AI-Powered Student Proctoring System

  • Unique Paper ID: 174159
  • Volume: 11
  • Issue: 10
  • PageNo: 4178-4184
  • Abstract:
  • This study introduces a real-time computer vision-based system for monitoring children's concentration levels during remote learning. Leveraging OpenCV and Mediapipe's Face Mesh for video capture and facial landmark detection, the system evaluates attention through visual cues such as eye movements, blink rates, and head posture. Lightweight machine learning models process these features to classify focus states, offering real-time feedback via a user-friendly Streamlit interface. Designed for affordability and accessibility, the system operates using standard webcams and computers, making it practical for home environments. By bridging a critical gap in remote education, this solution supports caregivers in enhancing children's study habits and ensuring sustained engagement.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{174159,
        author = {Nandepu Rama Venkata Durga Vamsi and A.Bhagyasri and Vattikuti Raghavendra and Sirigineedi Gamyasri and Sudhana Mukesh},
        title = {AI-Powered Student Proctoring System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4178-4184},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174159},
        abstract = {This study introduces a real-time computer vision-based system for monitoring children's concentration levels during remote learning. Leveraging OpenCV and Mediapipe's Face Mesh for video capture and facial landmark detection, the system evaluates attention through visual cues such as eye movements, blink rates, and head posture. Lightweight machine learning models process these features to classify focus states, offering real-time feedback via a user-friendly Streamlit interface. Designed for affordability and accessibility, the system operates using standard webcams and computers, making it practical for home environments. By bridging a critical gap in remote education, this solution supports caregivers in enhancing children's study habits and ensuring sustained engagement.},
        keywords = {},
        month = {March},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 10
  • PageNo: 4178-4184

AI-Powered Student Proctoring System

Related Articles