AI-Powered Resume Analyzer and Interview Preparation System | IJCT Volume 13 – Issue 3 | IJCT-V13I3P89
International Journal of Computer Techniques
ISSN 2394-2231
Author
Afwan Husaini
Abstract
The increasing competition in job recruitment has created a need for intelligent systems that assist candidates in improving their resumes and preparing effectively for interviews. This research presents an AI-powered Resume Analyzer and Interview Preparation System that processes user inputs such as resume content, job description, and self-description to generate meaningful insights. The system utilizes artificial intelligence through the Google Gemini API to evaluate candidate suitability and generate structured outputs including match score, technical and behavioral interview questions, skill gap analysis, and a personalized preparation plan. The system also generates an optimized and ATS-friendly resume in PDF format tailored to the job requirements. The backend is developed using Node.js and Express, while MongoDB is used for data storage. The performance of the system depends on the quality of input data and AI responses. The proposed system assists candidates in enhancing their preparation and improving their chances of successful job placement.
Keywords
Artificial Intelligence, Resume Analyzer, Interview Preparation, Google Gemini API, Node.js, MongoDB, NLP, ATS Resume, Skill Gap Analysis
Conclusion
In the context of the proposed research, an AI-powered resume analyzer and interview preparation system has been developed to assist candidates in improving their recruitment readiness. The system integrates artificial intelligence with modern web technologies to analyze resumes, evaluate candidate suitability, and generate structured outputs such as match score, interview questions, skill gap analysis, and preparation plans. It was observed that traditional methods of resume screening and interview preparation are manual and lack personalization, which limits their effectiveness. The proposed system addresses these limitations by providing an intelligent and automated solution that enhances user experience and reduces effort. The results demonstrate that the system is capable of generating relevant and meaningful outputs, thereby supporting candidates in better preparation and decision-making. However, the performance of the system depends on the quality of input data and AI-generated responses. Overall, the proposed system proves to be a reliable and efficient tool for modern recruitment preparation and can be further improved with advanced AI models and real-time feedback mechanisms.
References
[1] Google, “Google Gemini API Documentation,” 2024. [Online]. Available: https://ai.google.dev [2] Node.js Foundation, “Node.js Documentation,” 2024. [Online]. Available: https://nodejs.org [3] Express.js, “Express Web Framework Documentation,” 2024. [Online]. Available: https://expressjs.com [4] MongoDB Inc., “MongoDB Documentation,” 2024. [Online]. Available: https://www.mongodb.com [5] Facebook Inc., “React.js Documentation,” 2024. [Online]. Available: https://react.dev [6] Puppeteer Team, “Puppeteer Documentation,” 2024. [Online]. Available: https://pptr.dev [7] M. Allamanis, E. T. Barr, P. Devanbu, and C. Sutton, “A Survey of Machine Learning for Big Code and Naturalness,” ACM Computing Surveys, vol. 51, no. 4, pp. 1–37, 2018. [8] J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” NAACL, 2019. [9] S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach,” 3rd ed., Pearson, 2010. [10] A. Rajpurkar et al., “SQuAD: 100,000+ Questions for Machine Comprehension of Text,” EMNLP, 2016.
How to Cite This Paper
Afwan Husaini (2026). AI-Powered Resume Analyzer and Interview Preparation System. International Journal of Computer Techniques, 13(3). ISSN: 2394-2231.
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