Smart Classroom Assist: AI-Powered Automation for Attendance, Note Generation, and Learning Support

Abstract

Education systems across the world are rapidly evolving with the integration of digital technologies;
however, traditional classroom environments still depend heavily on manual processes such as attendance tracking,
note-taking, and student–teacher interactions. These processes are time-consuming, prone to errors, and lack
scalability. This paper presents Smart Classroom Assist, an AI-driven system designed to automate classroom
operations and enhance learning experiences through intelligent analytics and real-time assistance.
The proposed system integrates YOLOv11-based object detection for automated attendance, Tesseract OCR for
extracting classroom board content, and HuggingFace-based Large Language Models (LLMs) for generating
summaries and answering student queries. A Flutter-based cross-platform application ensures accessibility, while
Supabase provides a scalable backend infrastructure.
The system was evaluated in real classroom environments and achieved 92–95% accuracy in attendance detection
and 85–90% accuracy in OCR extraction, along with high-quality AI-generated responses. The results demonstrate
significant improvements in efficiency, reduced manual workload, and enhanced student engagement. This research
highlights the potential of integrating AI technologies to develop intelligent and scalable educational systems.