AI-Powered Mock Interview System for Automated Skill Assessment

Abstract

The present paper discusses in detail the design and validation of an Al-powered virtual mock interview system. Leveraging the MERN stack, the system employs natural language processing, semantic similarity algorithms, and speech recognition to evaluate user responses based on semantic accuracy. A validation study involving 20 students demonstrated a 23% improvement in interview performance, with 85% self-reporting a confidense boost.

Introduction

The text discusses the development of an AI-powered interview preparation and assessment system designed to address limitations of traditional mock interviews, such as inconsistent feedback, scalability issues, and accessibility barriers. Leveraging automation, Natural Language Processing (NLP), and Automatic Speech Recognition (ASR), the system transcribes spoken answers, evaluates responses against predefined rubrics, and delivers real-time, personalized feedback, improving candidate learning, retention, and confidence.

The system architecture is modular, integrating backend data management, model processing, and a responsive frontend interface to ensure scalability, usability, and smooth operation across multiple institutions. Experimental validation with 200 student sessions showed high scoring accuracy, consistency with expert evaluation, user satisfaction, and faster assessment compared to traditional methods.

Challenges include transcription errors in noisy environments, model bias, context misinterpretation, high computational demands, real-time processing constraints, and ethical concerns related to data security, consent, and algorithmic transparency.

Future enhancements aim to incorporate multimodal analysis (facial and gesture recognition), adaptive personalized feedback, and support for multiple languages and job domains, further improving the system’s effectiveness and accessibility.

Conclusion

Experimental results confirm the educational effect of the system. With continued advancements in multimodal and adaptive learning, the use of Al-driven mock interviews will soon be the standard to prepare job candidates effectively. This research presents a comprehensive AI-powered mock interview platform that incorporates natural Language processing, automatic speech recognition, and real-time feedback mechanisms to provide accessible scalable interview preparation. Built on the MERN stack architecture, the system leverages transformer-based language models for semantic answer evaluation, Web Speech API for voice-enabled interactions, and data-driven analytics for personalized improvement recommendations. Experimental validation revealed significant quantitative enhancements: 23% average answer quality increase, while 85% reported confidence boost-alongside strong user. The satisfaction metrics also establish the system\'s effectiveness as an interview preparation tool. Technical performance metrics including 95.45% evaluation accuracy and sub-second Feedback latency confirms the system\'s capability for real-world deployment. Open source, Cost-effective architecture democratizes access to quality interview coaching, thus addressing: equity concerns in career preparation.This research contributes towards Educational Technology by showing how AI-driven feedback can Loops accelerate skill development by providing immediate, objective assessment in combination with personalized guidance. The approach to semantic similarity using sentence embeddings and cosine similarity offers a scalable, reproducible methodology for automated answer evaluation applicable across domains. Integration of voice interaction enhances realism and engagement.as opposed to text-based systems, prepare candidates more for actual interview conditions.

References

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Copyright

Copyright © 2025 Dr. Vijayant Verma, Rana Padwar, Apurwa Chandrakar, Khushi Jaiswal, Palak Mishra. 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.