Abstract

The rapid growth of digital and hybrid learning environments has highlighted the need for intelligent systems that enhance student engagement and understanding. Traditional smart classroom solutions often lack real-time interaction, personalized feedback, and integrated learning support. This paper proposes an AI-powered interactive whiteboard that combines Optical Character Recognition (OCR), Natural Language Processing (NLP), multimedia content retrieval, Text-to-Speech (TTS), and real-time attentiveness monitoring using computer vision techniques. The system captures handwritten input, converts it into digital text, and generates relevant explanations along with supporting visual and audio content to improve comprehension. Additionally, the attentiveness monitoring module analyzes facial and eye movements to assess student focus during learning sessions. The proposed system provides a unified, cost-effective, and interactive platform that bridges the gap between traditional teaching and intelligent learning environments. Experimental results demonstrate improved student engagement, better concept understanding, and enhanced classroom interactivity compared to existing systems.