A working checklist for HR and L&D leaders building AI training inside their organizations. Most AI training is lecture-heavy, vocabulary-focused, and forgotten within a week. This is how to build the kind that changes what people actually do on Monday.
"If a participant can't tell you what they're going to do differently this week, your training was information transfer — not training."
These are the standards used to audit every workshop on this site. If you're designing your own AI training, hold it to the same bar.
Pair & share, hands-on tool work, drafting activities, discussions. Lecture blocks longer than 20 minutes get flagged for revision.
If you teach prompt structure, attendees write a prompt. If you teach AI policy, attendees draft a policy section. No concept is lecture-only.
"Understand," "know," "be aware of," and "appreciate" are not measurable. Use Bloom's taxonomy verbs — apply, analyze, evaluate, create — that a third party could watch and verify.
No yes/no questions. No rhetorical questions. No "is AI policy important?" Use "what's one scenario you could see happening at your company without an AI policy?"
Under 30 minutes. Specific, useful, and connected to the session — not assigned reading nobody does. If participants don't do the pre-work, the session should still work.
End every session with 5–10 minutes for participants to write: one thing I'll start doing this week, one thing I'll stop, one question I still have. Without it, training is information transfer.
The single most common failure in AI training is a learning objective written as "participants will understand generative AI." There's no way to assess that. Bloom's taxonomy gives you measurable verbs that change what the training has to do.
Participants will understand AI policy.
Participants will draft three sections of an AI policy (purpose & scope, approved tools, data privacy) using the worksheet template.
Participants will know how to use ChatGPT.
Participants will apply the nine-step prompt framework to produce one workplace-ready output (draft email, meeting summary, or process document).
Participants will be aware of AI risks.
Participants will identify three AI risks specific to their role and recommend one immediate safeguard for each.
These are the formats used across the talks on this site — every one of them maps to a Bloom's level above Remember.
Two-minute partner conversations on a specific question. Surfaces what's already happening in attendees' organizations. Good for opening a session.
Participants open ChatGPT, NotebookLM, or whatever tool the session covers, and produce something real in 10–15 minutes. Non-negotiable for any AI session over an hour.
Show an AI-generated artifact with intentional errors — a brief with fabricated citations, a draft with hallucinated statistics. Participants find what's wrong. Builds the review reflex.
Small groups pick from 2–3 messy scenarios with no clean answer ("an employee included client financial data in a ChatGPT prompt — what do you do?"). Surfaces exactly the decisions a policy needs to make.
Pairs use a fill-in-the-blank planner to practice pitching a new AI tool to a skeptical leader. One plays the proposer, one plays the boss, then switch. Builds confidence in advocacy.
Small groups use a worksheet to draft three policy sections in 15 minutes. Each group shares their strongest decision and toughest unresolved question. Closes a policy session with a working artifact.
A working timing template. Adjust ratios for shorter or longer sessions, but never drop below 50% active.
| Time | Section | Mode |
|---|---|---|
| 0:00 – 0:08 | Opening + objective preview | Lecture |
| 0:08 – 0:18 | Pair & share opener | Active |
| 0:18 – 0:35 | Core concept + cautionary tales | Lecture + discussion |
| 0:35 – 0:60 | Hands-on exercise or drafting activity | Active |
| 0:60 – 0:78 | Share-back + hard-case discussion | Active |
| 0:78 – 0:90 | Application planning + wrap-up | Active |
Active time: 65 of 90 minutes — 72%.
If you can't answer yes to all ten, the module needs another pass.
If your L&D team is building AI training and wants a working session to pressure-test the design, the half-day workshop format covers exactly this material — using your real modules as the working examples.