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AdaptEd - Adaptive Learning Platform

🎓 Personalized Learning • 🤖 AI-Powered • 💻 Hands-on Coding

An intelligent learning platform that adapts to your pace and learning style

Built on AWS Cloud Infrastructure ☁️

📸 Screenshots

Dashboard - Learning Progress Overview

Dashboard Track your learning progress, streaks, XP, and upcoming modules

AI-Generated Lessons with Multi-Source Citations

Citations Lessons synthesized from multiple sources with Perplexity-style attribution

Comprehensive Study Notes

Notes AI-generated study notes with key takeaways and practice exercises

MCP-IDE - Integrated Development Environment

MCP-IDE Full-featured code editor with AI tutor, multi-file support, and real-time execution

Viva Voice - Voice-Based Examinations

Viva Voice Interactive voice assessments with speech-to-text and AI evaluation

Dyslexia-Friendly Mode

Dyslexia Mode Chrome extension providing accessible reading experience with OpenDyslexic font

🌟 Overview

AdaptEd is a comprehensive learning platform built on AWS cloud infrastructure, providing:

  • Personalized Learning Roadmaps - AI-generated paths using AWS Bedrock (Claude 3)
  • Interactive Lessons - Rich content with videos, documentation, and hands-on exercises
  • Viva Voice Examinations - Voice-based assessments powered by AWS Transcribe & Polly
  • Code Sandbox (MCP-IDE) - Full-featured IDE with AI tutor for coding practice
  • Progress Tracking - Detailed analytics stored in AWS DynamoDB
  • Accessibility Features - Dyslexia-friendly mode via Chrome extension

☁️ AWS-Powered Architecture

AdaptEd leverages multiple AWS services for enterprise-grade scalability and reliability:

  • AWS Bedrock (Claude 3) - AI content generation and intelligent tutoring
  • AWS DynamoDB - Serverless NoSQL database for user data and roadmaps
  • AWS Transcribe - Speech-to-text for voice examinations
  • AWS Polly - Text-to-speech for AI interviewer responses
  • AWS S3 - Secure file storage for audio and media
  • AWS EC2 - Scalable compute for application hosting

📁 Project Structure

AdaptEd/
├── frontend/              # Main React frontend (Port 5173)
├── backend/              # Python FastAPI backend (Port 8001)
├── mcp-ide/              # Integrated coding environment
│   ├── frontend/         # IDE React frontend (Port 5174)
│   └── backend/          # IDE Python backend (Port 8000)
├── sample-frontend/      # Demo/template frontend (Port 8080)
├── chrome-extension/     # Dyslexia mode extension
└── docs/                 # Documentation

🚀 Quick Start

Prerequisites

  • Node.js 18+ and npm
  • Python 3.8+
  • Git
  • AWS Account with access to:
    • AWS Bedrock (Claude 3 models)
    • AWS DynamoDB
    • AWS Transcribe
    • AWS Polly
    • AWS S3
  • AWS Credentials (Access Key ID and Secret Access Key)
  • Firebase Account (for authentication)

Installation

1. Clone the Repository

git clone <repository-url>
cd AdaptEd

2. Setup Main Frontend

cd frontend
npm install
cp .env.example .env
# Edit .env and add your Firebase credentials
npm run dev  # Runs on http://localhost:5173

3. Setup Main Backend (AWS-Powered)

cd backend

# Install Python dependencies
python -m venv venv
venv\Scripts\activate  # Windows
# source venv/bin/activate  # Mac/Linux
pip install -r requirements.txt

# Configure AWS credentials
cp .env.example .env
# Edit .env and add:
# - AWS_ACCESS_KEY_ID
# - AWS_SECRET_ACCESS_KEY
# - AWS_REGION (default: us-east-1)

# Set up AWS infrastructure (DynamoDB tables, S3 bucket)
python aws_setup.py

# Start backend
python -m uvicorn main:app --reload --port 8001

Note: You need to enable AWS Bedrock and request access to Claude 3 models in the AWS Console before running the backend.

