Smart Prep AI

Abstract

SmartPrep AI is an intelligent, cross-platform web-based application designed to revolutionize the way job seekers prepare for interviews. With the rising demand for employment and the competitive nature of job markets, interview readiness has become essential. Traditional mock interview methods often lack accessibility, personalization, and real-time feedback. To address these limitations, SmartPrep AI provides a complete AI-driven interview simulation platform that helps users enhance their verbal and non-verbal communication skills. The main motivation behind developing this system was to create an affordable, accessible, and intelligent platform that uses modern technologies to bridge the gap between academic knowledge and professional readiness. In SmartPrep AI, users can register as job seekers and take part in text-based or video-based interview simulations. The system generates questions relevant to the user’s selected domain and analyzes responses, Speech Analysis, and Body Language Detection. Real-time AI feedback is provided on various parameters such as tone, content relevance, knowledge, posture, emotion, speech analysis, eye contact, and hand gestures. A progress report system tracks user improvements over time, while an integrated job portal allows users to search and apply for jobs. Our application is developed using the MERN stack (MongoDB, Express.js, React.js, Node.js) for frontend and backend development, while Python-based AI modules are used for video and audio analysis. We integrated external AI APIs for response evaluation and feedback generation. For documentation and modeling purposes, Visual Paradigm was used for class, use case, and ER diagrams, and MS Word was used for report writing. Chapter 1 of the SmartPrep AI report introduces the project, its motivation, and methodology. Chapter 2 presents the background and literature review highlighting the need for AI-based interview training. Chapter 3 defines system requirements and includes detailed use case diagrams. Chapter 4 covers system design with architecture, flow, and data diagrams. Chapter 5 elaborates on the implementation with screenshots and technical details. Chapter 6 explains system testing through detailed test cases, and Chapter 7 concludes the report with the project’s impact, challenges, and future enhancements.