Teachers and students often face significant challenges with preparing for exams:
- 🕒 Time-Consuming: Teachers spend valuable time manually preparing question papers, especially when tests are scheduled unexpectedly.
- 📑 Lack of Automation: Preparing custom question papers can be tedious, requiring careful selection of questions and topics.
- 🤔 Struggling with Practice: Students often have difficulty generating relevant practice questions from study materials (e.g., textbooks, notes).
- 🧠 Inefficient Study: Without comprehensive self-assessment tools, students may focus on less important content or miss critical topics.
My solution is a mobile app that combines Optical Character Recognition (OCR) and a Large Language Model (LLM) to automatically generate question papers from scanned images or PDFs of study materials.
- 📸 OCR for Text Extraction: The app uses Optical Character Recognition (OCR) to extract text from scanned images or PDFs of study materials (like books, notes, etc.).
- 🤖 Large Language Model (LLM): The extracted text is processed by an LLM to generate relevant and meaningful question papers based on the content.
- 📚 Comprehensive Exam Preparation: Automatically generated question papers allow students to practice based on actual study materials, ensuring a more thorough and focused review.
- ⚡ Saves Time for Teachers: Teachers can save time by automating question paper generation, enabling them to quickly prepare tests, quizzes, or practice papers.
- OCR Technology: Utilizes OCR libraries (e.g., Tesseract) to extract text from images and PDFs.
- Large Language Model (LLM): Powered by a language model (e.g., GPT) for text-based question generation and content understanding.
- Mobile App Development: Built for both iOS and Android using cross-platform tool Flutter .
- Backend: A robust backend powered by Firebase and OpenAI API Key to handle OCR processing, LLM interactions, and data storage.
- 📸 Upload Materials: Teachers or students upload scanned images or PDFs of study materials (like textbooks, notes, etc.) into the app.
- 🧠 Text Extraction with OCR: The app uses OCR technology to extract the text from the uploaded materials.
- 💬 Question Generation with LLM: The extracted text is then processed by an LLM to generate relevant questions, such as:
- Multiple choice questions (MCQs)
- Short answer questions
- Long-form essay questions
- 📑 Question Paper Output: The generated question paper is presented to the user, who can adjust settings like difficulty, question type, and more.
- 📚 Practice Mode: Students can use the generated question papers to practice, improving their exam preparation.
- ⏳ Time-Saving for Teachers: Automates the tedious task of question paper preparation, giving teachers more time for teaching.
- 📚 Efficient Study for Students: Students can generate relevant practice questions directly from their study materials, ensuring comprehensive preparation.
- 🎯 Personalized Question Papers: Teachers can customize the question paper generation based on their specific needs (e.g., difficulty, subject focus).
- 💡 Focused Exam Preparation: The app helps students focus on the most important and relevant topics for exams, improving their overall performance.
- OCR Libraries: Google ML-Kit for text extraction from images/PDFs.
- Large Language Models (LLMs): GPT-3, GPT-4, or similar models for generating contextually relevant questions based on extracted text.
- Mobile Framework: Flutter, React Native, or native Android/iOS development for building the app.
- Backend Services: Python Flask/Django or Node.js for handling text extraction, question generation, and user interactions.
- 📝 Question Customization: Allow users to create custom question templates for specific exam formats (e.g., essay-based, MCQs).
- 🌐 Multi-Language Support: Add support for multiple languages to allow global use of the app.
- 📚 Smart Question Suggestions: Integrate smart algorithms to suggest topics based on the user’s past study habits or performance.
- 👩🏫 Teacher Feedback: Enable teachers to provide feedback or modify the generated questions for better accuracy or difficulty adjustments.
We welcome contributions from the open-source community! If you have any suggestions, bug fixes, or features to add, please feel free to submit a pull request or open an issue.
This project is licensed under the MIT LICENSE License. See the LICENSE file for more details.
Made with ❤️ by Akhil