Grounding and guardrails
How the platform stops the AI from inventing your curriculum.
The principle
Generated content must trace back to something you supplied. Every artefact carries the source spans it was drawn from, and generation fails loudly rather than inventing content when it cannot ground an assertion.
Why this matters more here than elsewhere
A plausible-sounding hallucination in a chat assistant wastes a minute. The same hallucination in a syllabus becomes a lesson plan, a set of questions, a mark, and eventually a transcript entry.
Before paper-quiz grading was grounded in the extracted answer sheet, a model could produce responses that were never on the page. A blank answer sheet scored twelve out of twelve. The extraction was hallucinating, not the grader — and the mark looked entirely reasonable. Grounding was added so every mark names the criterion and the extracted text it was matched against.
Where grounding applies
Other guardrails
- Approval before exposure. Nothing generated reaches a student unapproved and unpublished.
- Teacher-only generation, enforced server-side rather than hidden in the interface.
- Strict output validation. A malformed response fails the job instead of being partially written.
- Override always wins. Any AI-assigned mark is overridable, and the override recomputes everything downstream.
- Tutor sequencing. The tutor will not explain gates the student has not reached.
What grounding does not promise
Traceable is not the same as correct. Grounding guarantees an artefact came from your document; it does not guarantee the pitch suits your cohort, the terminology matches your textbook, or the emphasis is right. That is what the review step is for.