AI Flashcard Learning Platform

A web-based educational tool that leverages AI to streamline the creation and study of flashcards, designed to help high school students prepare for tests using proven spaced repetition methodology.

Table of Contents

• Project Description
• Tech Stack
• Getting Started Locally
• Available Scripts
• Project Scope
• Project Status
• License

Project Description

The AI Flashcard Learning Platform is designed to solve the problem of time-consuming manual flashcard creation that discourages students from using effective study methods like spaced repetition.

Key Features

• AI-Powered Flashcard Generation: Generate high-quality flashcards from user-provided text (150-15,000 characters)
• Google Authentication: Secure sign-in using Google OAuth
• Deck Management: Create and organize flashcards into named decks
• Flashcard Review System: Review, edit, accept, or reject AI-generated cards before adding them to your deck
• Manual Card Creation: Create flashcards manually when needed
• Spaced Repetition Study Sessions: Study using the proven Leitner system for optimal learning
• Progress Tracking: View performance summaries after study sessions

Target Users

Primarily high school students (ages 13+) seeking an efficient way to prepare for tests by combining AI-assisted content generation with spaced repetition methodology.

Tech Stack

Frontend

• Astro 5 - Fast, efficient website framework with minimal JavaScript
• React 19 - Interactive components where needed
• TypeScript 5 - Static typing and enhanced IDE support
• Tailwind CSS 4 - Utility-first CSS framework for styling
• Shadcn/ui - Accessible React component library for UI elements

Backend

• Supabase - Comprehensive backend-as-a-service solution
  • PostgreSQL database
  • Built-in user authentication
  • SDK for seamless integration
  • Open source with self-hosting options

AI Integration

• Openrouter.ai - AI service providing access to multiple models
  • Support for OpenAI, Anthropic, Google, and other providers
  • Financial limits and cost optimization
  • High efficiency and flexibility

CI/CD and Hosting

• GitHub Actions - Automated CI/CD pipelines
• DigitalOcean - Application hosting via Docker containers

Getting Started Locally

Prerequisites

• Node.js version 22.14.0 (specified in .nvmrc)
• npm or yarn package manager
• Git

Installation

1. Clone the repository

   git clone <repository-url>
   cd flashcards

2. Install Node.js version

   # If using nvm
   nvm use
   # Or install Node.js 22.14.0 manually

3. Install dependencies

   npm install

4. Set up environment variables

   # Copy environment template (create if needed)
   cp .env.example .env.local
   # Configure your Supabase and Openrouter.ai credentials

5. Start the development server

   npm run dev

The application will be available at http://localhost:4321 (default Astro port).

Available Scripts

Script	Description
npm run dev	Start the development server
npm run build	Build the application for production
npm run preview	Preview the production build locally
npm run astro	Run Astro CLI commands
npm run lint	Run ESLint to check for code issues
npm run lint:fix	Run ESLint and automatically fix issues
npm run format	Format code using Prettier

Project Scope

Included Features (MVP)

• Authentication: Google Sign-In integration
• Deck Management: Create and view named flashcard decks
• AI Generation: Generate flashcards from text input with monthly limits per user
• Card Review: Edit, accept, or reject AI-generated cards before saving
• Manual Creation: Create flashcards manually with question/answer format
• Study Sessions: Spaced repetition using the Leitner system
• Progress Tracking: Session summaries showing performance metrics

Current Limitations

The following features are not included in the MVP scope:

• Custom repetition algorithms (Leitner system only)
• File imports (text input only)
• Deck sharing between users
• Native mobile applications (web-responsive only)
• Administrative UI panel
• Sub-decks, deck renaming, or deck deletion
• Bulk "Accept All" / "Reject All" actions for generated cards

Success Metrics

• AI Flashcard Acceptance Rate: Target 75% (accepted cards / total generated cards)
• AI Usage Rate: Target 75% (AI-generated cards / total created cards)

Project Status

🚧 Development Phase - MVP (Minimum Viable Product)

This project is currently in active development, focusing on core functionality for the MVP release. The platform is designed to validate the AI-assisted flashcard generation concept and spaced repetition learning methodology.

Development Priorities

1. Core AI flashcard generation functionality
2. User authentication and deck management
3. Spaced repetition study system implementation
4. Performance optimization and user experience refinements

License

License information not specified. Please contact the project maintainers for licensing details.

---

Contributing: This project follows clean code practices with ESLint and Prettier configuration. Please run npm run lint:fix and npm run format before submitting contributions.

Support: For questions or issues, please refer to the project documentation or create an issue in the repository.