4. Setup MCP-IDE Frontend

cd mcp-ide/frontend
npm install
npm run dev  # Runs on http://localhost:5174

5. Setup MCP-IDE Backend

cd mcp-ide/backend
python -m venv venv
venv\Scripts\activate  # Windows
pip install -r requirements.txt
cp .env.example .env
# Edit .env and add Supabase credentials
python -m uvicorn app.main:app --reload --port 8000

6. Setup Chrome Extension (Optional)

# Load unpacked extension in Chrome
# 1. Go to chrome://extensions/
# 2. Enable "Developer mode"
# 3. Click "Load unpacked"
# 4. Select AdaptEd/chrome-extension folder

🎯 Features

1. Personalized Learning Roadmaps (AWS Bedrock)

  • AI-generated learning paths using Claude 3 via AWS Bedrock
  • Adaptive content that skips what you already know
  • Weekly module structure with clear progression
  • Support for multiple tech stacks (Full-Stack, Frontend, Backend, DevOps, etc.)

2. Interactive Lessons (AWS Bedrock + Multi-Source)

  • Rich content with multiple sources:
    • YouTube video tutorials
    • MDN documentation
    • Interactive examples
  • AI-generated lesson content using AWS Bedrock (Claude 3)
  • Progress tracking per module
  • Note-taking and bookmarking

3. Viva Voice Examinations (AWS Transcribe + Polly)

  • Voice-based Q&A assessments powered by AWS services
  • 5 questions per module
  • Pass threshold: 60%
  • Speech recognition using AWS Transcribe
  • AI-powered evaluation using AWS Bedrock (Claude 3)
  • Natural voice responses using AWS Polly

4. Code Sandbox (MCP-IDE)

  • Full-featured code editor with Monaco
  • Multi-file support with file explorer
  • AI coding tutor (Shadow Tutor)
  • Code execution for JavaScript, Python, C++
  • Terminal integration
  • Code history and snapshots
  • RAG-powered context-aware assistance
  • Embedded in main app or standalone

5. Progress Dashboard (AWS DynamoDB)

  • Module completion tracking stored in DynamoDB
  • Streak counter
  • XP and leveling system
  • Viva score analytics
  • Weekly activity charts
  • Achievement badges

6. Accessibility

  • Dyslexia-friendly mode
  • Chrome extension for web-wide support
  • Customizable fonts and spacing
  • High contrast options

🔧 Configuration

Environment Variables

Main Frontend (.env)

VITE_FIREBASE_API_KEY=your_firebase_api_key
VITE_FIREBASE_AUTH_DOMAIN=your_project.firebaseapp.com
VITE_FIREBASE_PROJECT_ID=your_project_id
VITE_FIREBASE_STORAGE_BUCKET=your_project.appspot.com
VITE_FIREBASE_MESSAGING_SENDER_ID=your_sender_id
VITE_FIREBASE_APP_ID=your_app_id
VITE_API_URL=http://localhost:8001

Main Backend (.env)

# AWS Credentials (Required)
AWS_ACCESS_KEY_ID=your_aws_access_key_id
AWS_SECRET_ACCESS_KEY=your_aws_secret_access_key
AWS_REGION=us-east-1

# AWS DynamoDB Tables
DYNAMODB_USERS_TABLE=adapted-users
DYNAMODB_ROADMAPS_TABLE=adapted-roadmaps
DYNAMODB_VIVA_SESSIONS_TABLE=adapted-viva-sessions

# AWS S3 Bucket
S3_AUDIO_BUCKET=adapted-audio-files

# YouTube API (Optional)
YOUTUBE_API_KEY=your_youtube_api_key

MCP-IDE Backend (.env)

SUPABASE_URL=your_supabase_url
SUPABASE_KEY=your_supabase_anon_key
# Note: MCP-IDE can be migrated to AWS RDS if needed

Port Configuration

Service Port URL
Main Frontend 5173 http://localhost:5173
Main Backend 8001 http://localhost:8001
MCP-IDE Frontend 5174 http://localhost:5174
MCP-IDE Backend 8000 http://localhost:8000
Sample Frontend 8080 http://localhost:8080

