FutureThink Edge - AI-Powered Adaptive Learning Platform
A production EdTech platform for students with ADHD and learning differences, featuring AI-powered adaptive learning, multi-role portals, and real-time collaboration.
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Click the image for better qualityProject Overview
FutureThink Edge is a comprehensive AI-powered adaptive learning platform specifically designed for students with ADHD and learning differences. The platform serves 3,000+ concurrent users across multiple roles (Students, Teachers, Parents, Admins, Organizations) with features including AI-powered tutoring, real-time collaboration, gamification, mental health monitoring, and advanced analytics. Fully deployed on Render with PostgreSQL database and Redis caching.
My Role & Responsibilities
Main Role:
Junior Full-Stack DeveloperKey Responsibilities/Focus Areas:
Backend Developer, Database Administrator, DevOps Engineer, AI Integration Specialist
Key Frontend Features
- Integrated backend APIs with existing Next.js 15.3 frontend components using TypeScript.
- Connected database endpoints to UI components for seamless data flow and state management.
- Adjusted UI/UX elements using Tailwind CSS v4 to improve user experience and visual consistency.
- Modified existing React hooks and components to support new backend functionality.
Key Backend Features
- Built 40+ RESTful API endpoints using FastAPI handling authentication, learning sessions, analytics, and admin functions.
- Designed and implemented 42+ database models with SQLAlchemy ORM supporting complex role-based relationships.
- Developed JWT authentication system with role-based access control (RBAC) for 5 user types (Student, Teacher, Parent, Admin, Organization).
- Implemented Redis caching strategy with connection pooling (pool size: 20, max overflow: 40) supporting 3,000+ concurrent users.
- Created WebSocket server with room-based broadcasting for real-time collaboration and notifications.
- Built 25+ database migration scripts ensuring zero-downtime production deployments.
- Integrated 3 AI providers (OpenAI, Groq, Google Gemini) with orchestration layer for optimal performance.
- Developed audit logging system for FERPA, COPPA, and GDPR compliance.
- Created rate limiting and security measures preventing account enumeration and brute-force attacks.
Challenges & Solutions
Supporting 3,000+ concurrent users without performance degradation.
Implemented Redis caching with multiple cache layers (SmartCache, AnalyticsCache, AICache), configured database connection pooling with pool size of 20 and max overflow of 40, and optimized database queries.
Managing complex role-based access across 5 different user types with varying permissions.
Designed a comprehensive RBAC system with JWT tokens, middleware-based permission checks, and database-level row security for sensitive data.
Integrating multiple AI providers while maintaining consistent response quality.
Built an AI orchestration layer that dynamically selects providers based on availability, cost, and latency. Optimized LLM prompts for the AI Classroom to improve student engagement.
Ensuring zero-downtime deployments with database schema changes.
Created 25+ incremental migration scripts with rollback capabilities, tested migrations in staging environment before production deployment.
Real-time collaboration features across multiple user sessions.
Implemented WebSocket server with room-based broadcasting, allowing targeted notifications and live updates without overwhelming the server.