About This Architecture

TeachMate AI is a microservices-based LMS architecture combining React.js teacher dashboards, React Native student apps, and specialized AI services for intelligent tutoring. A central API Gateway with JWT authentication routes requests to four core microservices: AI Grading Service (OCR + Vision-Language Model), Knowledge Graph Service (Bayesian Knowledge Tracing + Graph Neural Networks), Question Generator Service (DistilBERT + Integer Linear Programming), and Personalized Learning Service (recommendation engine with logistic regression). All services persist to Firebase Realtime Database, while the recommendation engine integrates YouTube APIs and push notifications for student engagement. This architecture demonstrates how to decouple AI inference workloads, scale each service independently, and maintain secure, authenticated access across web and mobile clients. Fork this diagram on Diagrams.so to customize microservice boundaries, add AWS Lambda or SageMaker endpoints, or integrate additional ML models for your learning platform. Consider containerizing services with ECS Fargate for production deployment and adding CloudWatch monitoring for model performance tracking.

People also ask

How do you architect an AI-powered learning management system with separate microservices for grading, knowledge graphs, and personalized recommendations?

TeachMate AI uses a microservices pattern where a central API Gateway routes authenticated requests from teacher and student apps to four specialized services: AI Grading (OCR + Vision-Language Model), Knowledge Graph (Bayesian Knowledge Tracing + GNN), Question Generator (DistilBERT + ILP), and Personalized Learning (recommendation engine). All services write to Firebase Realtime Database, enabli