AI as leverage

AI makes production faster. I make sure the right problem is being solved.

I use AI throughout my process to accelerate research, analysis, prototyping, production, and documentation. The judgment stays with me: what problem actually needs solving, what people need to understand, what should change, how the learning experience should work, and whether it worked. That combination is why the work moves quickly without getting thinner.

A designer reviewing a printed draft beside a laptop — AI drafts and accelerates. The design judgment is where the value is.

Analysis

Understand faster, decide the same way.

AI-assisted

Research synthesis across scattered source material and prior program documentation.
Audience analysis drafts — likely prior knowledge, obstacles, and motivations to verify with real people.
Content review that surfaces gaps, contradictions, and unstated assumptions in SME material.

Stays human

Deciding whether learning is the right intervention at all, and which performance gap is worth solving first. AI can summarize a situation; the recommendation is mine to make and defend.

Design

More options on the table, chosen by a designer.

AI-assisted

Brainstorming instructional approaches and alternate sequences to compare against each other.
Scenario creation — generating plausible situations and wrong answers, then hardening them against real practice.
Storyboarding drafts that make structure visible early enough to change cheaply.

Stays human

Objectives, assessment evidence, and alignment. If the objective and the evidence are wrong, an efficiently produced course simply arrives at the wrong destination faster.

Development

Build the prototype while the thinking is still warm.

AI-assisted

Working prototypes learners and stakeholders can click, instead of static specifications.
Content refinement for plain language, reading level, and consistent voice.
Accessibility checks on structure, alt text, contrast, and keyboard operability as a first pass.

Stays human

Instructional accuracy, tone with a specific audience, and the final accessibility judgment. Automated checks catch defects; usability is a judgment made against a specific audience.

Evaluation

Read the signal, then decide what changes.

AI-assisted

Feedback analysis across open-response comments to surface recurring themes.
Pattern detection in assessment data to locate where learners consistently break down.
Improvement recommendations as a first draft for the next iteration.

Stays human

Interpreting whether the original performance gap actually closed, and choosing which changes are worth the cost of making them.

Working rules

Four constraints applied to every AI-assisted project

Judgment stays with the designer

Diagnosis, objectives, assessment alignment, and accessibility decisions are mine. Those choices determine whether learning changes anything.

Everything factual is verified

AI output is treated as a draft, never as a source. Any claim, figure, or citation is confirmed before it reaches a learner.

Data minimization by default

Confidential, proprietary, and personal information is never pasted into a tool that has not been approved for it.

Speed is spent on quality

Time saved on drafting is reinvested in diagnosis, practice design, and revision — better outcomes per hour, not more content per hour.

Designing learning for new tools and ideas

These two demonstrations are not courses about AI so much as courses about adoption — building learner confidence, responsible use, and practical application when something unfamiliar lands on people's desks.

AI Workplace Essentials AI Productivity Toolkit