NSET IT Exam Proctoring
High-performance exam proctoring system capable of detecting 4 types of malpractice — paper usage, mobile phones, verbal communication (chitchat), and peeking behavior.
Strategic Overview
High-performance exam proctoring system capable of detecting 4 types of malpractice — paper usage, mobile phones, verbal communication (chitchat), and peeking behavior.
Project Showcase
Objectives & Outcomes
Front-End Development & Integration
Karthik
| Technical Area | Responsibilities & Impact |
|---|---|
UX/UI Development | Completely led the end-to-end design and front-end development, focusing on a high-stakes, low-latency proctoring UI. |
WebSocket Integration | Engineered robust WebSocket listeners for real-time malpractice alert streaming across concurrent exam sessions. |
Visual Analytics | Implemented real-time snapshot rendering and forensic evidence display to provide immediate visual proof of violations. |
Alert Framework | Developed a sophisticated alert handling system to manage and prioritize multiple concurrent proctor notifications. |
Team Collaboration
Bhanu
AI & API LeadArchitected core AI logic and backend API development for malpractice detection.
Ashith
AI EngineerDeveloped AI models for Paper detection, Mobile phone usage, Chitchat analysis, and Peeking behavior.
Akash
AI Training LeadManaged AI model training cycles and led the labeling reviewing process.
Labeling Squad
Data IntegrityHarsha, Sireesha, Rachana, Hema, Kranthi, and Priyanka — essential data labeling and training set preparation.