AI-Powered Online Examination System with Retrieval-Augmented Generation and Microservices Architecture

This paper presents an AI-powered online examination system designed using Retrieval-Augmented Generation (RAG) and a microservices-based architecture to improve scalability, security, and intelligent assessment generation. The system integrates large language models with retrieval mechanisms to generate context-aware examination content, automate evaluation workflows, and enhance user interaction. A microservices architecture is employed to ensure modularity, maintainability, and efficient deployment of independent services such as authentication, question generation, examination management, and result processing. The proposed system demonstrates how modern AI techniques can be integrated into online examination platforms to provide adaptive, scalable, and efficient educational solutions.