AI-Powered Resume Analyzer and Interview Preparation System

Abstract

The process of evaluating resumes and preparing candidates for interviews traditionally requires significant manual effort, domain expertise, and time. This paper presents an AI-powered Resume Analyzer and Interview Preparation System designed to automate resume parsing, skill extraction, job-role matching, and AI-driven interview preparation. The system integrates Natural Language Processing (NLP), Machine Learning (ML), and Large Language Models (LLMs) to analyze resume content, extract skills, compute job-role fit, generate candidate insights, and conduct intelligent mock interviews. A Flask-based backend processes the resume, while a web-based interface presents structured results, suggestions, and interactive interview sessions. Experimental evaluation demonstrates that the system enhances accuracy, reduces human effort, and provides personalized interview guidance, making it an efficient tool for recruitment, career assessment, and skill development.

Introduction

The AI-Powered Resume Analyzer and Interview Preparation System is designed to automate and enhance recruitment and career development. Traditional resume screening is time-consuming, inconsistent, and prone to human bias, while candidates often struggle to assess their own fit for roles. Leveraging AI, NLP, deep learning, and large language models (LLMs), this system extracts structured information from resumes, identifies skills, matches candidates to suitable job roles, and provides AI-driven mock interviews with real-time feedback on clarity, confidence, and technical depth.

The system handles multiple resume formats (PDF, DOCX, TXT), performs preprocessing, section segmentation, named entity recognition, and semantic classification. Skills are extracted using dictionary and embedding-based methods, mapped to job-role vectors, and matched via cosine similarity to recommend relevant careers. The interview module generates role-specific questions, evaluates responses, and offers personalized guidance, enabling continuous learning without human intervention.

Conclusion

The AI-Powered Resume Analyzer and Interview Preparation System developed in this work demonstrates the potential of integrating Natural Language Processing, Machine Learning, and Large Language Model–driven reasoning into a unified, intelligent recruitment-support framework. The system successfully automates end-to-end resume evaluation by accurately extracting skills, experience, education, and project information from heterogeneous resume formats, and further enhances its utility by providing context-aware job-role recommendations using embedding-based similarity matching. The integration of a dynamic, conversational AI interview module significantly improves candidate readiness, as it not only generates highly relevant technical and behavioral interview questions but also evaluates user responses with near-human accuracy, offering constructive and personalized feedback. Experimental results obtained from diverse real-world resumes confirm that the system performs robustly across all major modules, delivering consistent accuracy, scalability, and adaptability while reducing dependency on manual evaluation and subjective decision-making. The system’s overall performance illustrates that AI-driven recruitment support tools can meaningfully improve both the efficiency of initial screening processes and the quality of candidate preparation, thereby benefiting students, job seekers, academic institutions, training platforms, and HR professionals.

References

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Copyright

Copyright © 2025 Ramesh B E, Pratham K R, Parimala Kadappa Biradar Patil, Arun Kumar V, Vidya V. 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.