AI-Powered Mock Interview Web Application

Abstract

The recruitment process has evolved significantly with the adoption of Artificial Intelligence (AI), automation, and intelligent assessment technologies. Interview preparation remains one of the most challenging aspects for students, fresh graduates, and job seekers due to limited access to professional guidance, lack of realistic practice environments, and insufficient personalized feedback. Traditional mock interview approaches often depend on mentors, coaching institutes, or predefined questionnaires, which restrict flexibility and scalability. Recent developments in Natural Language Processing (NLP), Large Language Models (LLMs), and Generative Artificial Intelligence have enabled the creation of intelligent interview preparation platforms capable of generating contextual questions and evaluating candidate responses automatically. This survey paper reviews recent research in AI-based mock interview systems, behavioral analysis frameworks, remote hiring platforms, transformer-based language models, and generative AI technologies. Various methodologies, technologies, advantages, and limitations are analyzed through comparative evaluation. Based on identified research gaps, an AI-Powered Mock Interview Web Application is discussed that integrates Google Gemini AI, Next.js, MYSQL, Firebase, and Clerk authentication to provide personalized interview experiences, automated feedback, performance analytics, and multimodal interaction through text, voice, and video responses. The survey highlights the role of intelligent automation in improving interview readiness and discusses future directions including emotion recognition, multilingual support, adaptive learning mechanisms, and resume-based interview generation

Introduction

Interviews are a critical component of recruitment, academic admissions, and professional evaluations, assessing candidates' technical knowledge, communication, confidence, and problem-solving abilities. Traditional interview preparation methods, such as coaching centers and mock interviews, are often expensive, time-consuming, and provide limited personalized feedback. Recent advances in Artificial Intelligence (AI), particularly Generative AI and Large Language Models (LLMs) like Gemini and GPT, have enabled intelligent interview preparation platforms capable of generating role-specific questions, evaluating responses, and providing automated, personalized feedback.

The proposed AI-Powered Mock Interview Web Application addresses these challenges by offering a scalable web-based platform for personalized interview preparation. Users can configure interviews based on job role, skills, and experience level, after which Google Gemini AI dynamically generates relevant technical and HR questions. The system supports text, voice, and video-based interview sessions, evaluates candidate responses for relevance, clarity, technical accuracy, and communication skills, and provides constructive feedback. User authentication is managed securely through Firebase, while MySQL stores interview history, scores, and performance analytics. The frontend is developed using Next.js, React, and Tailwind CSS, providing a responsive and user-friendly interface.

The literature review shows that existing AI interview systems primarily focus on specific aspects such as emotion recognition, facial expression analysis, speech analysis, or static question generation using CNNs, LSTMs, NLP, and machine learning techniques. Although these systems improve interview assessment and reduce human bias, they often lack dynamic AI-based question generation, comprehensive analytics, scalable cloud architecture, and integrated database management. The proposed system overcomes these limitations by combining generative AI, hybrid cloud storage, performance tracking, and multimodal interview support into a unified platform.

The system follows a modular workflow consisting of user authentication, interview configuration, AI-driven question generation, response evaluation, secure data storage, and analytics visualization. After each interview, users receive detailed feedback and can monitor their progress through an interactive dashboard. Expected benefits include personalized interview preparation, adaptive AI-generated questions, automated evaluation, secure cloud-based data management, continuous performance tracking, improved accessibility, and enhanced interview confidence. Overall, the proposed platform provides an intelligent, scalable, and cost-effective solution for modern interview preparation while reducing dependence on human evaluators.

Conclusion

This paper reviewed recent advancements in AI-based mock interview systems and analyzed various approaches used for interview preparation and candidate evaluation. The study highlighted the growing role of Artificial Intelligence, Natural Language Processing, and Generative AI in providing personalized interview experiences and automated feedback. Based on the identified research gaps, an AI-Powered Mock Interview Web Application was proposed that integrates Google Gemini AI with modern web technologies to support dynamic question generation, response evaluation, and performance analytics. The proposed solution offers a scalable and intelligent platform for interview training, helping users improve their communication skills, technical knowledge, and overall interview readiness. The architecture also provides flexibility for future enhancements, making it suitable for both academic and professional applications.

References

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Copyright

Copyright © 2026 Hitesh Aghav, Atharva Waykar, Atharva Shinde, Atharva Shendkar, Prof. Zeba Vishwakarma. 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.