The UofAi method
Take real work, collaborate with AI through iteration and validation, and prove the result with portfolio evidence.
A written guide to the method. A recorded learner walkthrough is not available yet.
Choose a task from your own work and describe what a useful result must do.
Gather the sources and constraints. Mark anything you still need to check.
Give AI a clear instruction, review its draft, and steer the next attempt.
Check the claims against your sources and improve the output against your criteria.
Save the artifact, its sources, and a note on the decisions you made. This is portfolio work you can show; it is not a verified credential.
The UofAi method
Choose one campaign thesis for Q2, grounded in the work our customers need to do.
Existing customers considering an upgrade, with a clear decision to make.
Direct the draft toward the actual audience.
Frame a task clearly enough that an AI hand-off can succeed.
Watch for: Under-specified prompts that produce generic output.
Read output critically and give targeted corrections.
Watch for: Accepting the first answer as finished work.
Co-develop across turns and steer the model toward intent.
Watch for: One-shot prompting instead of collaboration.
Stress-test claims, verify sources, and catch confident errors.
Watch for: Over-trust, hallucination risk, and weak provenance.
Use AI to extend your own expertise instead of replacing it.
Watch for: Dependence, deskilling, and shallow fluency.
Learners decompose their real role into tasks, then identify where AI creates deskilling risk versus upskilling opportunity — a clear read on what to stop competing on, what to augment first, and where human judgment matters most.
Lessons and labs are sequenced through five collaboration modes: directive, feedback loop, task iteration, validation, and learning. The goal is to move learners beyond prompt fluency into high-success augmentation behavior.
Every rep follows the same loop: a real task, a focused attempt at the target skill, Spotter feedback on the collaboration process, one targeted correction, and an immediate re-rep at higher difficulty.
Progress is measured with evidence: augmentation share, task value attempted, success rate, and critical-validation rate. Learners see their Augmentation Curve bend upward through demonstrated work, not vanity completion.
Build portfolio artifacts today. Human-reviewed credentials are planned under the draft Verification Standard and are not currently included.
The method teaches durable judgment: task decomposition, model selection, validation discipline, and recalibration when frontier models change. Recipes decay; augmentation capability transfers.