AI Smart Interview Coach: A Web-Based System for Automated Interview Preparation and Evaluation

Abstract

Students and job seekers who want to be successful in the hiring process must prepare for interviews. Static question banks, simulated interviews, and self-study resources are examples of traditional preparation techniques that frequently lack automated assessment and organized feedback. The AI Smart Interview Coach, a web-based tool that allows users to rehearse interview questions and get an automatic assessment using Natural Language Processing (NLP) techniques, is presented in this study. Users of the system can respond to interview questions from a variety of categories, including verbal, technical, aptitude, and HR. A rule-based NLP evaluation engine is used to assess user replies, producing scores and feedback through text preprocessing, tokenization, keyword extraction, and coverage ratio analysis. The responsive user interface is created using HTML, CSS, and Javascript, and the system is constructed using Python and the Flask framework for backend processing. The database used to store user information, interview questions, and assessment findings is SQLite. Additionally, the system offers a performance dashboard that monitors user tries and scores over several sessions. The findings of the experiment show that the suggested platform efficiently assesses user answers and offers helpful criticism, assisting users in becoming more prepared for interviews.

Introduction

The text presents the AI Smart Interview Coach, a web-based platform designed to help students and job seekers prepare for interviews by providing automated evaluation and feedback. Traditional methods of interview preparation, such as books, online questions, or mentor-led mock interviews, often lack structured feedback and real-time assessment. The proposed system integrates Natural Language Processing (NLP) with web technologies to analyze candidate responses, evaluate answers based on keyword matching, generate scores and suggestions, and track performance via a dashboard.

The system features a three-tier architecture (presentation, application, and database layers) with modules for user authentication, interview practice, answer evaluation, feedback generation, and performance monitoring. Technologies used include Python Flask for backend, HTML/CSS/JavaScript for frontend, and SQLite for data management. Functional testing and usability observations show that the platform effectively supports continuous practice, real-time feedback, and performance tracking, though limitations include potential evaluation errors due to synonyms and scalability issues with large datasets. Overall, the AI Smart Interview Coach provides a structured, interactive, and accessible tool for improving interview readiness.

Conclusion

Through automated evaluation and structured practice modules, the AI Smart Interview Coach system was created as a web-based platform to help students and job seekers improve their interview preparation. The system creates an interactive learning environment where users may practice interview questions and get feedback on their answers by fusing natural language processing techniques with contemporary web technology. Learning courses, practice exams, interview simulations, and a performance dashboard that monitors user advancement are just a few of the modules offered by the platform. Together, these sessions offer a whole interview preparation experience. The system architecture guarantees effective communication between the SQLite database used to store user data and evaluation results, backend processing modules, and the frontend interface. It was discovered through system testing and evaluation that the platform effectively carries out essential features such presenting interview questions, evaluating user responses, producing scores, and storing data for performance monitoring. Users are encouraged to improve their responses through repeated practice and are assisted in identifying concepts that are lacking from their responses by the automatic feedback process. The platform shows the promise of integrating Natural Language Processing techniques to enhance interview preparation and skill development, even though the current implementation concentrates on keyword-based evaluation.

References

[1] “Smart Prep: AI-Based Interactive Interview Preparation System,” ResearchGate, 2025. [2] “AI-Powered Mock Interview Platform with NLP and Speech Analysis for Personalized Feedback,” ResearchGate, 2025. [3] “AI-Based Interview Preparation System,” Journal of Engineering Research and Reports (JERR), 2024 [4] S. V. Golande et al., “Mock Interview Evaluator Powered by AI,” Excel International Journal of Technology, Engineering and Management, vol. 12, no. 2, pp. 80–87, 2025. [5] “AI-Driven Real-Time Interview Simulation App with Voice Recognition and Facial Analysis,” Indian Journal of Science and Technology, 2024. [6] “AI-Based Interview Preparation System,” International Journal of Innovative Research in Multidisciplinary Projects (IJIRMPS), 2025. [7] T. T. H. Nguyen et al., “SimInterview: LLM-Based Multilingual Interview Training System,” arXiv preprint arXiv:2508.11873, 2025. [8] “AI-Based Interview Preparation Platform,” International Journal of Innovative Research in Technology (IJIRT), 2024. [9] “AI-Based Interview Preparation and Evaluation System,” IEEE Conference Publication, 2025. [10] “AI-Based Interview Preparation System,” International Journal of Innovative Research in Multidisciplinary Projects (IJIRMPS), 2025. [11] “AI-Based Mock Interview System,” International Journal of Research Publication and Reviews, 2025. [12] “AI-Powered Mock Interview Preparation System,” International Journal for Modern Trends in Science and Technology, 2025. [13] “AI-Driven Mock Interview System Using NLP and Speech Analysis,” Journal of Advanced AI Research, 2025. [14] “AI-Based Mock Interview Evaluation Using NLP and Voice Analysis,” International Journal of Advanced Research in Engineering and Science, 2024. [15] “AI-Powered Mock Interview System for Automated Skill Assessment,” IJRASET, 2026. [16] “AI MockPrep: AI-Driven Interview Simulation System,” International Journal of Engineering Research and Technology (IJERT), 2024.

Copyright

Copyright © 2026 Potala Bhargavi, Medaboina Gnapika, Maruvada Sai Harshitha, Seelam Chaitanya Sai, Kagitapalli Charan Sree Akhilesh, Onteddu Siva Ramakrishna. 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.