EdTech
Governing AI-driven learning recommendations
AI-personalised learning paths that had to be suitable, explainable, and not misleading.
Illustrative · anonymised and reconstructed from real work
The scenario
Where the decision had to hold under scrutiny
Illustrative governance work focuses on AI used to personalise learning pathways and generate recommendations for students. The main challenge is ensuring outputs are suitable, explainable, and not misleading in real educational settings; the focus is introducing control around how recommendations are reviewed and applied — helping keep learning decisions appropriate, consistent, and explainable.
What the scenario turns up
The issue
AI was personalising learning pathways and generating recommendations for students, where the outputs had to be suitable, explainable, and not misleading when applied in real educational settings.
What the work does about it
The response
Introduces control over how recommendations are reviewed and applied, keeping learning decisions appropriate, consistent, and explainable rather than accepted on trust.
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 Design, Build — 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.