Autodidact - AI-Powered Learning Assistant

Note: I have a bunch of kinks I need to iron out with this project (I'll do them over the next few days), but the main structure is mostly done

Here is a quick video overview of the project: https://www.loom.com/share/c321490734f84a30b3dafa00b39acffa

Autodidact is an AI-powered personalized learning assistant that creates custom study plans, provides interactive tutoring sessions, and tracks your learning progress.

Features

• 🔍 Deep Research: AI investigates your topic and creates comprehensive study plans
• 📊 Knowledge Graphs: Visual representation of concepts and their prerequisites
• 👨‍🏫 AI Tutoring: Personalized 30-minute learning sessions with an AI tutor
• 📈 Progress Tracking: Monitor your mastery of each concept over time
• 🔄 Session Recovery: Resume interrupted learning sessions

Installation

1. Clone the repository:

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

2. Create and activate a virtual environment:

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

3. Install dependencies:

pip install -r requirements.txt

4. Run the application:

streamlit run app.py

Setup

On first run, you'll need to provide your OpenAI API key. The app will guide you through this process and store your key securely at ~/.autodidact/.env.json.

Database Schema

The application uses SQLite with the following schema:

• project: Learning projects with topics and metadata
• node: Knowledge graph nodes (concepts to learn)
• edge: Relationships between concepts
• learning_objective: Specific objectives for each concept
• session: Learning sessions linking projects and nodes
• transcript: Conversation history for each session

Database Migration

If you're upgrading from an earlier version, run the migration script:

python backend/migrate_db.py

This will update your database schema to include the new session tracking features.

Usage

1. Start a New Project: Enter a topic you want to learn
2. Review the Plan: Examine the generated knowledge graph and report
3. Begin Learning: Start tutoring sessions for available topics
4. Track Progress: Monitor your mastery levels across concepts

Project Structure

autodidact/
├── app.py                 # Main Streamlit application
├── backend/
│   ├── db.py             # Database operations
│   ├── jobs.py           # AI job processing
│   ├── graph.py          # LangGraph tutor implementation
│   ├── deep_research.py  # Deep research module
│   └── migrate_db.py     # Database migration script
├── components/
│   └── graph_viz.py      # Graph visualization
└── utils/
    └── config.py         # Configuration management

Development

To contribute or modify Autodidact:

1. Follow the installation steps above
2. Make your changes
3. Test thoroughly with various topics
4. Submit a pull request

Requirements

• Python 3.8+
• OpenAI API key

License

MIT License - see LICENSE file for details