AI-Powered Interview Preparation System: Integrating Resume Analysis, HR Simulation, and Technical Skill Assessment

Harsh Koshti *

MVPS KBT College of Engineering, Nashik, Maharashtra, India.

Prathamesh Gosavi

MVPS KBT College of Engineering, Nashik, Maharashtra, India.

Roshan Pagar

MVPS KBT College of Engineering, Nashik, Maharashtra, India.

Prathamesh Khairnar

MVPS KBT College of Engineering, Nashik, Maharashtra, India.

Sopan Talekar

MVPS KBT College of Engineering, Nashik, Maharashtra, India.

*Author to whom correspondence should be addressed.


Abstract

This article presents an AI-powered interview preparation system designed to objectively collect and evaluate candidate responses. The solution automates key stages of the interview process, enhancing recruitment efficiency through real-time emotion detection using Convolutional Neural Networks (CNN), natural language processing (NLP) for resume analysis, and speech recognition for response evaluation. The system integrates with an Applicant Tracking System (ATS) for resume screening, an HR interview simulator focusing on communication and behavioural skills, and a technical assessment module for domain-specific knowledge evaluation. Extensive testing demonstrated significant improvements in candidate preparation efficiency and performance quality compared to traditional methods. The system also supports candidates by automatically generating questions, providing instant feedback, and offering detailed performance insights. This research delivers a scalable and unbiased approach, bridging traditional hiring techniques with modern AI capabilities.

Keywords: AI interview preparation, resume analysis, technical assessment, HR simulation, candidate performance scoring


How to Cite

Koshti, Harsh, Prathamesh Gosavi, Roshan Pagar, Prathamesh Khairnar, and Sopan Talekar. 2025. “AI-Powered Interview Preparation System: Integrating Resume Analysis, HR Simulation, and Technical Skill Assessment”. Journal of Engineering Research and Reports 27 (5):21-33. https://doi.org/10.9734/jerr/2025/v27i51489.

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