Hire Ready AI: An AI-Powered Resume Builder and Mock Interview Preparation System
Authors
Prof. Swati Mohite
Department of Computer Engineering, Trinity College of Engineering and Research, Pune, Maharashtra, India. (IN)
Om Gunturkar
Department of Computer Engineering, Trinity College of Engineering and Research, Pune, Maharashtra, India. (IN)
Mrinal More
Department of Computer Engineering, Trinity College of Engineering and Research, Pune, Maharashtra, India. (IN)
Purva Tapare
Department of Computer Engineering, Trinity College of Engineering and Research, Pune, Maharashtra, India. (IN)
Dixita Jadhav
Department of Computer Engineering, Trinity College of Engineering and Research, Pune, Maharashtra, India. (IN)
Article Information
Publication Timeline
Submitted: 2026-06-29
Published: 2026-06-29
Abstract
Preparing for job interviews can be an arduous task. This is due to the lack of structured, timely, and personalized support, which could help candidates to understand potential areas of improvement. To overcome this, we propose Hire Ready AI, an intelligent platform for Resume enhancement and Interview preparation. The system takes as input the candidate's profile and resume and dynamically generates interview questions. As the candidate responds, Hire Ready AI evaluates the candidate's answer across multiple dimensions such as content, grammar, organization, communication, confidence, resume consistency and natural language processing. A voice response is expected from the interviewee and will be transcribed using speech recognition. Then the text is also used as another source of input for the system to analyze the candidate's confidence, personality, and tone of job interviews. We perform simulation experiments on multiple mock interview sessions and evaluate the system performance. The results show that our proposed framework generates reliable responses, timely feedback, and personalized interview questions.
Keywords
Hire Ready AI, NLP, ML, Voice Analysis, IQG, Response Evaluation Model, Confidence Analysis, Real-time Feed-back System.
This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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References
1. J. Manuel et al., “Q&AI: AI-powered mock inter-view bot,” IEEE, 2023. [Google Scholar] [Crossref]
2. R. A. M. Rajeswari et al., “InterviewIQ: AI chat-bot for candidates,” IEEE, 2023. [Google Scholar] [Crossref]
3. R. M. Marvaniya et al., “Smart Prep: AI-based in-terview system,” IJCS, 2023. [Google Scholar] [Crossref]
4. Y. Liang et al., “Resume parsing using multi-task learning,” IEEE, 2023.. [Google Scholar] [Crossref]
5. S. V. Golande et al., “AI mock interview evalua-tor,” 2025. [Google Scholar] [Crossref]
6. S. Mohanty et al., “Resumate: NLP-based recruit-ment system,” IEEE, 2023. [Google Scholar] [Crossref]
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