Assessment, ethics & iteration
Grading AI-assisted work fairly, using the AI Audit as an assessment instrument, and handling the ethical questions that come up in class.
Content in development. The teacher training materials are being built with the same structure as the student curriculum. This page shows the planned outline — full content lands after the materials are validated with the first teacher cohort.
What to grade when AI did part of the making: process evidence, judgement calls, and the Creator's Note + AI Audit as first-class deliverables. Guiding proposal under consortium review: declared uncertainty is credited, not penalised — and production quality is scored on a separate axis from historical rigour, so a polished reel cannot disguise absent reasoning.
- Rubrics for the three-artefact deliverable
- Grading the audit, not just the artefact
- Crediting the honest UNKNOWN — why a story with no unknowns has invented something
- Spotting outsourced thinking
Materials
Slides, demos, and hand-outs for this block will appear here.
Dignity, stereotypes, and cultural sensitivity when students give voice to real historical people — especially people whose voices were silenced. The project position on synthetic voice and likeness (which subjects may be voiced or depicted synthetically at all) is being agreed across the consortium and will be covered here.
- The dignity check (Card 6), applied
- Bias in image models: what to expect and how to counter it — the Two Portraits moment
- Synthetic voice & likeness: the project position (consortium decision pending)
- When NOT to use AI
Materials
Slides, demos, and hand-outs for this block will appear here.
Closing the loop: what you'll improve next run, and how the Teacher Ambassador Network keeps the exchange going after the bootcamp.
- Your iteration plan
- Joining the ambassador network
- Sharing adaptations under CC BY-SA
Materials
Slides, demos, and hand-outs for this block will appear here.