NeuraBlend: AI-Powered Learning Assistant

Overview

NeuraBlend is an AI-powered learning platform designed to create personalized study plans for students. The application leverages artificial intelligence to generate customized learning paths tailored to individual learning styles and goals.

Features

• AI-Generated Study Plans: Create personalized study plans based on grade level, subject, and learning goals
• Step-by-Step Learning: Break down complex topics into manageable steps with guided resources
• Interactive Quizzes: Test knowledge with AI-generated quizzes at each learning step
• Progress Tracking: Monitor learning progress and achievements in the dashboard
• Badge Collection: Earn and purchase badges to showcase achievements
• Gamified Learning: Earn XP and Kudos for answering quiz questions correctly

Tech Stack

Frontend

• React: JavaScript library for building the user interface
• React Router: For navigation and routing between different pages
• Tailwind CSS: Utility-first CSS framework for styling
• Framer Motion: For animations and transitions
• Tailwind Components: Custom components built with Tailwind classes

Backend

• FastAPI: High-performance Python web framework for building the API
• SQLAlchemy: SQL toolkit and ORM for database interactions
• SQLite: Lightweight database for storing user data, study plans, and badges
• OpenAI GPT: For generating personalized study plans, quizzes, and learning resources

Key Libraries

• React Markdown: For rendering markdown content from study plans
• Remark GFM: GitHub Flavored Markdown plugin for React Markdown
• Framer Motion: Animation library for React
• Papa Parse: CSV parsing library
• React Icons: Icon library for React

Project Structure

neura-blend/
├── frontend/                # React frontend application
│   ├── public/              # Public assets
│   │   └── assets/          # Images, logos, and other static files
│   └── src/
│       ├── components/      # Reusable UI components
│       ├── pages/           # Page components
│       └── ui/              # UI utility components
│
├── backend/                 # FastAPI backend application
│   ├── api.py              # API endpoints
│   ├── database.py         # Database connection and session management
│   ├── models.py           # SQLAlchemy models
│   ├── openai_utils.py     # OpenAI integration utilities
│   └── init_badges.py      # Script to initialize badge data

Application Workflow

1. User Registration/Login: Users create an account or log in with their username and email
2. Dashboard: Users can view their learning progress, XP, and Kudos
3. Create Study Plan: Users specify their grade level, subject, and learning goal to generate a personalized study plan
4. Study Plan Execution: Users work through the generated study plan step by step
5. Knowledge Testing: Each step includes a quiz to test understanding and earn XP/Kudos
6. Badge Collection: Users can spend Kudos to purchase achievement badges in the shop

User Experience

• Modern UI: Clean, responsive interface designed with Tailwind CSS
• Personalization: AI-generated content tailored to individual learning needs
• Gamification: Points, badges, and achievements to motivate continued learning
• Resource Integration: Each learning step includes relevant resources or AI-generated materials

Installation and Setup

Prerequisites

• Node.js and npm
• Python 3.8+
• OpenAI API key

Frontend Setup

1. Clone the repository
2. Navigate to the frontend directory
3. Install dependencies:

   npm install
   

4. Start the development server:

   npm run dev
   

Backend Setup

1. Navigate to the backend directory
2. Create 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. Set up environment variables:

   echo "OPENAI_API_KEY=your_openai_api_key" > .env
   

5. Initialize the database and badges:

   python init_badges.py
   

6. Start the FastAPI server:

   uvicorn main:app --reload
   

Future Enhancements

• Collaborative Learning: Features for group study and peer-to-peer learning
• Advanced Analytics: Detailed insights into learning patterns and progress
• Content Expansion: More subjects, grade levels, and specialized learning tracks
• Mobile App: Native mobile application for on-the-go learning
• AI Tutoring: Real-time AI tutoring sessions with personalized feedback
• More Game Mechanics: Maybe add a prize ticket machine where the student can use Kudos to purchase prize ticket to get rare badges that they can't buy in the shop.

Contributors

• Stan Chen - Full Stack Developer & Product Manager
• Sean Donovan - UI/UX Developer

Tech Stack

License

MIT License