Mock Interviewer Powered by AI-An Intelligent System for Comprehensive Interview Preparation

Abstract

The increasing competitiveness of modern job markets highlights the need for effective interview preparation platforms. This research proposes “Mock Interviewer Powered by AI,” an intelligent system designed to help job seekers improve their interview skills. The system integrates multiple modules including aptitude testing, resume analysis, AI-based live interview simulation, and personalized feedback generation. By utilizing advanced Natural Language Processing (NLP) and Machine Learning (ML) techniques, the platform evaluates communication ability, technical knowledge, and response quality. The system provides detailed performance insights and improvement suggestions to users. Experimental results indicate that the proposed system improves user interview performance consistency by approximately 30%, demonstrating its potential to enhance accessibility to structured interview preparation. The paper presents the system architecture, methodology, evaluation results, and future scope of AI-based interview training solutions.

Introduction

The text presents an AI-based system called “Mock Interviewer Powered by AI”, designed to improve job interview preparation. It addresses the challenges faced by candidates, especially those with limited resources, who lack access to realistic interview practice and meaningful feedback. Traditional methods like peer mock interviews and question banks are limited, so the proposed system uses artificial intelligence to simulate real interview conditions and provide personalized guidance.

The system integrates multiple components including resume analysis, aptitude testing, AI-driven interview simulation, and feedback generation. Using NLP and machine learning, it analyzes resumes to extract skills and suggest improvements, conducts aptitude assessments, and generates technical and behavioral interview questions based on user profiles. During mock interviews, user responses are evaluated for clarity, relevance, confidence, and technical accuracy, and detailed performance reports are generated.

Existing research shows growing use of AI in recruitment, including job matching systems, NLP chatbots, and sentiment-based interview feedback tools. However, gaps remain in providing fully interactive, realistic, and personalized interview training.

Conclusion

1) The “Mock Interviewer Powered byAI”system presents a robust, scalable solution for comprehensive interview preparation in the digital age. By combining aptitude testing, resume analysis, AI-driven live interviews, and multimodal feedback, the platform addresses critical gaps in traditional and existing AI-based preparation methods. The system’s performance—demonstrated by high model accuracies and positive user outcomes—underscores its potential to democratize interview readiness, reduce preparation inequities, and enhance candidates’ confidence and performance. 2) Future enhancements will focus on further personalizing the interview experience, integrating real-time video and audio analysis for nuanced feedback on tone and body language, expanding the question bank to include company-specific and domain- specialized scenarios, and refining the conversational flow through advanced dialogue management techniques. In addition, ongoing research will explore the ethical and privacy implications of AI-mediated assessment, ensuring transparency, fairness, and user agency. 3) By advancing the frontier of AI-enabled human resource technology, this work lays the foundation for more equitable and effective pathways to employment in an increasingly digital workforce.

References

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Copyright

Copyright © 2026 Prof. Archana Ugale, Mr. Patel Viraj Harishchandra, Mr. Gholap Utkarsh, Mr. Lokhande Dhanaraj Arjun, Mr. Lawande Sagar Jitendra. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.