📚 API Documentation

Main Backend Endpoints

Roadmap Generation

POST /generate-roadmap
Body: { goal, current_skills, time_commitment }

Lesson Content

POST /generate-lesson-content
Body: { topic, module_title, week }

Viva Examinations

POST /viva/start-simple?module_topic=...&user_goal=...
POST /viva/chat
Body: { session_id, user_text, module_topic }
POST /viva/complete
Body: { user_id, module_id, final_score }

MCP-IDE Backend Endpoints

File Management

GET /api/v1/files/projects
POST /api/v1/files/files
GET /api/v1/files/files/{file_id}
PATCH /api/v1/files/files/{file_id}

Code Execution

POST /api/v1/executor/run
Body: { code, language, project_id, file_path }

AI Tutor

POST /api/v1/tutor/ask
Body: { editor_state, user_question, model_type }

🎨 Tech Stack

Frontend

  • React 18 with TypeScript
  • Vite for build tooling
  • TailwindCSS for styling
  • Framer Motion for animations
  • Monaco Editor for code editing
  • React Query for data fetching
  • React Router for navigation
  • Firebase for authentication

Backend

  • FastAPI (Python)
  • AWS Bedrock (Claude 3) for AI generation
  • AWS DynamoDB for NoSQL database
  • AWS Transcribe for speech-to-text
  • AWS Polly for text-to-speech
  • AWS S3 for file storage
  • YouTube API for video search

Infrastructure

  • AWS EC2 for application hosting
  • AWS CloudWatch for monitoring and logging
  • AWS IAM for access management
  • Boto3 (AWS SDK for Python)

AI/ML

  • AWS Bedrock (Claude 3 Sonnet) - Content generation and conversation
  • AWS Transcribe - Speech recognition
  • AWS Polly (Neural) - Natural voice synthesis
  • Sentence Transformers - Code embeddings (MCP-IDE)
  • RAG - Context-aware tutoring

🧪 Testing

Frontend

cd frontend
npm run test
npm run test:coverage

Backend

cd backend
pytest
pytest --cov

MCP-IDE

cd mcp-ide/backend
pytest tests/

📖 User Guide

Getting Started

  1. Sign Up/Login - Use Google authentication
  2. Onboarding - Set your learning goal and current skills
  3. Roadmap Generation - AI creates your personalized path
  4. Start Learning - Follow modules week by week
  5. Take Vivas - Pass voice exams to unlock next modules
  6. Practice Coding - Use Code Sandbox for hands-on practice

Taking a Viva

  1. Navigate to "Viva Voice" in sidebar
  2. Select a module you've completed
  3. Click "Start Viva"
  4. Answer 5 questions using voice or text
  5. Get instant feedback and scoring
  6. Pass with 60% or higher

Using Code Sandbox

  1. Click "Code Sandbox" in sidebar
  2. MCP-IDE loads in embedded view
  3. Create files, write code, run programs
  4. Ask AI tutor for help
  5. Click maximize for fullscreen mode

🔒 Security

  • Firebase Authentication for user management
  • API key validation on all endpoints
  • CORS configuration for cross-origin requests
  • Environment variables for sensitive data
  • No PII stored in code or logs

🐛 Troubleshooting

Frontend won't start

# Clear node_modules and reinstall
rm -rf node_modules package-lock.json
npm install

Backend API errors

# Check if AWS credentials are set
cat backend/.env
# Verify AWS Bedrock access
aws bedrock list-foundation-models --region us-east-1
# Restart backend
python -m uvicorn main:app --reload --port 8001

AWS Bedrock Access Denied

  • Go to AWS Console → Bedrock → Model access
  • Request access to Claude 3 models
  • Wait for approval (usually instant)
  • Verify IAM permissions include bedrock:InvokeModel

DynamoDB Table Not Found

# Run AWS setup script to create tables
cd backend
python aws_setup.py

AWS Transcribe Errors

  • Verify audio file format (WAV, MP3, FLAC supported)
  • Check S3 bucket exists and has correct permissions
  • Ensure IAM role has transcribe:StartTranscriptionJob permission

