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Project Evaluation
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Project Documentation
Inspiration
We wanted to make learning more personalized by helping students understand concepts, identify knowledge gaps, and focus on what they need to improve.
What it does
StudySage combines AI-powered explanations, source-grounded learning, assessments, and Bayesian Knowledge Tracing to support personalized learning.
How we built it
We used React, Tailwind CSS, and Lucide icons for the frontend, FastAPI for the backend, a source-grounding engine for reliable explanations, and Bayesian Knowledge Tracing for learner modeling.
Challenges we ran into
Integrating AI-generated explanations, reliable source grounding, assessments, and learner knowledge tracking into one cohesive platform was our biggest challenge.
Accomplishments that we're proud of
We brought source-grounded AI and learner modeling together in an architecture designed to make learning more adaptive and personalized.
What we learned
We learned how AI, knowledge tracing, and assessment data can work together to create more personalized learning experiences.
What's next for StudySage
We plan to improve adaptive learning paths, progress analytics, targeted practice, and the reliability of AI-generated explanations.
Built With
- css
- fastapi
- javascript
- python
- tailwind
