IntelliLearn — AI-Powered Microservices Learning Platform

A modern, enterprise-grade Learning Management & Tutoring System built on a Microservices Architecture with an integrated AI Tutor (RAG Engine), Role-Based Workflows (Student & Instructor), and Keycloak SSO Authentication.

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Executive Overview

IntelliLearn is an intelligent, full-stack microservices platform designed for modern higher education and online learning. The system pairs course administration, assessment, and real-time student analytics with an AI-driven Intelligent Tutor powered by Retrieval-Augmented Generation (RAG) and OpenRouter LLMs.

Why Microservices?

• Horizontal Scalability: High-demand workloads (e.g. AI tutoring requests and course catalog browsing) scale independently without affecting core user authentication.
• Fault Isolation: An outage in analytics or quiz logging will not prevent students from accessing learning materials or taking courses.
• Service Decoupling: Independent services connect to isolated MongoDB database namespaces, enforcing strict domain boundaries.

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Key Features

👨‍🎓 Student Experience

• Interactive Course Catalog: Browse, search, filter, and enroll in curated technology courses.
• AI Command Terminal: Ask questions directly to an AI Tutor trained on course topics, request summaries, and get code explanations.
• Real-Time Progress Tracking: Dynamic progress bars, course completion percentages, and milestone badges.
• Interactive Quiz Assessments: Take quizzes with automated evaluation, score breakdowns, and attempt history.

👨‍🏫 Instructor Console (Full-Screen Dashboard)

• Course Authoring: Create, configure, and publish new courses with custom categories and modules.
• Quiz Creator: Dynamic multi-choice quiz builder with real-time correct answer selection and option management.
• Submissions & Performance Analytics: Monitor student quiz submission scores, completion dates, and overall assessment metrics.
• Dedicated Navigation: Independent, role-scoped workflow preventing accidental mixing with student views.

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System Architecture & Data Flow

All external client traffic passes through the API Gateway (port 8088), which handles rate-limiting, CORS, and OAuth2 JWT verification before proxying requests to downstream microservices.

graph TD
    Client["📱 React Frontend Client\n(Port 3000)"]
    
    subgraph Edge Layer
        Gateway["🌐 API Gateway\n(Port 8088)\n• Route Proxying\n• OAuth2 Security\n• Redis Rate Limiting"]
    end
    
    subgraph Microservices Layer
        UserService["👤 User Service\n(Port 8081 - Spring Boot)"]
        CourseService["📚 Course Service\n(Port 8082 - Spring Boot)"]
        QuizService["🧩 Quiz Service\n(Port 8083 - Spring Boot)"]
        ProgressService["📊 Progress Service\n(Port 8084 - Spring Boot)"]
        TutorService["🤖 AI Tutor Service (RAG)\n(Port 8085 - Spring Boot)"]
    end
    
    subgraph Infrastructure Layer
        MongoDB[(🍃 MongoDB Database Cluster\nuserdb, coursedb, quizdb, progressdb, tutordb)]
        Redis[(⚡ Redis Cache\nRate Limiting)]
        Keycloak[(🔑 Keycloak IAM\nOAuth2 & OIDC)]
    end

    Client -->|REST / JSON| Gateway
    Gateway -->|Rate Limit| Redis
    Gateway -->|JWT Validation| Keycloak
    Gateway -->|Route /api/users, /api/auth| UserService
    Gateway -->|Route /api/courses| CourseService
    Gateway -->|Route /api/quizzes| QuizService
    Gateway -->|Route /api/progress| ProgressService
    Gateway -->|Route /api/tutor| TutorService

    UserService --> MongoDB
    CourseService --> MongoDB
    QuizService --> MongoDB
    ProgressService --> MongoDB
    TutorService --> MongoDB

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Microservices Architecture Matrix

