Rasegan Ai

RASEGAN AI is an AI exam coach that transforms study materials and past papers into personalized revision plans, practice questions, and insights to help university students prepare smarter.

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Inspiration

RASEGAN AI — An AI Exam Coach for University Students Worldwide

Inspiration

University students often have to prepare for exams using scattered lecture notes, lengthy study materials, and previous years' question papers. Figuring out what to study, identifying important topics, and testing their understanding can be overwhelming.

I wanted to build something that makes exam preparation more structured and accessible. That idea became RASEGAN AI: an AI-powered exam coach designed for university students across the world.

What It Does

RASEGAN AI uses AI to help students make better use of their study materials and past exam papers.

The project focuses on:

  • Analyzing previous exam papers to identify recurring topics and question patterns.
  • Extracting questions and useful information from uploaded study materials.
  • Generating AI-powered explanations and study assistance.
  • Helping students approach revision in a more organized and focused way.

The goal is not to promise exact exam predictions, but to help students understand their material and prepare more strategically.

How I Built It

I built RASEGAN AI using a React-based frontend and a Python FastAPI backend.

The frontend uses React, Vite, and modern UI libraries to provide an interactive student experience. The backend handles API requests, document processing, and AI-powered analysis.

I integrated NVIDIA Nemotron-3-Ultra-550b-a55b through Nebius Token Factory using an OpenAI-compatible API. For PDF processing, I used PyMuPDF to extract text from uploaded past papers and study documents.

The workflow connects document extraction, analysis, and AI-generated responses to make exam preparation more useful and interactive.

Challenges I Faced

Building this project as a student and working independently was challenging.

Some of the biggest challenges were:

  • Connecting the Nebius Token Factory API and configuring the AI model correctly.
  • Building communication between the React frontend and FastAPI backend.
  • Processing uploaded PDF documents and extracting meaningful text.
  • Debugging API errors, dependency issues, and integration problems.
  • Turning an initial idea into a working application under a tight hackathon deadline.

I approached these challenges by testing each component separately, fixing integration issues, and gradually connecting the frontend, backend, and AI model.

What I Learned

This project helped me gain practical experience with AI model integration, API development, document processing, frontend-backend communication, and debugging.

More importantly, I learned that building an AI application is not just about connecting a model to a user interface. It requires designing a useful workflow, handling real input, testing outputs, and solving problems when different technologies interact.

What's Next

I want to expand RASEGAN AI with better document understanding, more structured revision plans, personalized practice, and support for a wider range of university subjects and examination formats.

RASEGAN AI — Study smarter. Understand deeper. Prepare better.

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