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Paper ID:IJSREM65466

PREPMATE AI: A NEXT-GENERATION RESUME ANALYSER AND MOCK AI INTERVIEWER

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65466

Volume:

10 |

6 |

Jun 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

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.

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Authors

SD

Sujal Datkar

Department of Computer Engineering

Sinhgad College of Engineering

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RC

Rajeev Chouhan

Department of Computer Engineering

Sinhgad College of Engineering

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MP

Maheshwari Palkar

Department of Computer Engineering

Sinhgad College of Engineering

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KK

Krushna Kudale

Department of Computer Engineering

Sinhgad College of Engineering

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Mentors

PBRB

Prof. B. R. Ban

Department of Computer Engineering

Sinhgad College of Engineering

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Abstract

Recruitment processes have become increasingly dependent on automation due to the growing number of job applicants and the limitations of manual candidate evaluation. Traditional resume screening and interview preparation methods require significant human effort and often fail to provide personalized feedback to candidates. This research proposes a Next-Generation Resume Analyser & Mock AI Interviewer, an intelligent AI-based framework that integrates Natural Language Processing (NLP), Machine Learning (ML), Large Language Models (LLMs), and multimodal analysis techniques to automate resume evaluation and simulate realistic interview environments. The proposed system extracts meaningful information from resumes, identifies candidate skills, matches them with job requirements, and generates personalized interview questions. The mock interview module conducts AI-driven interviews by analyzing candidate responses using text, speech, and video-based features. The system evaluates communication skills, confidence level, technical knowledge, and answer relevance to generate a comprehensive performance report. To improve reliability and ethical decision-making, fairness-aware assessment techniques are incorporated to reduce bias during candidate evaluation. The framework aims to assist job seekers in improving interview performance while helping recruiters achieve faster, consistent, and unbiased candidate assessment.

KEYWORDS

Natural Language Processing (NLP) Machine Learning (ML) Large Language Models (LLMs) and multimodal analysis techniques

Cite This Article

Sujal Datkar, Rajeev Chouhan, Maheshwari Palkar, Krushna Kudale, Prof. B. R. Ban, "PREPMATE AI: A NEXT-GENERATION RESUME ANALYSER AND MOCK AI INTERVIEWER," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-6, Jun 2026.

Full Article/Conclusion

The proposed Next-Generation Resume Analyser & Mock AI Interviewer provides an intelligent solution for modern recruitment and career preparation. By integrating NLP-based resume analysis, AI question generation, conversational interview agents, and multimodal assessment, the system creates a realistic and personalized interview experience. Unlike traditional resume screening and mock interview platforms, the proposed framework evaluates candidates from multiple perspectives while maintaining fairness and transparency. The system benefits both candidates and recruiters by improving preparation quality, reducing manual effort, and enabling efficient AI-assisted hiring.

References

[1]     Raj Pandey et al., “Interview Bot with Automatic Question Generation and Answer Evaluation.”   [2]     Mingzhe Li et al., “EZInterviewer: To Improve Job Interview Performance with Mock Interview Generator,” WSDM 2023.   [3]     Truong Thanh Hung Nguyen et al., “SimInterview: Transforming Business Education through Large Language Model-Based Simulated Multilingual Interview Training System,” ICEFM 2025.   [4]     Changwoo Kim et al., “Fairness-Aware Multimodal Learning in Automatic Video Interview Assessment,” IEEE Access 2023.     [5]     Bimasena Putra et al., “MAG-BERT-ARL for Fair Automated Video Interview Assessment,” IEEE Access 2024.   [6]     Joel Manuel C J et al., “Q&AI: An AI Powered Mock Interview Bot for Enhancing Performance of Aspiring Professionals.” [7]      Onyema et al., “Remote Monitoring System Using Slow-Fast Deep Convolution Neural Network.”

Editorial & Review Transparency

Publication Date

6/25/2026

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.

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