Intelligent Interview Preparation System

Abstract

Preparing for job interviews is a significant challenge for students, especially when they lack access to mentors or regular interview practice. This paper presents an AI-powered interview coaching system that automatically generates interview questions, evaluates candidate responses, and provides real-time confidence-based feedback. The system uses resume information to create personalized questions and guides users with constructive feedback based on speaking clarity, confidence level, and answer quality. The platform is developed using a modern web stack with a Next.js frontend and a Convex serverless backend, making it fast, scalable, and accessible across devices. A small pilot test conducted with students indicated that the system helped reduce hesitation and improved interview readiness. The paper discusses the system architecture, module design, implementation details, and observed results. This work demonstrates how artificial intelligence can effectively support interview preparation and confidence building among learners.

Introduction

The text presents an AI-powered interview coaching system designed to help students prepare for job interviews. Many students struggle with interviews due to lack of practice, stage fear, and limited access to expert guidance. Traditional mock interviews require experienced mentors and significant resources, making them difficult for many learners to access. The proposed system uses artificial intelligence to simulate interview environments, generate personalized questions, analyze responses, and provide feedback, making interview preparation more accessible, scalable, and affordable.

The key contributions of the project include the development of a complete AI-based mock interview platform, resume-based personalized question generation, confidence evaluation using speech characteristics, a scalable serverless backend architecture, and pilot testing showing improved user confidence.

Existing interview preparation tools mostly rely on static question banks or limited chatbot interactions, while human-led mock interviews are effective but costly and time-consuming. Although some modern systems use generative AI and speech analysis, few integrate resume analysis, adaptive question generation, confidence detection, and feedback in one platform, which motivates the proposed solution.

The system uses a modular client–server architecture with Next.js for the frontend, Convex serverless functions for the backend, AI services for question generation and evaluation, and ImageKit for resume storage. The workflow involves uploading a resume, extracting skills, generating interview questions, collecting user responses (voice or text), analyzing answers, calculating a confidence score, and providing feedback.

Key modules include:

• Resume Parsing Module: Extracts skills and project information.

• Generative Questioning Module: Creates personalized interview questions.

• Confidence Detection Module: Analyzes speech features such as pace, clarity, and pauses.

• Feedback Module: Suggests areas for improvement.

• Backend Module: Manages sessions and APIs.

The system was implemented using Next.js, Convex, ImageKit, browser audio APIs, and AI services. Testing results showed that personalized questions improved relevance, confidence analysis helped users identify hesitation, practice sessions were short (10–15 minutes), and the system responded quickly due to the serverless architecture. Overall, the system improves interview preparedness and reduces user anxiety through personalized practice and instant feedback.

Conclusion

This paper presented an AI-powered interview coaching system that combines resume analysis, generative question creation, confidence detection, and structured feedback within a web-based platform. The system effectively simulates key aspects of real interview scenarios while remaining easily accessible through a browser. Pilot testing showed that users experienced improved confidence, reduced hesitation, and better interview readiness after using the system. The results demonstrate the potential of AI-based educational tools in supporting skill development and career preparation. By offering a scalable, cost-effective, and automated alternative to traditional mock interviews, the proposed system can be useful for students, training institutes, and placement preparation programs.

References

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Copyright

Copyright © 2026 Janhvi Dhage, Janvi Bhise, Vaidehi Turkar, Shrushti Dokarimare. 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.