StudyLumina

StudyLumina takes your uploaded course materials — lecture slides, notes, or textbook chapters — and maps them into a structured chapter index automatically. From there, you generate quizzes and flashcards against that content, and every attempt feeds a deterministic ERS: a 0–100 score computed in code from your real performance, not inferred by a language model. The score updates per chapter, so you can see that Dynamic Programming sits at 58% while Sorting Algorithms is at 92%, and the tool tells you which to study next given your exam date. The vendor describes the scoring approach as grounded in retrieval-practice research — specifically Roediger and Karpicke (2006), which found retrieval practice improves long-term retention significantly over passive re-reading.

The differentiating feature is the ERS itself. Quizlet drills you; NotebookLM answers questions; neither tells you whether you’ll pass. StudyLumina’s score is deterministic — built from your actual attempt history, not generated fresh each session — which means the number moves only when your knowledge does. The AI tutor answers questions with citations pointing back to your own documents, so you can verify the source rather than trust a hallucinated paraphrase.

The tool fits a specific profile well: a student with a fixed exam date, a defined set of course materials, and a need for objective readiness measurement rather than passive content generation. It covers 50+ courses and runs in both French and English. Where it breaks: the free tier limits you to three courses and ten AI tutor queries per day, which means a student managing a full course load across five subjects hits the ceiling fast. The chapter-level ERS breakdown and the smart study planner — the features that make the tool actionable — are paid-only. There is no API and no self-hosted option, so teams or institutions looking to integrate readiness scoring into an existing platform have no path forward without going through the vendor’s interface directly.