AI-Powered Virtual Mock Interview Training and Evaluation System

Abstract

In the highly competitive modern employment landscape, performing well in corporate interviews requires a robust blend of technical expertise, problem-solving capabilities, and confident communication. Traditional interview preparation methods often fail to simulate the high-pressure, adversarial nature of real-world recruitment and lack objective, scalable evaluation. To bridge this gap, this project presents an AI-Powered Virtual Mock Interview Training and Evaluation System designed to rigorously train candidates and provide high-trust data to recruiters. Developed using a modern technology stack comprising React.js for the frontend and Fast API with PostgreSQL for the backend, the system orchestrates a multi-tiered evaluation pipeline. The process begins with an AI-driven Applicant Tracking System (ATS) that utilizes Natural Language Processing (NLP) to parse resumes, extract skills, and enforce a strict eligibility gate. Qualified candidates proceed to a practice sandbox, which prepares them for the core "Strict Mode" Real Interview module. This high-stakes phase features strictly timed Technical and Aptitude Multiple- Choice Questions with negative marking, an "Adversarial AI" theoretical voice round that actively challenges spoken responses with dynamic follow-up questions, Versant-style corporate communication stress tests, and a live coding "Bug Hunt" utilizing the Monaco Editor under strict time constraints. To ensure enterprise-grade data integrity for prospective recruiters, the system integrates a zero-tolerance AI Proctoring Engine. This security module enforces a "3-Strike Termination Protocol" utilizing computer vision and browser-state monitoring to detect tab- switching, unauthorized mobile phone usage, and multiple human faces in the camera frame. The final output is a verified, comprehensive candidate performance profile equipped with detailed analytics. These profiles are seamlessly shared with partner startup companies, effectively streamlining the recruitment pipeline, eliminating hiring bias, and significantly enhancing candidate employability