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📚 GrAIdscope — Gradescope Reimagined: An AI-Powered Grading System

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🚀 Project Overview

GrAIdscope is a revolutionary AI-powered grading platform designed to transform the way educators assess student work.

In today’s fast-paced educational environment:

  • Teachers are overwhelmed by grading large volumes of exams.
  • Students often wait weeks for meaningful feedback.

GrAIdscope automates free-response exam evaluation with speed and precision:

  1. Teachers upload their answer key PDF.
  2. Students submit completed exams.
  3. AI compares submissions against the key and produces comprehensive grades and detailed, actionable feedback—all within minutes.

💡 Benefits:

  • Consistency: Eliminates human error and subjective bias.
  • Efficiency: Reduces grading time from weeks to minutes.
  • Learning: Students receive immediate, transparent feedback.

For investors, GrAIdscope represents a cutting-edge opportunity at the intersection of AI, EdTech, and workflow automation.


💡 Inspiration

We identified major issues with OSU’s current grading platform, Gradescope:

  • Slow: Manual grading in big classes takes 2+ weeks.
  • ⚠️ Unreliable: Reports of lost student tests, missing grades, and software glitches.

We researched other solutions, compared their features, and designed a system that combines AI efficiency with reliability and accessibility.


🧩 Features

Feature Description
Teacher Upload Teachers upload the exam answer key PDF.
Student Submission Students upload completed exam PDFs.
AI Grading Compares student answers with teacher key and produces:
• Detailed scoring breakdowns (Completeness, Correctness, Simplification/Presentation)
• Overall exam score
• Feedback comments per question
Secure & Fast Uses API keys and backend processing to handle multiple submissions efficiently.
User-Friendly Frontend Intuitive interface for uploading files and viewing results.

🛠 Design Process

Click to expand the design process

Gap Identification

  • Existing graders fail at handling handwritten text.
  • Many AI grading tools have a steep learning curve for teachers.

Experimental Exploration

  • Tested multiple OCR engines, AI scoring logic, and dataset preprocessing.
  • Brainstormed intuitive UI layout for students and teachers.

Implementation Actions

  • Integrated OpenAI GPT-5 API for semantic grading.
  • Used Amazon S3 for scalable file storage.
  • React + Vite frontend for responsiveness.
  • Google OAuth for authentication.
  • Flask + PostgreSQL backend with NGINX deployment.

Outcome: Improved usability, data flow, OCR precision, and AI grading accuracy.


📊 Metrics

  • Grading Speed: Reduced midterm grading from 2 weeks to minutes.
  • 🎯 Accuracy: AI scores closely match human graders with detailed explanations.
  • 📈 Scalability: Handles hundreds of submissions concurrently.

Screenshot


🛠 Technology Stack

Backend

  • Web Server: NGINX
  • Cloud Storage: Amazon S3
  • Web Framework: Flask
  • Database: PostgreSQL
  • AI/ML Integration: OpenAI GPT-5 API

Frontend

  • Frameworks/Libraries: React, Vite
  • Languages: HTML, CSS, JavaScript

🏁 Getting Started

1. Clone the repository

git clone https://github.com/25kgozon/HackOHIO25.git
cd HackOHIO25

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