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-Powered Mock Interview Platform for Personalized Interview Preparation |
|---|---|
| ABSTRACT | In today's competitive environment, it is essential for candidates to possess clear communication skills, technical expertise, and confidence during interviews to achieve success. Traditional interview training methods are often too broad, lack immediacy, and are not readily available. This paper suggests leveraging Artificial Intelligence to create a smart interview preparation tool, specifically an AI-Powered Mock Interview Assessment System. The proposed system tailors interview questions based on specific job roles, technology stacks, and levels of experience. It evaluates candidate responses using Google Gemini AI, providing detailed feedback, performance scores, strengths and weaknesses, and recommendations for improvement. The system is developed using React.js, Next.js, MongoDB, Clerk Authentication, and Gemini AI. The platform provides a user-friendly, efficient, and scalable solution that assists customers in improving their interview preparation and boosting candidate performance by offering continuous evaluation and feedback. |
| AUTHOR | PROF. M.P. NAVALE, PRERNA PAWAR, DURVA MATADE, TEJAS BANGINWAR, SIDDESH DESAI Guide, Dept. of Computer Engineering, NBN Sinhgad Technical Institute Campus, Pune, India UG Student, Dept. of Computer Engineering, NBN Sinhgad Technical Institute Campus, Pune, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406068 |
| pdf/68_AI-Powered Mock Interview Platform for Personalized Interview Preparation.pdf | |
| KEYWORDS | |
| References | [1] Google Gemini AI Documentation. [2] MongoDB Documentation. [3] Clerk Authentication Documentation. [4] React.js Official Documentation. [5] Next.js Official Documentation. [6] Tailwind CSS Documentation. [7] Vaswani, A., et al., “Attention Is All You Need,” NeurIPS, 2017. [8] Devlin, J., et al., “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” NAACL, 2019. [9] Brown, T., et al., “Language Models are Few-Shot Learners,” NeurIPS, 2020. [10] Russell, S., and Norvig, P., Artificial Intelligence: A Modern Approach. [11] Jurafsky, D., and Martin, J., Speech and Language Processing. [12] Research Studies on AI-Based Interview Assessment Systems. |
