AI Mock Interview Platform for Performance Analysis

Abstract

Interview preparation has become increasingly complex due to the growing demand for technical competency, communication proficiency, behavioral intelligence, and real-time decision-making skills. Traditional mock interview methods often depend on human evaluators, resulting in subjective assessment, limited scalability, high operational costs, and inconsistent feedback mechanisms. This survey presents a comprehensive review of Artificial Intelligence (AI)-driven mock interview systems designed to automate and enhance interview preparation through intelligent candidate evaluation. The study explores recent advancements in Natural Language Processing (NLP), speech and emotion analysis, computer vision–based behavioral assessment, and Large Language Model (LLM)-based conversational agents for realistic interview simulation. Based on the analysis of existing approaches, a unified multimodal framework is proposed that integrates textual response evaluation, speech confidence analysis, facial expression recognition, sentiment understanding, and adaptive interview generation. The proposed architecture incorporates explainable AI for transparent feedback, visual analytics for progress monitoring, multilingual capabilities for broader accessibility, and cloud-based deployment for scalability. Additional features such as bias mitigation, learning platform integration, gamification, and secure data management further improve usability and effectiveness. The proposed intelligent framework aims to deliver objective, personalized, scalable, and cost-effective interview readiness assessment while addressing limitations of existing isolated evaluation systems.