A comprehensive AI-powered learning platform that personalizes education using Machine Learning and Large Language Models.

• Personalized Learning: AI analyzes your learning style and adapts content accordingly
• AI Tutor Chat: Real-time conversation with AI for explanations and help
• Custom Quizzes: Auto-generated quizzes based on your performance
• Progress Analytics: Visual dashboards showing your learning journey
• Adaptive Content: Lessons that adjust to your pace and understanding

• Student Analytics: Comprehensive insights into student performance
• Content Management: Create and manage courses, lessons, and assessments
• AI-Assisted Content: Generate lesson plans and quizzes with AI
• Class Management: Monitor multiple students and classes
• Performance Reports: Detailed reports on student progress

• Learning Style Detection: K-means clustering to identify learning patterns
• Performance Prediction: ML models predict student outcomes
• Sentiment Analysis: Analyze student feedback and engagement
• Content Generation: Gemini AI generates personalized lessons and explanations
• Adaptive Difficulty: Automatically adjusts content difficulty

• Framework: Django 4.2 + Django REST Framework
• Database: MongoDB (via djongo)
• AI/ML: Google Gemini API, scikit-learn, NLTK
• Authentication: JWT (Simple JWT)
• Task Queue: Celery + Redis
• Testing: pytest

• Framework: React 18
• State Management: Redux Toolkit
• UI Library: Material-UI (MUI)
• Charts: Recharts
• HTTP Client: Axios
• Routing: React Router v6

• Clustering: K-means for learning style detection
• Classification: Random Forest for performance prediction
• NLP: TextBlob for sentiment analysis
• LLM: Google Gemini for content generation

• Python 3.10+
• Node.js 18+
• MongoDB 6.0+
• Redis 7.0+
• Google Gemini API Key

git clone <repository-url>
cd "ai powered tutor"

# Create virtual environment
python -m venv venv

# Activate virtual environment
# Windows:
venv\Scripts\activate
# Linux/Mac:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Copy environment file
copy .env.example .env
# Edit .env with your configuration

# Run migrations
python manage.py migrate

# Create superuser
python manage.py createsuperuser

# Load initial data (optional)
python manage.py loaddata initial_data.json

# Start development server
python manage.py runserver

cd frontend

# Install dependencies
npm install

# Copy environment file
copy .env.example .env.local
# Edit .env.local with your configuration

# Start development server
npm start

# Windows (if Redis is installed)
redis-server

# Or use Docker
docker run -d -p 6379:6379 redis:alpine

# In a new terminal, activate venv and run:
celery -A backend worker -l info

1. Access the application:

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:8000/api
   • Admin Panel: http://localhost:8000/admin

2. Create accounts:

   • Register as a student or teacher
   • Or use the admin panel to create users

3. For Students:

   • Complete the learning style assessment
   • Browse available courses
   • Chat with AI tutor
   • Take quizzes and track progress

4. For Teachers:

   • Create courses and lessons
   • Use AI to generate content
   • Monitor student progress
   • Generate reports

ai-powered-tutor/
├── backend/                    # Django project
│   ├── settings.py
│   ├── urls.py
│   └── wsgi.py
├── apps/
│   ├── users/                 # User management & authentication
│   ├── courses/               # Course & lesson management
│   ├── assessments/           # Quizzes & tests
│   ├── ai_tutor/              # AI chat & content generation
│   ├── analytics/             # Progress tracking & analytics
│   └── ml_models/             # ML model training & inference
├── frontend/                  # React application
│   ├── src/
│   │   ├── components/       # Reusable components
│   │   ├── pages/            # Page components
│   │   ├── redux/            # State management
│   │   ├── services/         # API services
│   │   └── utils/            # Utility functions
│   └── public/
├── ml_models/                # Trained ML models
├── tests/                    # Test files
├── requirements.txt
└── README.md

pytest
pytest --cov=apps  # With coverage

cd frontend
npm test
npm run test:coverage

• JWT-based authentication
• Role-based access control (RBAC)
• CORS protection
• SQL injection prevention (via ORM)
• XSS protection
• CSRF protection
• Rate limiting on API endpoints
• Secure password hashing

• Uses K-means to identify 4 learning styles: Visual, Auditory, Reading/Writing, Kinesthetic
• Features: interaction patterns, content preferences, performance metrics

• Random Forest classifier predicts student success
• Features: quiz scores, time spent, engagement metrics, learning style

• Analyzes student feedback and chat messages
• Helps identify struggling students early

• Generates personalized lesson content
• Creates custom quizzes and questions
• Provides explanations in student's learning style
• Powers the AI tutor chat interface

docker-compose up -d

See DEPLOYMENT.md for detailed instructions.

API documentation is available at:

• Swagger UI: http://localhost:8000/api/docs/
• ReDoc: http://localhost:8000/api/redoc/

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

This project is licensed under the MIT License - see the LICENSE file for details.

• Your Name

• Google Gemini API for AI capabilities
• scikit-learn for ML models
• Django & React communities

For support, email [email protected] or open an issue in the repository.