First-Year Exam Intelligence Platform

Description

A web application designed for first-year engineering students to prepare more effectively for exams using past exam paper intelligence.
Faculty upload exam papers, the system extracts and analyzes questions, and students receive optimized study plans and high-yield survival guides.

Live Demo

Click here to view the contents

Demo Login (For Judges)

Use the following test faculty credentials to access the faculty upload flow: Email: test@email.com
Password: test123

Note: These credentials are for hackathon evaluation only.

Key Features

• Faculty-authenticated exam paper upload
• Automatic question extraction using Google Gemini Vision
• Support for Internal and End Semester exams
• Intelligent question clustering and frequency analysis
• Student study optimizer and exam survival mode
• Transparent data coverage and confidence indicators

Tech Stack

• Frontend: Next.js / React
• Backend: Supabase (PostgreSQL, Auth, RLS)
• AI: Google Gemini (Vision + embeddings)

Prerequisites

• Node.js (v18 or higher)
• npm

Running the Project Locally

1. Configure Environment Variables: Copy the example environment file and fill in your keys:

   cp .env.example .env.local

   Open .env.local and add your:

   • NEXT_PUBLIC_SUPABASE_URL
   • NEXT_PUBLIC_SUPABASE_ANON_KEY
   • NEXT_PUBLIC_GEMINI_API_KEY

2. Install dependencies:

   npm install

3. Start the development server:

   npm run dev

4. Access the application:
   The application will run at http://localhost:3000

Usage Flow

Faculty

1. Navigate to /faculty/login
2. Log in using a verified faculty email(email: test@email.com , password: test123)
3. Upload exam papers at /faculty/upload
4. View extraction results (subject, exam type, questions, marks)

Student

1. Navigate to /student
2. Select subject, exam type, and exam date
3. View the generated study optimizer
4. Use Exam Survival Mode for high-yield preparation

Security & Access Control

• Faculty upload routes are protected using Supabase Authentication
• Only authenticated faculty users can upload exam papers
• Students have read-only access to exam data
• Row Level Security (RLS) is enabled on all database tables

Notes

• This project is built as a hackathon MVP
• The system optimizes preparation based on historical exam patterns
• It does not guarantee exam results, but reduces preparation uncertainty