An Artificial Intelligence Powered Interview Preparation System using MERN Stack

Abstract

The Artificial Intelligence Preparation System is designed to support job seekers by enhancing their aptitude, data structures, and algorithm (DSA) skills while also providing AI-powered resume optimization and mock interviews. In today’s competitive job market, candidates often face challenges in finding structured preparation resources, refining their resumes, and gaining realistic interview practice. This system tackles these issues using Machine Learning (ML) and Natural Language Processing (NLP) technologies. It features three main components: a Prebuilt Question Bank, an AI-Powered Resume Analyzer, and an AI-Driven Interview Simulator. The Prebuilt Question Bank offers a curated set of aptitude and DSA questions categorized by difficulty level, helping users learn progressively while tracking their performance and receiving personalized study recommendations. The Resume Analyzer evaluates resumes by comparing them with job descriptions and suggests improvements in content and structure. It uses NLP for keyword extraction to ensure alignment with job roles, improving the chances of passing applicant tracking systems (ATS). The Interview Simulator replicates real-world technical interviews based on selected topics, analyzing user responses for technical accuracy, communication skills, and overall performance using AI models. Developed with the MERN (MongoDB, Express.js, React.js, Node.js) stack, the system integrates AI to improve resume assessment accuracy and enhance clarity in interview feedback. By offering structured learning, AI-driven insights, and realistic practice, this innovative platform empowers users to be more confident, competitive, and well-prepared for real-world hiring processes.

Introduction

This project develops an AI-powered interview preparation platform designed to help job seekers improve technical skills, optimize resumes, and practice mock interviews through a unified, user-friendly web application built with the MERN stack.

Key components include:

• Aptitude & DSA Practice Module: Offers a categorized question bank with real-time evaluation, detailed solutions, and performance tracking to enhance problem-solving skills.

• Resume Analyzer Module: Uses NLP and large language models to analyze resumes against job descriptions, providing match scores and actionable optimization feedback on keywords, formatting, and skill alignment.

• AI Interview Simulator Module: Simulates realistic interviews via voice and video, records responses, and uses ML to evaluate technical knowledge, communication, and confidence, delivering instant feedback.

The platform features a dashboard for easy access to all modules, stores user progress and feedback in a centralized database, and supports automated notifications to encourage consistent practice.

Technologies and Methods:

• MERN stack (MongoDB, Express, React, Node.js) for development and API integration.

• AI techniques including NLP (keyword extraction, sentiment analysis), speech recognition, and transfer learning with models like BERT and Whisper.

• Algorithms ranging from classical (sorting, searching, dynamic programming) to ML classifiers (logistic regression, SVM, decision trees) power skill assessment and feedback.

• Feature extraction from text and audio inputs ensures rich, meaningful analysis.

• An admin panel allows content management and user analytics.

Objective:
To bridge the gap between academic preparation and job market demands by providing a comprehensive, AI-driven system that boosts confidence and improves interview readiness, especially for fresh graduates.

Conclusion

The AI-Powered Interview Preparation System using the MERN stack offers an innovative and efficient solution for candidates preparing for interviews. By leveraging Machine Learning and Artificial Intelligence, the platform provides personalized learning, real-time feedback, and adaptive assessments to enhance users\' skills. The MERN stack (MongoDB, Express.js, React.js, Node.js) ensures a scalable, responsive, and high-performance application, making it accessible across devices. With AI-driven insights, automated evaluations, and an interactive user experience, this system empowers users to improve their problem-solving abilities and succeed in technical and behavioral interviews. In summary, this project bridges the gap between candidates and recruiters, offering a smart, data-driven approach to interview preparation while utilizing modern web technologies for an optimized user experience

References

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Copyright

Copyright © 2025 Dr. PVN Rajeswari , Mrs. CH Swapna. 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.