-
-
interface
Inspiration
Students use textbooks, PDFs, and lecture notes to study. Finding answers and understanding difficult topics can take a lot of time. I wanted to build an AI tutor that makes learning easier and more personalized.
What it does
StudyMate AI allows students to upload study materials and ask questions about them. It uses AI to provide answers based on the uploaded documents, along with source references. In the future, it will also provide quizzes, track learning progress, and suggest topics that need more practice.
How we built it
I am building the project using Python and Streamlit. I use PyMuPDF to extract text from PDFs and a pretrained embedding model to find relevant information. A RAG pipeline retrieves the most useful content and sends it to an AI language model to generate answers.
Challenges we ran into
The main challenges are finding the correct information, providing accurate answers, and showing the right sources. I am working on making the system reliable and easy to use.
Accomplishments that we're proud of
What we learned
I learned how RAG works and how document processing, AI models, and semantic search can work together to create a useful application.
What's next for master debug
I plan to add adaptive quizzes, track student performance, and provide personalized study recommendations based on each student's strengths and weaknesses.
Built With
- css
- gemini
- llama
- ollama
- python
- rag
- react/vite
- streamlit
- typescript
