PersonalAITutor - AI-Powered Learning Platform

Built with AutoGen 0.4.8, AItution leverages multiple AI agents to create a personalized learning experience:

• Assessment Agent: Evaluates student knowledge through interactive conversations
• Content Creation Agent: Generates customized course content based on assessment results
• Course Plan Reviewer Agent: Ensures high-quality and structured learning materials

Overview

AItution is an innovative AI-powered learning platform that creates personalized learning experiences. The platform uses advanced AI agents to:

1. Assess student knowledge and capabilities through interactive conversations
2. Generate customized course content based on assessment results
3. Create structured learning modules with quizzes and exercises
4. Provide real-time feedback and adaptive learning paths

Features

• 🤖 AI-powered assessment system
• 📚 Automated course content generation
• 🎯 Personalized learning paths
• 📝 Interactive quizzes and exercises
• 📊 Progress tracking
• 🔄 Real-time content adaptation
• 🌐 Web-based interface

Prerequisites

• Python 3.8 or higher
• Node.js 14+ (for frontend)
• OpenAI API key
• SQLite3

Installation

1. Clone the repository:

git clone https://github.com/yourusername/AItution.git
cd AItution

2. Create and activate a virtual environment:

python -m venv venv
# On Windows
venv\Scripts\activate
# On macOS/Linux
source venv/bin/activate

3. Install backend dependencies:

pip install -r requirements.txt

4. Install frontend dependencies:

cd frontend
npm install

Environment Setup

1. Navigate to the config directory:

cd config
cp secrets.py.example secrets.py

2. Edit secrets.py with your configuration:

OPENAI_API_KEY = 'your-openai-api-key'
FLASK_SECRET_KEY = 'your-secret-key'

3. Set up environment variables (optional):

# On Windows
set FLASK_ENV=development
set OPENAI_API_KEY=your-api-key

# On macOS/Linux
export FLASK_ENV=development
export OPENAI_API_KEY=your-api-key

Running the Application

1. Start the backend server:

cd backend
python server.py

2. Start the frontend development server:

cd frontend
npm start

3. Access the application at http://localhost:3000

API Documentation

Assessment Endpoints

Start Assessment

• POST /api/assessment/start
• Starts a new assessment session
• Returns: { "success": true, "session_id": "uuid" }

Get Question

• GET /api/assessment/question
• Retrieves the next question in the assessment
• Returns: { "success": true, "question": "...", "formatted_question": {} }

Submit Answer

• POST /api/assessment/answer
• Submit answer to current question
• Body: { "answer": "user's answer" }

Get Assessment Result

• GET /api/assessment/result
• Retrieves final assessment results
• Returns: { "success": true, "assessment": {} }

Content Creation Endpoints

Start Content Creation

• POST /api/content/start
• Begins course content generation
• Returns: { "success": true, "session_id": "uuid" }

Get Content Status

• GET /api/content/status
• Checks content creation progress
• Returns: { "success": true, "progress": {} }

Usage Examples

Starting an Assessment

import requests

# Start assessment
response = requests.post('http://localhost:5000/api/assessment/start')
session_id = response.json()['session_id']

# Get first question
question = requests.get('http://localhost:5000/api/assessment/question').json()

# Submit answer
requests.post('http://localhost:5000/api/assessment/answer', 
             json={'answer': 'User response'})

# Get results
results = requests.get('http://localhost:5000/api/assessment/result').json()

Creating Course Content

import requests

# Start content creation
response = requests.post('http://localhost:5000/api/content/start', 
                        json={'session_id': 'your-session-id'})

# Check progress
status = requests.get('http://localhost:5000/api/content/status', 
                     params={'session_id': 'your-session-id'}).json()

Contributing

We welcome contributions to AItution! Here's how you can help:

1. Fork the repository
2. Create a new branch (git checkout -b feature/improvement)
3. Make your changes
4. Run tests (python -m pytest)
5. Commit your changes (git commit -am 'Add new feature')
6. Push to the branch (git push origin feature/improvement)
7. Create a Pull Request

Please ensure your PR:

• Includes tests for new features
• Updates documentation as needed
• Follows the existing code style
• Includes a clear description of changes

License

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

Support

• 📫 For bugs and features, open an issue on GitHub
• 💬 For questions, start a discussion
• 📝 Check out our Wiki for more documentation

Acknowledgments

• OpenAI for their powerful AI models
• The open-source community for various tools and libraries
• All contributors who help improve AItution