Journal of Ubiquitous Computing and Communication Technologies
IRO Journals
Volume 7 — Issue 4 — December 2025

Student Monitoring System Using AI and Image Processing

https://doi.org/10.36548/jucct.2025.4.004
Ravi Chandra M.L.
Department of Electronics and Communication Engineering, Srinivasa Ramanujan Institute of Technology, Anantapur, Andhra Pradesh
Neeha Begum S.
Department of Electronics and Communication Engineering, Srinivasa Ramanujan Institute of Technology, Anantapur, Andhra Pradesh
Sai Geetha P.
Department of Electronics and Communication Engineering, Srinivasa Ramanujan Institute of Technology, Anantapur, Andhra Pradesh
Murali M.
Department of Electronics and Communication Engineering, Srinivasa Ramanujan Institute of Technology, Anantapur, Andhra Pradesh
Sasi Vardhan Raju J.
Department of Electronics and Communication Engineering, Srinivasa Ramanujan Institute of Technology, Anantapur, Andhra Pradesh
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How to Cite

M.L., Ravi Chandra, Neeha Begum S., Sai Geetha P., Murali M., and Sasi Vardhan Raju J. 2026. “Student Monitoring System Using AI and Image Processing”. Journal of Ubiquitous Computing and Communication Technologies 7 (4): 375-93. https://doi.org/10.36548/jucct.2025.4.004.
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Keywords

— AI (Artificial Intelligence)
— Image Processing
— Facial Recognition
— Raspberry Pi
— Database Support
— Automated Attendance System
— Technology-Driven
— Speaker Announcements
Published: 30-01-2026

Abstract

The purpose of developing a Student Monitoring System that incorporates AI and Digital Image Processing is to automate the process of logging attendance, making it easier and more accurate to identify each student in the classroom or laboratory. The Student Monitoring System is built on a Raspberry Pi, which serves as the Central Processing Unit (CPU) of the system and stores current attendance data (logs) in a database created in conjunction with the RPI. An additional component of this system is an AI Vision Machine, which is used to identify each student's face when they attend class and is capable of providing audio announcements to students in class. Thus, the system will assist in eliminating the manual attendance logging method, resulting in a significant reduction of errors associated with this type of recording, saving time for both the student and the instructor. The Student Monitoring System allows the various components (AI algorithms, RPI, and database) used to identify students and the associated components of the system to work together with the latest data available for ongoing updates and monitoring. This solution was designed specifically for educational facilities to improve their attendance management processes and provide an enhanced student experience in a technology-driven campus environment.

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