StudyBuddy AI：我為朋友 Linuka 開發的 AI 學習夥伴
# StudyBuddy AI: I Built an AI Study Buddy for My Friend Linuka 📚
作者為朋友 Linuka 開發 StudyBuddy AI，讓用戶上載 PDF、TXT 或 Markdown 檔案，或貼上筆記生成測驗。用戶可設定題數、難度和選填主題，完成選擇題後取得回饋、解釋及分數；程式以 React、Vite、FastAPI 和 Ollama 建構，預設使用 Qwen2.5 3B。本機推理可讓學習資料留在用戶電腦，但掃描版 PDF 因尚未實作 OCR 而不支援。
This is my submission for the Hacktoberfest Weekend Challenge: Build for a Friend.
What I Built
I built StudyBuddy AI for my friend Linuka, who could use a simpler way to turn lecture notes into practice questions.
The idea is pretty straightforward: instead of reading through notes again and again, Linuka can upload a PDF, TXT, or Markdown file or simply paste notes into the app and generate a quiz from the content.
Users can choose the number of questions, difficulty level, and an optional topic. StudyBuddy AI then creates multiple-choice questions, provides feedback and explanations, and calculates a score at the end.
I wanted to make studying a little more interactive and help turn passive reading into active practice. 📚
Code
GitHub Repository: [https://github.com/antonluckshman/StudyBuddy-AI]
The project uses React for the frontend, FastAPI for the backend, and Ollama to run a local AI model.
How I Built It
I used React and Vite to build the user interface, Python with FastAPI for the backend, and Ollama to generate quizzes using the Qwen2.5 3B model by default.
The workflow is simple:
- Upload study materials or paste notes.
- Choose the quiz difficulty and number of questions.
- Let the AI generate multiple-choice questions from the material.
- Answer the questions and get feedback, explanations, and a final score.
I also added basic input validation and error handling to make the experience easier to use.
One current limitation is that scanned PDFs aren't supported because OCR hasn't been implemented yet.
Why Open Innovation Matters
I wanted to experiment with AI without depending on a paid, hosted AI API.
Using open-source tools gives me the flexibility to experiment with different models and run inference locally through Ollama. When configured for local inference, the study material can stay on the user's computer instead of being sent to a hosted AI service.
It also gives me more control over how the application works and makes it easier to learn by building something practical.
Of course, the project is still a work in progress, and there's plenty of room to improve the quiz quality and user experience.
Built with open-source tools, for my friend Linuka and for more focused, interactive studying. 🌱
來源：DEV Community · dev.to