MCP-IDE not loading

  • Verify MCP-IDE frontend is running on port 5174
  • Check browser console for CORS errors
  • Ensure Supabase credentials are correct (or migrate to AWS RDS)

Viva not working

  • Check if main backend is running on port 8001
  • Verify AWS Transcribe and Polly are enabled in your region
  • Test with text input first (fallback mode)

📝 Development

Adding a New Feature

  1. Create a branch: git checkout -b feature/your-feature
  2. Make changes
  3. Test thoroughly (including AWS integration)
  4. Commit: git commit -m "Add: your feature"
  5. Push: git push origin feature/your-feature
  6. Create Pull Request

Code Style

  • Frontend: ESLint + Prettier
  • Backend: Black + Flake8
  • Commits: Conventional Commits format

AWS Best Practices

  1. Use IAM roles instead of hardcoded credentials in production
  2. Enable CloudWatch logging for all services
  3. Set up billing alerts to avoid unexpected costs
  4. Use AWS Secrets Manager for sensitive data
  5. Enable DynamoDB point-in-time recovery for data protection

☁️ AWS Deployment

For detailed AWS deployment instructions, see AWS_DEPLOYMENT.md

Quick Deploy to AWS EC2

# 1. Launch EC2 instance (t3.medium recommended)
aws ec2 run-instances \
  --image-id ami-0c55b159cbfafe1f0 \
  --instance-type t3.medium \
  --key-name your-key-pair

# 2. SSH into instance
ssh -i your-key.pem ec2-user@your-instance-ip

# 3. Clone and setup
git clone https://github.com/your-repo/AdaptEd.git
cd AdaptEd/backend
pip3 install -r requirements.txt
python3 aws_setup.py

# 4. Run application
python3 -m uvicorn main:app --host 0.0.0.0 --port 8001

AWS Cost Estimation

For 100 active users per month:

  • AWS Bedrock: ~$30
  • DynamoDB: ~$1.25
  • Transcribe: ~$12
  • Polly: ~$8
  • S3: ~$0.30
  • EC2 (2x t3.medium): ~$60
  • Total: ~$116/month

See AWS_DEPLOYMENT.md for detailed cost breakdown and optimization tips.

🤝 Contributing

Contributions are welcome! Please:

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Add tests
  5. Submit a pull request

📄 License

[Your License Here]

👥 Team

[Your Team Information]

🙏 Acknowledgments

  • AWS for cloud infrastructure and AI services (Bedrock, DynamoDB, Transcribe, Polly)
  • Anthropic for Claude 3 AI models via AWS Bedrock
  • Firebase for authentication
  • Monaco Editor for code editing
  • All open-source contributors

📞 Support

  • Issues: [GitHub Issues]
  • Email: [support@adapted.com]
  • Docs: [Documentation Site]
  • AWS Support: For AWS-specific issues, contact AWS Support

🗺️ Roadmap

Current Version (v1.0)

  • ✅ Personalized roadmaps (AWS Bedrock)
  • ✅ Interactive lessons (AWS Bedrock)
  • ✅ Viva examinations (AWS Transcribe + Polly)
  • ✅ Code sandbox
  • ✅ Progress tracking (AWS DynamoDB)
  • ✅ AWS cloud deployment

Upcoming Features

  • 🔄 Mobile app
  • 🔄 Collaborative coding
  • 🔄 Live mentorship
  • 🔄 Certificate generation
  • 🔄 Community forums
  • 🔄 AWS Lambda serverless functions
  • 🔄 AWS CloudFront CDN integration

📊 Project Stats

  • Lines of Code: ~50,000+
  • Components: 100+
  • API Endpoints: 30+
  • Supported Languages: JavaScript, Python, C++
  • AI Models: AWS Bedrock (Claude 3)
  • Cloud Provider: AWS
  • Database: AWS DynamoDB
  • Speech Services: AWS Transcribe + Polly

Made with ❤️ by the AdaptEd Team | Powered by AWS ☁️ | Built with Claude 3 🤖

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