International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Preparing for job interviews effectively remains a critical challenge for job seekers, with many relying on traditional coaching methods that lack personalization, scalability, and real-time feedback mechanisms. Current interview preparation tools provide limited domain-specific guidance, lack interactive real-time evaluation, and fail to capture the nuanced assessment criteria that technical interviewers use. The absence of adaptive, AI-driven mock interview systems leaves candidates underprepared for behavioral, technical, and situational questions tailored to their target roles and experience levels.

To address these limitations, the AI-Powered Mock Interview Application integrates Generative AI, Large Language Models (LLMs), and Machine Learning to deliver personalized, interactive interview preparation at scale. The system leverages advanced NLP and transformer-based models to generate contextually relevant technical and behavioral questions based on job role, tech stack, and years of experience provided by users. Real-time speech-to-text conversion captures candidate responses, while AI-powered evaluation mechanisms assess communication clarity, technical accuracy, and confidence levels against industry benchmarks. The application provides instant, detailed feedback including answer quality assessment, improvement suggestions, performance analytics, and comparative metrics across multiple interview attempts.

Through a user-centric web platform, candidates access role-specific question banks, view personalized performance dashboards, and receive AI-generated recommendations for skill enhancement. Stakeholders including job seekers, career coaches, and educational institutions benefit from comprehensive analytics and interview metrics. By combining adaptive question generation with real-time speech analysis and predictive performance insights, the proposed solution significantly improves candidate confidence, reduces interview anxiety, and enhances job selection probability while democratizing access to high-quality interview preparation.

Downloads: | DOI: 10.17148/IJARCCE.2026.15131