Service Name	Stack / Runtime	Port	Database	Primary Responsibilities
api-gateway	Spring Cloud Gateway	8088	Redis	Single entry point, CORS, rate limiting, and JWT validation
user-service	Spring Boot 3.3	8081	userdb	Authentication fallback, user registration, profiles & roles
course-service	Spring Boot 3.3	8082	coursedb	Course creation, catalog search, module management & enrollments
quiz-service	Spring Boot 3.3	8083	quizdb	Quiz builder, dynamic evaluation, submission history
progress-service	Spring Boot 3.3	8084	progressdb	Student milestone tracking, analytics, and completion percent
tutor-service	Spring Boot + RAG	8085	tutordb	OpenRouter LLM integration, AI tutoring chat, content summarizer
client	React 19 + Vite	3000	—	Single Page Application (SPA) with full mobile responsiveness

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OpenAPI & Swagger Documentation Hub

IntelliLearn exposes interactive Swagger Documentation across all microservices for inspecting raw OpenAPI specs and testing endpoints:

Documentation Interface	URL
API Gateway Aggregated Swagger	http://localhost:8088/swagger-ui.html
User Service Swagger	http://localhost:8081/swagger-ui.html
Course Service Swagger	http://localhost:8082/swagger-ui.html
Quiz Service Swagger	http://localhost:8083/swagger-ui.html
Progress Service Swagger	http://localhost:8084/swagger-ui.html
AI Tutor Service Swagger	http://localhost:8085/swagger-ui.html

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Tech Stack & Engineering Tools

• Frontend: React 19, React Router v7, Lucide Icons, Vanilla CSS (Design Tokens, Glassmorphism, Responsive Grid System)
• Backend & Gateway: Java 17, Spring Boot 3.3.2, Spring Cloud Gateway
• Databases & Cache: MongoDB Atlas / Local MongoDB 7.0, Redis 7.0
• AI / LLM: OpenRouter API (RAG Engine + Topic Summarizer)
• Security: Keycloak SSO (OAuth2 / OpenID Connect) + X-API-KEY microservice filter
• DevOps & Orchestration: Docker, Docker Compose, Nginx

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Quick Start & Deployment Guide

Prerequisites

• Docker Engine 24+
• Docker Compose v2+
• Node.js 20+ (for local client development)

1. Run via Docker Compose (Recommended)

To launch the complete ecosystem (10 containers) in detached mode:

docker compose up -d --build

Verify Running Containers

docker compose ps

Expected containers:

• api-gateway (:8088)
• client (:3000)
• user-service (:8081)
• course-service (:8082)
• quiz-service (:8083)
• progress-service (:8084)
• tutor-service (:8085)
• mongodb (:27017)
• redis (:6379)
• keycloak (:8180)

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2. Local Frontend Development

If you prefer running the React client locally while microservices run in Docker:

# Navigate to client directory
cd client

# Install dependencies
npm install

# Start Vite development server
npm run dev

Open http://localhost:3000 in your browser.

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3. Team Member Work Breakdown Matrix

Student Name / ID	Role	Microservice Name	Key Responsibilities & Endpoints
ITBIN-2313-0137	Gateway Lead & Member	user-service & course-service	API Gateway, OAuth 2.0, Rate Limiting, API Key Auth. Endpoints: /api/users/profile, /api/courses, /api/courses/{id}
ITBIN-2313-0007	Frontend Lead & Member	quiz-service, progress-service, tutor-service	React SPA Client, RAG LLM integration, API Key Auth. Endpoints: /api/quizzes/submit, /api/progress/{userId}, /api/tutor/chat

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Security & Environment Variables

Key service security headers:

• Gateway Entry: http://localhost:8088
• Microservice Key Header: X-API-KEY

Service	Environment Variable	Default Development Value
User Service	USER_SERVICE_API_KEY	user-service-secret-key-123
Course Service	COURSE_SERVICE_API_KEY	course-service-secret-key-456
Progress Service	PROGRESS_SERVICE_API_KEY	progress-service-secret-key-789
AI Tutor Service	OPENROUTER_API_KEY	(Set in .env)

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License

Distributed under the MIT License. See LICENSE for details.