Rectify Learn
Summary
An adaptive learning platform that turns a student's own lecture notes into graded quizzes and flashcards. I built the backend and the entire AI pipeline solo; a collaborator owns the React frontend.
Problem
Students collect PDFs and lecture slides they never turn into anything they can be tested on. Writing good questions from your own notes is slow, so most people re-read passively instead — which feels like studying and mostly isn't.
Constraints
- C01Solo on backend and AI — no ML or DevOps help
- C02Source documents are wildly inconsistent (slides, scans, 80-page PDFs) and the output has to stay consistent anyway
- C03Cost per user has to work for a bootstrapped product, which caps how freely I can call the model
Architecture
PDF and DOCX uploads, parsed and chunked before anything reaches the model.
Azure OpenAI produces difficulty-graded quizzes and flashcards with hints, explanations and keyword tags.
Django REST serves the frontend and owns session state; Supabase handles auth and cross-device sync.
Cards resurface based on how confident the student says they are, not a fixed schedule.
Decisions
Results
Retrospective
The document parser is what I'd rebuild first. Real lecture notes are messier than any test set, and it fails softly — you get a mediocre question, not an error — so bad output reaches students before it reaches me. I'd instrument generation quality before adding another feature.