AI Interview System: AI-Powered Smart Interview Management System

Abstract

The increasing competitiveness of the job market necessitates effective, scalable interview-preparation tools. This paper presents an AI-powered Mock Interview System that simulates real-time interview scenarios using Artificial Intelligence (AI), Natural Language Processing (NLP), and speech technologies. The system generates dynamic, role-specific questions and supports both text-based and voice-based interactions through speech-to-text (STT) and text-to-speech (TTS). It evaluates user responses in real time and provides personalized feedback based on communication clarity, correctness, and problem-solving ability. The platform is developed using modern web technologies, including Next.js and React for the frontend, along with a scalable backend integrated with AI models and a relational database. Experimental results demonstrate reliable speech recognition performance and strong alignment between AI-generated evaluations and human assessments. The proposed system enhances interview preparedness by offering an intelligent, accessible, and interactive solution, with potential applications in education, recruitment, and professional skill development.

Introduction

The study presents an AI-Powered Interview System designed to improve interview preparation by overcoming the limitations of traditional methods such as static question banks, coaching dependency, and lack of real-time interaction. With increasing competition in the job market, candidates need technical knowledge along with communication and problem-solving skills. The proposed system uses Artificial Intelligence (AI), Natural Language Processing (NLP), speech technologies, and modern web frameworks to create a realistic and personalized mock interview environment.

Traditional interview preparation platforms often fail to provide adaptive learning, voice-based interaction, and instant feedback. The proposed system addresses these challenges by generating dynamic role-specific questions, analyzing user responses, and providing real-time performance evaluation without requiring human interviewers.

The main objectives of the system are:

• Generate AI-based interview questions according to job role and difficulty level.
• Enable voice interaction using Speech-to-Text (STT) and Text-to-Speech (TTS) technologies.
• Evaluate responses using NLP techniques.
• Provide personalized feedback and performance analysis.
• Support different interview categories such as technical and behavioral interviews.

The literature review highlights advancements in deep learning-based speech processing, transformer-based language models, AI tutoring systems, and NLP techniques. Technologies such as GPT-based models and speech APIs have enabled intelligent question generation, semantic analysis, and realistic human-computer interaction. However, existing platforms still face limitations related to scalability, personalization, and real-time feedback.

The proposed methodology follows a modular architecture consisting of:

• Frontend: Developed using Next.js and React for an interactive user interface.
• Backend: Built using Node.js to manage APIs and system logic.
• Database: Uses scalable databases such as PostgreSQL or MongoDB for storing user profiles, responses, and feedback.
• AI Module: Uses generative AI for question creation and NLP-based response evaluation.

The workflow begins with user authentication and interview configuration, where users select job role, interview type, and difficulty level. The AI engine then generates customized questions. Users answer through text or voice, and speech responses are converted into text using STT technology. NLP algorithms evaluate responses based on grammar, relevance, clarity, and problem-solving ability. The system then provides instant feedback, highlighting strengths and improvement areas.

Testing results show that the system successfully created an interactive interview experience. The speech recognition module provided accurate voice-to-text conversion, while the NLP module effectively analyzed responses. The platform maintained quick response times, making it suitable for real-time usage. User testing indicated improved confidence, better preparation, and increased awareness of personal strengths and weaknesses.

The developed system includes several interfaces:

• Landing Page: Provides platform overview and authentication options.
• Dashboard: Displays user history, interview sessions, and performance analytics.
• Interview Setup Page: Allows selection of role, difficulty, and interview type.
• Interview Session Interface: Enables AI-driven questions with text/voice responses.
• Feedback Page: Generates detailed performance reports and improvement suggestions.

Conclusion

The AI Mock Interview System presents an intelligent and scalable solution for enhancing interview preparation by integrating Artificial Intelligence, Natural Language Processing, and speech technologies. It effectively simulates real-time interview scenarios through dynamic question generation and supports both text and voice interactions, providing users with a realistic experience. The system evaluates responses and delivers personalized feedback, helping users improve communication, confidence, and problem-solving skills. Unlike traditional methods, it offers accessibility, adaptability, and continuous learning without the need for human evaluators. Experimental results indicate improved user performance and satisfaction, demonstrating the system’s effectiveness. Overall, the proposed system bridges the gap between conventional preparation techniques and modern recruitment demands, making it a valuable tool for students, job seekers, and professionals.

References

[1] Amodei, D., et al. (2016). Deep Speech: Scaling up end-to-end speech recognition. arXiv. [2] Lee, S., et al. (2019). Impact of Intelligent Tutoring Systems on Personalized Learning. JETS. [3] Jurafsky, D., & Martin, J. H. (2020). Speech and Language Processing (3rd ed.). Pearson. [4] Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial Intelligence in Education. [5] Kumar, P., et al. (2025). AI-Powered Mock Interview Generator IRJET. [6] Pramp. (n.d.). Live peer-to-peer mock interviews. [7] Interviewing.io. (n.d.). Anonymous technical interview practice.

Copyright

Copyright © 2026 Sanjay M, Hamsa B C, Akarsh M K, Deeksha T R, Deekshith H Y, Mohan G R. 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.