Teach uncertainty with an engine students can open.
Probability, calibration, evidence discipline and reproducibility — taught not from slides but from a live, glass-box decision engine that runs free in every student’s browser and refuses to fake what it does not know.
Why teach with it
Most people leave education able to calculate a probability and unable to use one. LEXUN is a working decision engine whose every design choice is a lesson in applied uncertainty — and because it is a glass box, students can open it.
Bands, not points
The engine answers "60–70%", never "67.3%" — a live demonstration of what precision the evidence does and does not support.
Refusal as rigour
Thin evidence gets "insufficient evidence", not a guess. Students see a system that would rather be silent than wrong.
Reproducibility
Seeded simulation means every student, on every laptop, gets the identical result from identical inputs — the scientific method, enforced by code.
Open source of truth
The models are auditable JavaScript with stated weaknesses and version numbers. Nothing is "the algorithm says so".
Three classroom exercises that work today
1 · Reproduce a published result
Give the class the reproducibility ID from the worked example on Reproduce a result, or take a live one from the public forecast register — forecasts are frozen there with resolution dates already on official calendars, so a class can read a prediction before reality answers it and come back to score it. Every student reruns the seeded simulation and verifies they get the same band, then changes one input and explains why the band moved. Verification before trust — practised, not preached.
Use the reproduction guide → AVAILABLE LESSON ACTIVITY
2 · Facts versus assumptions
Students frame a real decision — a gap year, a first car — and must classify every input as fact, assumption or unknown before the engine will run. The sensitivity analysis then shows which assumption their whole answer secretly rests on.
Frame a decision → AVAILABLE LESSON ACTIVITY
3 · Forecast, freeze, score
Each student makes a forecast with a frozen resolution criterion and a due date. When the date arrives, outcomes are reported and Brier-scored — the class builds its own calibration record and discovers, with numbers, whether their 80% really meant 80%.
Teacher note: this fits a term exactly — freeze forecasts in September, score them in December. Fits A-level maths (probability, expectation) and economics (forecasting, uncertainty), and intro university statistics (scoring rules, calibration). The loop — predict, freeze, wait, score — is the method teaching itself.
See the scoring method → AVAILABLE LESSON ACTIVITY
The ninety-second calibration test
A classroom starter that teaches the central lesson of this whole page in one round: being right and knowing how right you are are different skills. Ten true-or-false statements; for each, pick an answer and how confident you are. The score is a Brier score — the same rule that scores LEXUN’s public forecasts. Everything runs in this page; nothing you answer is stored or sent anywhere.
Ten questions is a taster, not a measurement — a usable calibration record needs far more answers, which is why LEXUN refuses to display an accuracy percentage anywhere before ten resolved real outcomes exist in a domain. Teacher note: run it twice, once before and once after teaching what the confidence levels mean; the improvement is usually visible immediately.
The teacher one-pager
LEXUN in the classroom — one page
What it is: a free, glass-box decision engine at lexun.co.uk/platform. It turns a written decision into declared inputs, refuses to run until facts and assumptions are separated, returns a probability band with its drivers, and freezes forecasts so they can be scored against reality.
Setup: none. Browser tabs. No accounts, no installs, no student email addresses, no personal data leaving the machine.
Starter (10 min): the calibration test on lexun.co.uk/education — ten true/false questions with confidence levels, Brier-scored instantly.
Lesson 1 — Reproduce a result: students re-run a seeded simulation from lexun.co.uk/reproduce and verify the identical band, then move one input. Concept: reproducibility, sensitivity.
Lesson 2 — Facts vs assumptions: frame a real decision (gap year, first car); classify every input before the engine runs. Concept: evidence discipline.
Lesson 3 — Predict, freeze, wait, score: forecasts with frozen criteria in September, Brier scores in December. Concept: calibration. The public register at lexun.co.uk/forecast shows the same loop running in public, misses included.
Fits A-level maths and economics, and university intro statistics. Free is architectural, not promotional: the engine computes on the student’s device. Questions: hello@lexun.co.uk
Printing this page gives you the one-pager box on its own sheet — or link colleagues straight to lexun.co.uk/education#one-pager.
What it costs a classroom
Nothing, and that is architectural. The engine runs in the browser on any laptop or school machine — nothing to install, no accounts to provision, no student email addresses collected, no personal data leaving the device. A class of thirty needs thirty browser tabs.
That also answers the safeguarding question before it is asked: there is no server-side store of what a student typed, because the computation never leaves their machine. The privacy notice applies to students exactly as it does to everyone.
Honest limits
LEXUN is a decision engine, not courseware. There is no LMS integration, no gradebook, no lesson-plan library and no teacher dashboard today — and this page will say so until that changes. The three models are UK-flavoured (sterling, UK statistics as reference classes) and cover runway, career and purchase decisions — they are a laboratory for uncertainty, not a statistics curriculum. Each model publishes its own stated weaknesses, which make good seminar material in themselves.
Run a pilot
If you teach statistics, economics, business, computing or critical thinking and want to trial LEXUN with a class, tell us the shape of the course. We are a small team; pilots get a direct line to the people who wrote the models.