Over the past few weeks, I kept seeing the same move in places that have nothing to do with each other.
For the past year, our Design Futures workshop has started the same way. Participants take a piece of paper and sketch a bicycle built for a Nebraska winter. Then they snap a photo of the sketch and ask AI to reproduce it, sometimes from the drawing and sometimes from a written description. By the time AI enters the process, a lot of thinking has already happened. Sometimes the results are fantastic. Sometimes the AI hands back a bike that floats above the pavement, not by intent, but because that is what it perceived.
Last week we interviewed Nick Husbye on the podcast, and he described something curiously similar. Nick asks his preservice teachers to draft a lesson plan by hand. When they have a draft, they let the AI come in and critique the plan. What Nick teaches is that AI can be a very good critic, and that the teacher still has the agency to say, “I hear this critique, but it doesn’t apply here, and here’s why.”
The third example comes from our work with Southeast Community College, where we are asking Writing Center tutors how they work with AI. One tutor described a simple structure they give students.
Start with the rules. Know what your instructor allows so you don’t break them.
Use your own words.
Only then bring in AI to critique, improve, and suggest.
Finally, in Tina Shetabi’s work on using AI in the fashion design process som students reported that starting with AI gave them a strong sense that it is not their work. When they started with their own sketch, they felt more ownership.
The pattern
These are four examples from the last few weeks, and there are more. None of these people coordinated. Each one puts human thinking first and AI second, sometimes third. Each treats AI as a critic or collaborator that extends the existing work or points out what isn’t working. The ideas stay thoroughly human. As Nick puts it, AI should amplify your thinking, not export it.
Why the order matters
All four cases involve novices, people entering a domain they are becoming experts in but are at the beginning stages. That might mean designing a bike, writing a lesson plan, drafting a paper in a writing class, or, designing a garment. For novices, the priority is learning the domain and thinking it through.
A while ago I called this the novice problem. Novices are less likely to develop if they start with AI. We want them to start with their own thinking, let AI mirror back what is working and what isn’t, and then iterate. Some of that iteration can happen with AI, but in many cases I want them to go back and iterate with their own thinking.
The novice’s first draft does two things. It gives them something to compare the AI against, and it gives them a sense of ownership.
When students start with AI, they tell us they don’t feel ownership. Their sense of agency is taken away. They often go with whatever the AI made, and they become less and less critical. We want students critical enough to compare what they intended with what is on the page, and to learn from that comparison instead of being replaced by it.
The bikes show how this works. Students asked AI to add cup holders, which is a fine idea. Some kept pushing until the bike had 30 cup holders in ridiculous places. They knew immediately it was funny and implausible. Because AI will go as ridiculous as you ask, stretching it shows students quickly where it doesn’t understand the physics of a working bike or a wearable garment.
That signal leads to metacognition. Even as novices, they are the ones who can evaluate the result, because the work started with their intent.
What this isn’t
This is not an argument against AI. In each case, the AI step adds real value. It also isn’t a universal rule.
The sequence lines up with decisions universities are making about when to bring AI into the curriculum in a more organized way, including the debate in computer science education about when and how much to introduce it. In most of our interviews with professors and students, both groups say the same thing. In entry-level classes, AI makes little sense as the creator. It becomes a crutch because it is too good, and students build up cognitive debt. Once learners move past that stage, the roles can shift. They need to learn how to use AI when they’re stuck, when they lack ideas, or when they need to do research. There are times when AI should go first.
What schools can do
Most AI policies, in K-12 and higher ed, focus on which tools are allowed and what counts as acceptable use. That’s fine. Many policies also leave a lot of agency to instructors, which is great, but instructors still need a framework to work from.
The better question is about sequence. What do students do, and in what order? The answer depends on how novice they are and how much expertise they bring. This changes the policy conversation. A tool can be allowed at one point in the process and not another, with a different intent at each stage.
A human sketches the bike, drafts the paper, or designs the garment. Then AI comes into play, because we can iterate much faster with it. We can ask for small changes or big ones, extend the thinking, or get a critique. Everything is judged against the original intent. That keeps the learner in control, even as a novice, and keeps the product connected to the person who created it.
Three practices make this work.
Model it yourself. Nick models his own failures with AI, and I try to do the same. I show what I used and where it didn’t work. That gives students permission to critique AI without rejecting all of it. It isn’t all in or all out. Sometimes I dump what the AI produces. Sometimes I’m amazed. Usually I’m somewhere in the middle. And sometimes I say out loud, “This isn’t very good. I prefer what I made myself, and I’m not sure AI was worth using here.”
Make comparison the assignment. Ask students to compare their intent with the final product and explain what they learned from the critique. That metacognitive move is where much of the learning happens.
Grade the comparison. If your students respond to grades, put significant weight here. AI output quality varies, so grading only the product partly grades the AI. Grading the comparison grades the thinking.
Where have you seen it work?
Nick’s advice to his students is simple. Write first. Use AI, but use it a little later.
These are observations from all of us grappling with how to teach in the age of AI. So my question is if you seen this sequence work, and where have you seen it fail?
