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What do you need to preserve when AI speeds up your work?
That is the question I keep coming back to.
Generative AI makes it easier to move quickly: more drafts, more alternatives, more critique, more revision, more possibility.
But speed changes the work.
What gets lost when iteration becomes faster than reflection?
Where are you already experiencing your own version of Prompt 37?
What part of your work becomes more important as AI gets better?
This framework did not start as a framework
It grew out of practice.
Teaching with AI. Writing about AI. Designing with AI. Helping others make sense of AI. Revisiting the same projects often enough to realize that the interesting problems changed as the tools became more capable.
My earlier work focused heavily on where AI helps and where human judgment remains essential.
Writing The AI Field Guide for Learning Designers pushed that thinking further.
A later course redesign made another problem increasingly visible: once AI made iteration dramatically easier, preserving design continuity became harder—and more important.
That is where Expand → Preserve → Evolve came from.
It is my current attempt to describe a practice that is still changing.
And that word matters: current.
Work with me
I am interested in working with people and organizations that are trying to use AI thoughtfully in learning, design, faculty development, and organizational practice.
That might mean:
rethinking a course or program;
building a more coherent AI-supported design workflow;
helping a team develop practical AI literacy;
facilitating a workshop or professional-development session;
reviewing an existing learning experience;
designing resources, systems, or strategy;
or helping make sense of a messy problem that is not going to be solved by one more prompt.
My work tends to live at the intersection of instructional design, AI, learning technology, systems thinking, and practical implementation.
I am especially interested in projects where the goal is not merely to “use AI,” but to use it in a way that improves the quality of the work without losing the judgment, context, and intention that made the work worth doing in the first place.
If that sounds familiar, I would genuinely like to compare notes.
Let’s talk about the work.
Keep exploring
The AI Field Guide for Learning Designers
Hands-on Strategies for Education, Training, and Instructional Design
The book explores the practical side of human–AI collaboration in learning design: using AI for momentum, critique, iteration, and production while keeping instructional judgment where it belongs.
Humans Still Teach represents where that thinking has continued to evolve.
One last question
What becomes harder to preserve when AI makes your work faster?
You do not need to have a polished answer.
I am interested in the tension.