AI-BOT Mock Interviewer
Keywords
Abstract
This paper presents an Innovative AI-Bot Mock Interviewer system designed to assist job seeker’s by providing real-time, intelligent feedback on virtual interviews. The system integrates advanced language models via OpenAI APIs within a secure Next.js, Drizzle ORM, and Clerk for secure user authentication. A key feature is its intelligent real time interview feedback mechanism. The frontend, built with Next.js, offers a smooth and interactive user experience. Evaluation results show the system delivers over 90% accurate and relevant feedback, with average response times under 2 seconds. This solution supports people by providing virtual mock interviews while preserving human oversight, ultimately enhancing interview quality and accelerating job seeker’s confidence
Copyright & License
Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this Article
Mr. Nagaraju Vassey, Mr. Manne Naga VJ Manikanth, Mr. MANTHRI GANESH RAO (2025). AI-BOT Mock Interviewer. International Journal of Technology & Emerging Research (IJTER), 1(3), 19-24
BibTeX
@article{ijter2025212505224711,
author = {Mr. Nagaraju Vassey and Mr. Manne Naga VJ Manikanth and Mr. MANTHRI GANESH RAO},
title = {AI-BOT Mock Interviewer},
journal = {International Journal of Technology & Emerging Research },
year = {2025},
volume = {1},
number = {3},
pages = {19-24},
issn = {3068-109X},
url = {https://www.ijter.org/article/212505224711/ai-bot-mock-interviewer},
abstract = {This paper presents an Innovative AI-Bot Mock Interviewer system designed to assist job seeker’s by providing real-time, intelligent feedback on virtual interviews. The system integrates advanced language models via OpenAI APIs within a secure Next.js, Drizzle ORM, and Clerk for secure user authentication. A key feature is its intelligent real time interview feedback mechanism. The frontend, built with Next.js, offers a smooth and interactive user experience. Evaluation results show the system delivers over 90% accurate and relevant feedback, with average response times under 2 seconds. This solution supports people by providing virtual mock interviews while preserving human oversight, ultimately enhancing interview quality and accelerating job seeker’s confidence},
keywords = {Mock Interviewer; Artificial Intelligence; Drizzle ORM; Clerk; Interview Quality},
month = {Jul},
}