Edge AI-Based Smart Classroom with Dynamic Student Attentiveness Monitoring

Abstract

The traditional classroom mode of learning remains highly effective for student learning. However, with the increasing presence of digital gadgets it has increased distractions for students making it challenging for students to maintain their focus levels in the classroom. Also, with larger class sizes teachers also find it difficult to monitor and engage with students effectively. Using Edge AI technology our system works on these issues by providing real-time insights into student activity in the classroom. Running locally on the teacher’s laptop our system uses a containerized AI model to analyze live camera feeds and track attentiveness. Our system allows teachers to input students overall marks which is used to dynamically adjust the threshold level of attention based on past grades and performance. This personalized approach supports each student’s learning journey. By addressing these challenges our system enhances the classroom environment and makes learning more effective.

Publication
In Springr Journal