International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI Based Mock Interview System Using Generative Artificial Intelligence |
|---|---|
| ABSTRACT | Interview preparation is a critical yet resource-intensive activity that demands repeated practice, domain-specific knowledge, and structured feedback. Traditional approaches such as peer mock interviews and coaching engagements are constrained by availability, cost, and inconsistency. This paper presents an AI-powered Mock Interview System, a production-ready full-stack web application built on the MERN (MongoDB, Express, React, Node.js) stack that leverages Google Gemini generative AI to simulate end-to-end interview sessions across four categories: Aptitude, Technical, HR, and Managerial. The system dynamically generates calibrated questions, evaluates free-form candidate responses in real time using large language model (LLM) based scoring, and delivers per-question feedback encompassing identified strengths, improvement areas, and recommendations. Speech-to-text answer input through the Web Speech API replicate a realistic interview environment. A resilient dual-provider AI architecture combining Google Gemini with a Groq````` fallback service and deterministic question banks guarantees uninterrupted session delivery with AI service availability exceeding 95% under normal operating conditions. JWT and Google OAuth 2.0 authentication, Helmet.js security headers, express-mongo-sanitize input sanitisation, and bcrypt password hashing constitute the security layer. Post-session analytics with Recharts-based visualisations, a global performance leaderboard, and a full interview history module collectively provide a longitudinal preparation environment. Experimental evaluation confirms high AI feedback accuracy across all categories and robust system behaviour under degraded AI service conditions, validating the feasibility of embedding LLMs as the primary evaluation engine in educational technology platforms. |
| AUTHOR | DR. KAVYASHREE N, THEJU B S Associate Professor, Department of MCA, SSIT, Tumakuru, Karnataka, India 4th Sem, Department of MCA, SSIT, Tumakuru, Karnataka, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406013 |
| pdf/13_AI Based Mock Interview System Using Generative Artificial Intelligence.pdf | |
| KEYWORDS | |
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