OpenJEE: Flexible JEE Main Practice with Open-Source AI

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

I built OpenJEE, a JEE Main mock-test app for students who want on-demand practice without depending on a single AI provider.

Students can choose a subject, difficulty, test length, and question source. They can take a timed test, save answers for review, and see their score and step-by-step solutions afterward. Tests can come from a bundled question bank or be generated with an AI model.

The app is designed for JEE aspirants who want flexible practice: use the question bank for quick tests, a cloud model for generated questions, or run a model locally with Ollama.

Demo

Try OpenJEE

Code

View the OpenJEE repository on GitHub

How I Built It

OpenJEE uses FastAPI for its backend and serves a browser-based frontend built with HTML, CSS, and JavaScript. Pydantic models validate test questions and submissions. The frontend provides the timed exam experience, question navigation, browser-based session recovery, scoring, and solution review.

For AI-generated tests, I used LangChain to integrate Groq-hosted models and locally running models through Ollama. The app also includes a question-bank option that doesn’t require an AI provider. KaTeX renders mathematical notation in questions and solutions.

Why Does Open Innovation Matter?

Open innovation gives students a choice about where and how their practice questions are generated. With Ollama, a student can run a model locally instead of sending prompts to a cloud inference provider. That can be useful when privacy, internet access, or control over model choice matters.

The model-provider setup also avoids tying the app to one inference service. Students can use local inference, choose a hosted provider, or practice from the bundled question bank. That flexibility makes it easier to experiment with models and keep the basic practice workflow available even without a paid AI API.