Building Note2MCQ AI started from a simple frustration—how much time students waste rewriting notes, making summaries, and preparing MCQs instead of actually learning. I wanted to create something that could instantly turn raw text or chapter notes into meaningful study material, so I built this project using a Python–Flask backend, a clean frontend, SQL for storage, and AI models like OpenAI and Gemini for generating summaries, MCQs, and questions. Along the way, I learned a lot about prompt engineering, text preprocessing, database design, and handling API rate limits. Some parts were challenging—especially maintaining output quality, structuring data cleanly, and cleaning PDF text—but each challenge pushed me to refine the system. In the end, Note2MCQ AI became a simple, fast, student-friendly tool that helps convert any study content into useful exam-ready material within seconds, making learning easier and more efficient.
Built With
- css
- dotenv
- gemini
- html
- html5
- javascript
- openai
- pypdf2
- python
- sql
- sql/sqlite
- sqlalchemy
Log in or sign up for Devpost to join the conversation.