AI Powered Offline Exam Invigilator

The fast rise of artificial intelligence, along with computer vision tools, is changing how we monitor exams in places like schools. Old-school ways of watching students during tests usually come with mistakes, personal bias, or gaps in attention - especially when hundreds take the test at once. To fix this, our idea is a smart exam watcher using AI that spots odd actions, keeps tabs on what students do, flags forbidden items, while making sure rules are followed as things happen. It uses advanced neural networks to recognize faces, follow where someone’s eyes move, detect objects, check body positions - all helping make oversight more solid and quicker. This setup combines a smooth front-end made with up-to-date design tools plus a back-end running ML workflows and protected data storage. Live camera feeds get analyzed instantly to catch warning signs: sudden head turns, constant glancing off-screen, phone use, or multiple people showing up unexpectedly. Notifications pop up on their own for supervisors, so they spend less time checking things by hand while tests stay fairer. This smart exam helper works without help from people, runs smooth whether it’s used online or offline, scales easily when needed, keeps data safe, shows what's happening clearly, avoids favoritism, spots odd behavior fast, cuts down errors, saves effort across schools and boards alike.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-03
DOI
https://doi.org/10.5281/zenodo.21767765
Primary Topic
Artificial Intelligence and Decision Support Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI Powered Offline Exam Invigilator

Harshith Kushalappa C A, Deepak N, Kishan N R
Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence and Decision Support Systems
article

AI Powered Offline Exam Invigilator

Harshith Kushalappa C A, Deepak N, Kishan N R
article en

Abstract

The fast rise of artificial intelligence, along with computer vision tools, is changing how we monitor exams in places like schools. Old-school ways of watching students during tests usually come with mistakes, personal bias, or gaps in attention - especially when hundreds take the test at once. To fix this, our idea is a smart exam watcher using AI that spots odd actions, keeps tabs on what students do, flags forbidden items, while making sure rules are followed as things happen. It uses advanced neural networks to recognize faces, follow where someone’s eyes move, detect objects, check body positions - all helping make oversight more solid and quicker. This setup combines a smooth front-end made with up-to-date design tools plus a back-end running ML workflows and protected data storage. Live camera feeds get analyzed instantly to catch warning signs: sudden head turns, constant glancing off-screen, phone use, or multiple people showing up unexpectedly. Notifications pop up on their own for supervisors, so they spend less time checking things by hand while tests stay fairer. This smart exam helper works without help from people, runs smooth whether it’s used online or offline, scales easily when needed, keeps data safe, shows what's happening clearly, avoids favoritism, spots odd behavior fast, cuts down errors, saves effort across schools and boards alike.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 3%
Artificial Intelligence and Decision Support Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.