LiberiaLearn — Production AI Education Platform
🚀 AI-powered platform designed for national-scale deployment (1M+ students, 5,000+ schools)
What This Is
LiberiaLearn is a full-stack, production-grade AI system — not a demo — built to deliver real-world education infrastructure.
Built and operated by a single engineer (end-to-end system ownership), the platform is designed to function as national education infrastructure.
It combines:
- RAG-based AI tutoring (grounded responses, not hallucinations)
- Multi-tenant SaaS architecture (students, teachers, admins, MOE)
- Offline-first learning system for low-connectivity environments
- AWS-backed distributed infrastructure
- AI-driven workflows for tutoring, grading, and curriculum generation
Live System
👉 https://liberia-learn.vercel.app
Source Code
👉 https://github.com/fasiryon/liberia-learn
Key Metrics
- Tests: 1,577 passing
- Test files: 214
- API routes: 189
- Prisma models: 81
- Curriculum: 1,306 READY lessons
- Avg lesson length: 1,450 words
Traction
- 9,000+ repository interactions in recent weeks
- Increasing developer and system-level interest
Technical Highlights
- Multi-tenant role system (Student, Teacher, Guardian, Admin, MOE)
- Full App Router platform with 189 route handlers
- Prisma domain model with 81 models (users, curriculum, AI usage, governance)
- RAG-based AI architecture with retrieval + grounding + cost controls
- Offline-first system with IndexedDB sync + resumable sessions
- Immutable audit logging and governance reporting
- SLO tracking, health monitoring, and incident-ready architecture
- Full curriculum system with 1306/1306 lessons READY
AI System Design
- Retrieval: pgvector cosine similarity over curriculum chunks
- Grounding: responses constrained strictly to retrieved context
- Providers: OpenAI (primary), Groq (fallback routing)
- Prompting: structured templates with role, context, and curriculum injection
- Evaluation: recall@k and precision@k used for internal validation
- Cost Control: per-request tracking, rate limiting, and usage constraints
Engineering Decisions
- RAG used instead of fine-tuning to ensure explainability and curriculum grounding
- Multi-provider routing implemented to prevent vendor lock-in and increase reliability
- Offline-first architecture chosen due to unreliable connectivity environments
- Distributed workers (ECS + SQS) used for async scalability and background processing
- Observability built into core system for production readiness (CloudWatch + logs)
- Multi-tenant RBAC enforced at both API and middleware layers
Architecture
Repository Structure
app → UI + API route handlerscomponents → reusable UI componentslib → auth, AI orchestration, services, telemetryprisma → schema, migrations, seedsworker → background job processingscripts → audits, pipelines, maintenancedocs → architecture, governance, operationstests → Vitest suitesinfra → deployment and infrastructure
Local Setup
npm install
npx prisma generate
npx tsc --noEmit
npx vitest run
npm run build
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## Environment
Typical files:
• .env
• .env.local
• .env.production
See ENVIRONMENTS.md for full details.
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## Documentation
Document
Purpose
ARCHITECTURE_EXECUTIVE.md
Technical overview
API_REFERENCE.md
API documentation
MOE_TECHNICAL_BRIEF.md
Ministry-facing explanation
SYSTEM_ARCHITECTURE.md
Deep system design
SECURITY_MODEL.md
Security controls
SCALE_READINESS.md
Scalability analysis
INCIDENT_RESPONSE.md
Incident handling
WORKER_DEPLOYMENT.md
Background worker setup
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## Current Status
• Sprint 8 complete
• Sprint 9 documentation in progress
• Build validated via:
• npx tsc --noEmit
• npx vitest run
• npm run build
• Curriculum: 1306/1306 lessons READY
Execution tracking lives in [CURRENT_EXECUTION_STATE.md](C:/Users/fasir/liberia-learn/docs/roadmaps/CURRENT_EXECUTION_STATE.md).
