Leveraging NLP and LLMs for Intelligent Mock Interview Systems

Abstract

This paper presents the design and implementation of an LLM and NLP-powered Mock Interview Application that provides realistic, AI-driven interview simulations. The system integrates Large Language Models (LLMs) for dynamic question generation, contextual follow-ups, and personalized feedback, while Natural Language Processing (NLP) enables semantic understanding, sentiment analysis, and communication skill evaluation. Text-to-Speech (TTS) models facilitate voice-based interaction, and Machine Learning (ML) algorithms assess user responses based on content relevance, tone, and confidence. The modular architecture encompasses user authentication, interview setup, question generation, speech analysis, and performance visualization. Evaluation metrics include response accuracy, fluency, and emotional tone derived through NLP and LLM-based analysis. Future enhancements target multimodal emotion recognition, eye-contact detection, and multilingual support, positioning the platform as an advanced AI-driven career readiness and communication training assistant.