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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65821
Volume:
10 |
7 |
Jul 2026
Pages:
1-8
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Information Technology
G H Raisoni College of Engineering and Management Pune
Information Technology
G H Raisoni College of Engineering and Management Pune
Information Technology
G H Raisoni College of Engineering and Management Pune
Information Technology
G H Raisoni College of Engineering and Management Pune
The purpose of this research article is to present a new artificial intelligence-based system that enhances the search for and hiring of job applicants. The two model framework for this system employs very powerful models (Llama 3.370B) to analyze candidate resumes using a software product called PyMuPDF to determine the best candidates and lightweight models to rapidly create interview questions of the candidate. The system also employs conversational AI to allow the hiring manager and candidate to communicate almost instantly with each other in a conversational manner. The two models of this system work in conjunction with each other to provide greater insight by being able to ask questions that gain further insight into candidate thinking and behaviour and being able to engage in two-way communication/reciprocal dialogue with the candidate. Testing of the system indicates it is very effective at determining the best candidates and makes the hiring process less biased and more transparent. This represents a potential resolution for businesses seeking to improve their processes for hiring employees.
KEYWORDS
In this article, we present the AI Interview, a cutting-edge recruitment system that utilizes advanced artificial intelligence technology for automating and enhancing the screening of candidates and conducting candidate interviews. AI Interview provides an all-encompassing, intelligent decision support system to assist with modern hiring by including LLM-based candidate evaluation and resume parsing as well as conversational interactions enhanced by retrieval-augmented methods. AI Interview addresses key weaknesses in traditional re-cruitment methods, especially around keyword-based filtering and lack of personalized interactions. By utilizing contextually aware and adaptive questions when assessing candidates, AI Interview provides both more accurate candidate assessments and a more engaging candidate interview experience. The system’s modular design allows for scalability and flexibility, which will allow the system to continue to develop as new technologies become available and new recruitment needs arise. Additionally, the system’s use of retrieval ground-ing improves the reliability of its factual information, and its low-latency infrastructure makes the interaction in real-time practical for deployment in actual recruitment situations. AI Interview demonstrates that it has considerable potential to improve hiring efficiency, reduce bias, provide greater transparency, and increase recruiters’ confidence in their hiring decisions. As recruitment continues to rapidly evolve as the result of artificial intelligence, systems such as AI Interview will be essential to allow for smarter, faster, and more human-centric ways to hire.
[1] J. Smith and A. Kumar, “AI-Powered Video Interview Analysis Using Natural Language Processing,” IEEE Xplore, vol. 15, no. 3, pp. 245–258, 2022. [2] M. Chen et al., “Real-time Communication Systems for Remote Inter-views,” ACM Digital Library, vol. 8, no. 2, pp. 112–125, 2023. [3] R. Patel and S. Gupta, “Machine Learning Approaches for Candidate Assessment,” Springer Journal of AI Applications, vol. 34, pp. 567–582, 2021. [4] K. Lee and T. Wilson, “Automated Interview Scheduling and Manage-ment Systems,” IEEE Transactions on Cloud Computing, vol. 11, no. 4, pp. 892–905, 2023. [5] L. Johnson et al., “Bias Mitigation in AI-Assisted Recruitment,” Sci-enceDirect AI Ethics Journal, vol. 19, pp. 34–49, 2022. [6] H. Zhang and Y. Liu, “Natural Language Understanding for Interview Questions,” arXiv preprint arXiv:2303.12345, 2023. [7] D. Brown and E. Martinez, “Video Analytics for Non-verbal Commu-nication Assessment,” IEEE Access, vol. 10, pp. 15623–15638, 2022. [8] A. Thompson et al., “Scalable Web Applications for Enterprise HR Systems,” ACM Computing Surveys, vol. 55, no. 6, pp. 1–35, 2023. [9] S. Park and J. Kim, “Real-time Speech-to-Text Transcription Systems,” Journal of AI Research, vol. 68, pp. 421–445, 2023. [10] M. Anderson and R. White, “Data Security and Privacy in Cloud-based HR Platforms,” Emerald Insight Technology Journal, vol. 28, no. 5, pp. 678–694, 2022.
Publication Date
7/4/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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