StudyPilot AI

Inspiration

Students often learn from multiple sources, including textbooks, PDF notes, PowerPoint presentations, and recorded lectures. Finding relevant information across these materials, understanding complex concepts, verifying answers, and identifying weak areas can be time-consuming.

What it does

The platform help students understand their study materials, ask questions, practice assessments, and identify topics that need further attention.

How we built it

StudyPilot AI combines document processing, multimedia transcription, AI-powered information retrieval, and learning analytics into a unified workflow.

Challenges we ran into

Building a multimodal learning system requires more than extracting text. It involves preserving source locations, processing lecture recordings, connecting retrieval results to reliable answers, and ensuring that assessment feedback reflects the original learning material.

Accomplishments that we're proud of

As a solo person i am proud that i made this level of architecture from scratch and after facing so much error and problem i accomplish this project

What we learned

I learned that how i can able to develop this level of projects from scratch without any other person needs or help.

What's next for StudyPilot AI

We aim to strengthen visual understanding of diagrams, improve citation accuracy across pages, slides, and video timestamps, enhance adaptive assessment, and expand personalized study planning based on prerequisite knowledge and learner mastery.

Built With

• adaptive-learning
• ai-education
• css
• fastapi
• faster-whisper
• ffmpeg
• gemini-api
• generative-ai
• git
• github
• html
• javascript
• machine-learning
• multimodal-ai
• natural-language-processing-(nlp)
• pdf-processing
• personalized-learning
• powerpoint-processing
• pytest
• python
• react
• rest-api
• retrieval-augmented-generation-(rag)
• speech-to-text
• vite