Personalized Learning Co-pilot

A comprehensive AI-powered educational platform that provides tailored learning experiences based on student profiles using Retrieval-Augmented Generation (RAG).

Overview

The Personalized Learning Co-pilot is an educational technology solution that:

• Creates personalized learning plans using AI and student profile data
• Recommends relevant educational resources based on learning style, grade level, and interests
• Provides an interactive dashboard for tracking learning progress
• Supports various content types including videos, articles, interactive exercises, and quizzes
• Utilizes advanced RAG (Retrieval-Augmented Generation) techniques to deliver relevant content

Key Features

• Personalized Learning Plans: AI-generated learning paths customized to individual student needs
• Content Recommendations: Intelligent recommendation system based on student profile and learning progress
• Progress Tracking: Comprehensive dashboard to track completion and mastery across subjects
• User Profiles: Student profiles with learning style detection, grade level, and subject preferences
• Multimedia Support: Integration with various content types including videos, audio, and interactive elements
• Responsive Design: Mobile-friendly interface accessible on any device

Technologies

Frontend

• React: Modern component-based UI library
• React Router: Client-side routing
• TailwindCSS: Utility-first CSS framework for responsive design
• Axios: Promise-based HTTP client for API requests

Backend

• FastAPI: High-performance Python web framework
• LangChain: Framework for developing applications with LLMs
• Azure OpenAI: AI services for content generation and embeddings
• Azure AI Search: Vector database for semantic search capabilities
• Azure Cognitive Services: For content processing and analysis

Infrastructure

• Docker: Containerization for consistent deployment
• Azure: Cloud hosting and services
• Azure AI Search: Vector database for content storage and semantic search

Architecture

The system follows a modern microservices architecture:

1. User Interface Layer: React-based frontend with responsive design
2. API Layer: FastAPI backend providing RESTful endpoints
3. AI Services Layer: Integration with Azure OpenAI and LangChain
4. Persistence Layer: Azure AI Search for both structured data and vector search
5. Content Processing Layer: Services for analyzing and processing educational content

Getting Started

Prerequisites

• Node.js 16+
• Python 3.8+
• Docker and Docker Compose
• Azure OpenAI Service access
• Azure AI Search

Local Development Setup

1. Clone the repository

git clone https://github.com/yourusername/personalized-learning-copilot.git
cd personalized-learning-copilot

2. Set up environment variables

cp .env.example .env
# Edit .env with your credentials

3. Start the backend

cd backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
uvicorn app:app --reload

4. Start the frontend

cd frontend
npm install
npm start

Docker Setup

For a complete deployment with all services:

docker-compose up -d

This will start:

• Frontend on port 3000
• Backend API on port 8000
• All required services

API Documentation

Once the backend is running, you can access the API documentation at:

• http://localhost:8000/docs (Swagger UI)
• http://localhost:8000/redoc (ReDoc)

Content Personalization

The system personalizes learning content using:

1. Student Profile Analysis: Analyzing learning style, grade level, and interests
2. Content Embeddings: Vector representations of educational content for semantic search
3. Learning Progress Data: Adapting recommendations based on completed activities
4. RAG Techniques: Combining retrieval of relevant content with AI-generated learning plans

Demo and Screenshots

Dashboard

Learning Plan

Content Recommendations

Future Enhancements

• Advanced Analytics: Detailed insights into learning patterns and optimization
• Collaborative Learning: Group learning features and peer recommendations
• Content Creation: AI-assisted content generation for educators
• Mobile App: Native mobile applications for Android and iOS
• Offline Mode: Support for offline learning with synchronization
• AR/VR Integration: Immersive learning experiences

Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit your changes: git commit -m 'Add amazing feature'
4. Push to the branch: git push origin feature/amazing-feature
5. Open a Pull Request

License

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

Acknowledgments

• The ABC Education platform for educational content
• The Azure OpenAI team for AI capabilities
• The educational technology community for inspiration and standards# personalized_learning_copilot2