QuickEval - AI-Powered Answer Sheet Evaluation System

QuickEval is an intelligent system that automates the evaluation of student answer sheets using AI. It helps educators save time and maintain consistency in grading.

Features

• AI-powered evaluation of student answer sheets
• Configurable question papers and answer keys
• Detailed scoring with confidence levels and remarks
• Multi-educator support with unique IDs
• Comprehensive evaluation reports

Tech Stack

• Frontend: React.js with Tailwind CSS
• Backend: Node.js, Express.js
• Database: MongoDB
• AI: OpenAI API
• Other Tools: Zod (validation), Vite (build tool)

Project Structure

quickeval/
├── client/               # Frontend React application
│   ├── src/             # React source files
│   ├── tailwind.config.js
│   └── vite.config.js
└── server/              # Backend Node.js application
    ├── models/          # MongoDB schemas
    ├── routes/          # API routes
    ├── utils/           # Utility functions
    └── server.js        # Main server file

Getting Started

1. Clone the repository
2. Install dependencies:

cd client && npm install
cd ../server && npm install

3. Set up environment variables: Create a .env file in the server directory with:

DB_URL=your_mongodb_connection_string
PORT=3000
OPENAI_API_KEY=your_openai_api_key

4. Start the development servers:

# Start backend
cd server && npm start

# Start frontend
cd client && npm run dev

API Endpoints

• POST /evaluators: Create new evaluator with question paper and answer key
• GET /evaluators/:educatorId: Get all evaluators for a specific educator

Key Features

1. Smart Evaluation

   • Analyzes student answers against answer keys
   • Provides detailed scoring and feedback
   • Includes confidence levels for each evaluation

2. Structured Response

   • Student information extraction
   • Question-wise breakdown
   • Detailed remarks and justification

3. Educator Dashboard

   • Manage multiple evaluation sets
   • Track evaluation history
   • Access detailed reports

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License.