Adaptive practice
Question sets that respond to the individual student — easing off when something is secure, slowing down and re-teaching when it isn't. The path through a topic is different for every learner, because it should be.
Bnm makes digital learning tools that adapt to the individual student — so progress is visible, teaching decisions are grounded in evidence, and no one quietly falls behind.
Illustration of the teacher view. Sample data.
Every teacher knows the problem. Some students needed the lesson to slow down three examples ago; others were ready to move on before it started. By the time a gap shows up in an assessment, it has usually been widening for weeks.
Bnm exists to close that gap while it is still small — and to give teachers the evidence to act without adding hours to their week.
We would rather do a small number of things well than ship a dashboard nobody opens twice.
Question sets that respond to the individual student — easing off when something is secure, slowing down and re-teaching when it isn't. The path through a topic is different for every learner, because it should be.
Not a wall of charts. A short, honest answer to the question teachers actually ask on a Monday morning: who is stuck, on what, and what should I do about it in tomorrow's lesson?
Progress students can see, framed around mastery rather than leaderboards. We are careful with streaks, badges and anything else that trades long-term confidence for short-term engagement numbers.
A student working through a topic on Bnm is never guessing what to do next. The platform reads what they have just demonstrated and picks accordingly — a harder variation, a worked example, or a step back to the sub-skill that is actually causing the trouble.
Solve for x: 3(x − 4) = 2x + 5
Most classroom software adds a task to a teacher's day. Ours is judged on whether it removes one. Marking is automatic, gaps are surfaced without being hunted for, and the class view is designed to be read in the two minutes before a lesson starts.
Four students share the same misconception in Rearranging formulae:
Dividing only one term when
undoing a coefficient.
Illustration. Sample data.
Rolling out school software is usually the hard part. We have tried to make ours the easy part.
A short diagnostic establishes where each student genuinely is — not where the scheme of work assumes they are. Typically one lesson.
Students work through practice that responds to them. Teachers see gaps as they emerge and set targeted follow-up in a couple of clicks.
Department and leadership views show movement over time, so you can tell what is working and where support should go next.
We work with children's data and children's confidence. Both deserve more caution than the sector usually applies.
Not to advertisers, not to data brokers, not to "analytics partners". It is not a revenue line we are choosing to forgo — it is one we will not build.
No mechanics designed to make a fourteen-year-old anxious about breaking a streak. Motivation should come from getting better at something.
If the platform says a student is struggling, a teacher can see exactly which responses led to that conclusion, and disagree with it.
We are building an instrument, not a substitute. Every design decision assumes a skilled adult is in the room and knows the student better than we do.
Clear next steps, honest feedback and visible progress. Nothing that makes falling behind feel like a public event.
Marking handled, misconceptions surfaced, and planning informed by what happened in the last lesson rather than guesswork.
Department-level movement, intervention tracking and a defensible evidence trail — without another data-entry burden on staff.
Our first release focuses on secondary mathematics, mapped to the English national curriculum. Additional subjects follow once we are confident the underlying model holds up in a second domain — we would rather be genuinely good at one subject than shallow across eight. Talk to us about your specific requirements and timelines.
Student data is held in the UK and processed under UK GDPR. Your school remains the data controller; we act as processor under a written agreement. We do not sell data, and we do not use identifiable student data to train models for other customers. Full detail is in our privacy notice.
No, and it is designed not to. Every flag the platform raises is traceable to the specific responses behind it, and every automated suggestion can be overridden. The system's job is to notice things quickly across thirty students at once — the teaching decision stays with the teacher.
Pricing is per student per year, with the rate depending on cohort size and the length of the agreement. We would rather quote you honestly against your actual numbers than publish a headline figure that turns out not to apply. Get in touch and we will send a written quote.
It runs in the browser, with no installation and no plugin. It is built to work on Chromebooks, iPads, older Windows desktops and student phones, and to degrade gracefully on a slow or intermittent connection — which, in a lot of schools, is the actual constraint.
We import your class lists, run a short session with the department, and stay close through the first half-term. The baseline diagnostic takes roughly one lesson per class. We have deliberately kept setup light because a rollout that needs three INSET days will not happen.
We are deliberately keeping our first cohort small so we can stay close to the people using it. If the problem described on this page is one you recognise, we would like to hear how it shows up in your setting.