AIDetect Exam Platform

An AI-powered exam platform that detects AI-assisted submissions across text, image, audio, and video using Google Gemini, ensuring secure, fair, and authentic assessments

Comment

Inspiration

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for AIDetect Exam Platform

Gemini AI Detection Testing Platform

A full-stack web application designed to administer and evaluate candidate tests using AI detection technology. The platform allows administrators to invite candidates, manage timed tests, and analyze submissions (text, images, audio, video) to determine if they are human-generated or AI-assisted.

Features

Admin Functionality

  • Secure admin login with username/password authentication
  • Invite candidates via email with unique PIN codes
  • Set custom test time windows (start and end times)
  • View all candidate results and submissions
  • Generate final verdicts using AI analysis of all submissions

Candidate Experience

  • PIN-based login system
  • Time-windowed test sessions with strict enforcement
  • Multi-modal submission support:
    • Text answers with AI/human detection
    • Image uploads with forensic analysis
    • Audio recordings with voice analysis
    • Video submissions with content verification
  • Real-time submission status tracking
  • Automatic test completion detection

AI Analysis

  • Powered by Google Gemini 2.5 Flash model
  • Specialized prompts for each media type:
    • Text: Human vs AI content detection
    • Images: Real vs AI-generated image analysis
    • Audio: Human vs synthetic voice detection
    • Video: Authentic vs deepfake content verification
  • Comprehensive final verdict generation combining all analyses

Tech Stack

Frontend

  • React 19 - Modern JavaScript library for building user interfaces
  • Vite - Fast build tool and development server
  • ESLint - Code linting and formatting
  • CSS - Styling with modern CSS features

Backend

  • Flask - Lightweight Python web framework
  • SQLite - Embedded database for data persistence
  • Google Generative AI (Gemini) - AI analysis and detection
  • Pillow (PIL) - Image processing
  • Flask-CORS - Cross-origin resource sharing support

Additional Libraries

  • python-dotenv - Environment variable management
  • base64 - Data encoding for media processing
  • datetime - Time window management

Installation

Prerequisites

  • Python 3.8+
  • Node.js 16+
  • Google Gemini API key

Backend Setup

  1. Navigate to the server directory:

    cd server

  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: Create a .env file in the server directory with:

    GEMINI_API_KEY=your_api_key_here

  5. Initialize the database:

    python init_db.py

Frontend Setup

  1. Navigate to the client directory:

    cd client

  2. Install dependencies:

    npm install

Usage

Running the Application

  1. Start the backend server:

    cd server
python app.py

    The server will run on http://localhost:5000

  2. Start the frontend development server:

    cd client
npm run dev

    The client will run on http://localhost:5173 (default Vite port)

Admin Access

  • Navigate to the admin login page
  • Use credentials: admin / admin123
  • From the dashboard, invite candidates and view results

Candidate Flow

  1. Receive email invitation with PIN
  2. Login using PIN during the scheduled time window
  3. Complete test submissions (text, image, audio, video)
  4. Submit final test completion

Database Schema

Users Table

CREATE TABLE users (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    email TEXT UNIQUE NOT NULL,
    pin TEXT NOT NULL,
    test_start DATETIME,
    test_end DATETIME,
    status TEXT DEFAULT 'Invited',
    text_submitted BOOLEAN DEFAULT 0,
    image_submitted BOOLEAN DEFAULT 0,
    audio_submitted BOOLEAN DEFAULT 0,
    video_submitted BOOLEAN DEFAULT 0,
    final_submitted BOOLEAN DEFAULT 0,
    created_at DATETIME DEFAULT CURRENT_TIMESTAMP
);

Tests Table

CREATE TABLE tests (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    user_id INTEGER,
    question TEXT,
    answer TEXT,
    ai_result TEXT,
    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
    FOREIGN KEY(user_id) REFERENCES users(id)
);

API Endpoints

Authentication

  • POST /admin/login - Admin authentication
  • POST /validate-pin - Candidate PIN validation

Test Management

  • POST /generate-invite - Create candidate invitation
  • POST /submit-text-answer - Submit text response
  • POST /submit-image-answer - Submit image upload
  • POST /submit-audio-answer - Submit audio file
  • POST /submit-video-answer - Submit video file
  • POST /mark-submitted - Mark test as completed
  • GET /submission-status/<user_id> - Check submission status

Admin Dashboard

  • GET /admin/all-results - Retrieve all candidate results
  • GET /admin/final-verdict/<user_id> - Generate final AI verdict

Frontend Routes

  • GET /login-page - Candidate login page
  • GET /test-page - Test interface
  • GET /admin-page - Admin login
  • GET /admin-dashboard - Admin dashboard

Security Features

  • PIN-based authentication for candidates
  • Time window enforcement to prevent early/late access
  • Submission freezing after initial upload
  • CORS protection
  • Secure file upload handling

Development

Running Tests

# Backend linting (if configured)
# Frontend linting
cd client
npm run lint

Building for Production

# Frontend build
cd client
npm run build
npm run preview

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Test thoroughly
  5. Submit a pull request

License

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

Support

For support or questions, please open an issue in the repository.

Share this project:

Updates