Secure Real-Time Coding Interview System with AI-Based Evaluation and Monitoring

Abstract: The growing demand for remote technical hiring has accelerated the need for intelligent, unified platforms capable of conducting fair, scalable, and secure coding interviews. Traditional tools address only isolated aspects— collaboration, execution, or evaluation—leaving critical gaps in assessment integrity and real-time interactivity. This paper presents a Secure Real-Time Coding Interview System that integrates a conflict-free multi-user collaborative code editor, AI-based automated performance evaluation, Docker-sandboxed secure code execution, and behavioral monitoring into a single cloud-based platform. The system leverages WebSockets and CRDT/OT algorithms for real- time synchronization, Judge0 API for multi-language execution, and WebRTC/Socket.io for voice, video, and chat communication. Through a systematic review of five related works, six critical research gaps are identified, and a five- layer architecture is proposed to address them comprehensively. The platform requires no local installation and targets academic institutions and recruitment organizations seeking credible, interactive, and scalable online coding assessment infrastructure.

Keywords: Real-Time Collaboration; Coding Interview; AI Evaluation; WebSockets; CRDT; Docker; Monaco Editor; Secure Code Execution; Online Assessment; Socket.io; WebRTC; Node.js; React.js; Judge0 API; Behavioral Monitoring.