Education
Governing AI in teaching, feedback, and marking
AI-generated lessons, feedback, and marking that could not reach students unchecked.
Illustrative · anonymised and reconstructed from real work
The scenario
Where the decision had to hold under scrutiny
Illustrative governance work applies the same discipline behind the fraud-rule work to AI used for lesson preparation, student feedback, and marking. Where AI accelerates the work but outputs are not consistent enough to accept unchecked, the focus is reviewing and governing AI-generated material before it reaches students — supporting consistency and reducing the risk of poor or unfair educational outcomes.
What the scenario turns up
The issue
AI was accelerating lesson preparation, student feedback, and marking, but the outputs were inconsistent and could not be accepted and sent to students without review — an unfair or wrong result would land directly on a learner.
What the work does about it
The response
Applies the same review discipline used for fraud rules: review and validation of AI-generated material before it is used, improving consistency and reducing the risk of poor or unfair educational outcomes.
How the work is done
The same five steps, applied to this decision
Every piece of work runs the same path. For this one, the weight falls on Build, Deploy & test, Assure & hand over — the steps where a decision of this kind is most often challenged.
Discovery
Map where the decision is made, who could challenge it, and the regime that binds it — before any logic is written.
Design
Set the logic out in the open so it can be read and questioned: the same inputs always give the same decision, and nothing is a black box.
Build
Build it with the checks in: coverage and oversight are confirmed before anything ships, and nothing unassessed is waved through as safe.
Deploy & test
Run it against real cases behind a human checkpoint, and keep a dated record of exactly what was decided and on what basis — one that cannot be edited afterwards.
Assure & hand over
Hand over the decision log and a plain-language write-up, so your team owns the record the moment someone says prove it.
What carries across
No automation covers this case yet
58 automations are running today, each built for a rule it names. None of them covers this case, and that is all that is claimed here. What carries across is the method: every one of them refuses to write a sentence its evidence will not support, which is the discipline this kind of work once ran by hand. Any of them can be shown to you running.
Related cases
The same standard, holding elsewhere
Discuss your situation
A decision like this can be made correctly, and shown to be correct, on the record
A short discussion is usually enough to locate where the risk sits — in the logic, the controls, the evidence, or a combination — and where it is made to hold.