Most NEET apps say “AI”. Here are the algorithms.
Three named, published models decide what you revise, how well we think you know it, and what you see next. You can look every one of them up. That is the point.
1. Revision scheduling: FSRS
Lumi schedules revision with the Free Spaced Repetition Scheduler at a desired retention of 0.9, the same open algorithm modern Anki uses. Every card carries three numbers: a difficulty, a stability measured in days, and a retrievability that decays between reviews. A review is scheduled at the point where recalling the card is still possible but no longer easy, because that is where a single recall does the most work.
We run the reference implementation rather than an interval heuristic of our own. That is a deliberate constraint: it means our scheduling can be compared against published results instead of being taken on trust.
Read the FSRS whitepaper2. Ability estimation: 2PL Item Response Theory
A percentage score tells you how you did on the questions you happened to be asked. It does not tell you how good you are. Lumi estimates ability with a two-parameter logistic Item Response Theory model, the family of methods serious testing bodies use. Each question carries a discrimination and a difficulty, and your ability is fitted by maximum likelihood across your answers.
The consequence is worth stating plainly: two students with the same raw score can come out with different ability estimates. Getting a hard, sharply discriminating question right is stronger evidence than getting an easy one right, and the model treats it that way.
Read the IRT whitepaper3. What you see next: simpleKT knowledge tracing
Knowledge tracing predicts whether you would answer a given concept correctly right now, from the sequence of everything you have answered before. Lumi uses simpleKT, a transformer over concept and response embeddings, so the order and recency of your mistakes carry information rather than being averaged away.
Inside an adaptive exam the selection rule is different and simpler: Lumi picks the question with the highest Fisher information at your current ability estimate. In plain terms, the question whose answer would tell us the most.
All three run over the same concept map
Physics, Chemistry and Biology are broken into roughly 2,700 concept atoms. A chapter is too coarse to act on: “revise Thermodynamics” is not a plan. An atom is small enough that a wrong answer points at something specific, which is what makes per-question diagnosis possible at all. FSRS schedules atoms, Item Response Theory scores you on them, and knowledge tracing predicts them.
What we are not claiming
We are not the first AI app for NEET. Apps marketing AI for this exam predate us by years, and we would rather say so here than have it pointed out. We are not claiming these algorithms guarantee a rank, because nothing does, and any product that tells you otherwise is selling something.
What we do claim is narrow and checkable: these are the algorithms, this is what each one does, and we have published them so the claim can be argued with. As far as we can tell we are the only NEET app that names them. If that stops being true, we would rather someone told us than that we kept saying it.
Common questions
Which spaced repetition algorithm does Lumi use?
Lumi uses FSRS, the Free Spaced Repetition Scheduler, at a desired retention of 0.9. It is the same open scheduling algorithm modern Anki uses. Lumi runs the reference implementation rather than a home-grown interval heuristic, so each card carries a difficulty, a stability in days, and a retrievability that decays between reviews.
How does Lumi estimate a student's ability?
With a two-parameter logistic Item Response Theory model. Every question carries a discrimination and a difficulty parameter, and a student's ability is estimated by maximum likelihood across their responses. This is why two students with the same raw score can receive different ability estimates: getting a hard, highly discriminating question right is worth more evidence than getting an easy one right.
How does Lumi decide which question to show next?
Two mechanisms. Within an adaptive exam, Lumi picks the question with the highest Fisher information at the student's current ability estimate, which is the question whose answer will most sharply reduce uncertainty. Across a study plan, a simpleKT knowledge-tracing model, a transformer over the student's concept and response history, predicts the probability of answering each concept correctly right now.
Is 'AI' in Lumi just a large language model?
No. Language models are used for generating and explaining questions. The scheduling, ability estimation and question selection are done by FSRS, Item Response Theory and knowledge tracing respectively, which are specialised statistical models with published literature behind them. They are the parts that decide what a student sees.
Why does naming the algorithm matter?
Because 'AI-powered' is unfalsifiable and every competitor says it. A named algorithm can be looked up, argued with, and checked against what the product actually does. It is a claim that carries a cost to make, which is what makes it worth anything.
See what the models say about your prep
A diagnostic set is enough to produce a first ability estimate per subject and a revision schedule built from it.
Start with Lumi