Before the class
Choose a real, low-risk task and a synthetic or approved dataset. Write the desired output, boundaries and a short evaluation rubric.
TEAMS & TEACHING / A CAPABILITY YOU CAN OBSERVE
Teach for the work.
Assess the evidence.
A useful AI programme starts with what learners need to do, the information they may use and the mistakes they need to recognise.
A PRACTICAL TEACHING PATTERN
One cycle.
Many useful applications.
During the class
Demonstrate one baseline, then let learners vary a single input. Ask them to explain the change and preserve both useful and failed results.
After the class
Review artefacts against the rubric. Ask what remains uncertain, which safeguards matter and what should be tested next.
EXAMPLES OF LEARNING EVIDENCE
Make progress visible.
Creative team
A brief, a controlled image comparison, source and licence notes, and an edit log that explains why the final output was selected.
Operations team
A mapped process, a draft-only agent role, an approved tool contract and a small evaluation set with a documented human handover.
Technical team
A data-flow sketch, supported environment record, test workload and observations about quality, resources and failure recovery.
WORK WITH UDEMIA
What would useful
learning look like here?
Use the open classes now, or discuss adapting the structure to your team. Programme scope, delivery and availability are agreed through a conversation.
Discuss a learning programme ↗