THE COLLISION (Read This Before You Touch ChatGPT)
Here’s what a child developmental psychology expert sees immediately—and everyone else misses:
Learning works only inside the learner’s zone of proximal development.
Too easy = boredom.
Too hard = shutdown.
Right level = progress.
AI behaves the same way.
But here’s where people get it wrong. They hear that and think:
“So I need to simplify my prompts.”
No.
That’s how you get garbage.
Teachers don’t simplify. They sequence.
They front-load prerequisites, constrain the task, then gradually release complexity.
I’ll argue against myself for a moment:
Yes, clarity matters.
Yes, specificity matters.
But clarity without developmental staging is useless.
That’s the part everyone skips.
That’s why teachers crush this.
I said I’d come back to this. I am now.
THE STEPS
STEP 1: Define the Learner (Not the Task)
What to do
Stop telling ChatGPT what you want.
Tell it who the learner is and what they can already do.
This is where teachers quietly dominate.
Copy‑paste this prompt:
You are assisting with instruction for a learner with the following profile:
- Age/grade level:
- What they already understand:
- What they struggle with:
- Attention span and motivation level:
- Constraints (time, resources, standards):
Do not produce content yet. Confirm your understanding of the learner and ask ONE clarifying question.
What to expect
ChatGPT will slow down.
Good. That’s compliance, not hesitation.
Common mistake to avoid
Skipping this because “it feels obvious.”
That $847 mistake? This is it. People skip learner modeling and then blame the model.
STEP 2: State the Outcome in Observable Behavior
Teachers don’t say “understand fractions.”
They say “correctly compare two fractions with unlike denominators.”
AI needs the same discipline.
Copy‑paste this:
The learner should be able to demonstrate success by doing the following observable actions:
- Action 1:
- Action 2:
- Action 3:
Do not teach yet. Rewrite these outcomes to be more precise if needed.
What to expect
The AI will tighten your language.
Sometimes it will push back. Let it.
Common mistake
Vague verbs. “Know.” “Understand.” “Appreciate.”
Those are not behaviors. They are hopes.
STEP 3: Scaffold the Task (This Is the Whole Game)
Everyone talks about prompt engineering techniques.
This is the only one that matters.
Scaffolding.
Copy‑paste:
Design a scaffolded learning sequence with 3 phases:
1. Supported practice (modeling + guidance)
2. Guided practice (partial independence)
3. Independent application
For each phase, specify:
- What support is present
- What is removed
- What success looks like
Do not generate content yet.
What to expect
Structure. Calm. Predictability.
You’re teaching the AI how to teach.
Common mistake
Asking for the final worksheet immediately.
That’s like handing a kid a test before the lesson.
Stop doing that. Seriously.
STEP 4: Introduce Productive Constraint (Play, Not Chaos)
Child psychology 101: play works because it’s bounded.
AI works the same way.
Copy‑paste:
Apply the following constraints to all outputs:
- Use language appropriate to the learner profile
- Limit explanations to [X] sentences unless asked
- Ask a check-for-understanding question after each section
- Avoid introducing new concepts not listed in the outcomes
Acknowledge these constraints.
What to expect
Cleaner output. Less rambling. Fewer hallucinations.
Common mistake
Thinking constraints limit creativity.
They don’t. They channel it.
X is everything.
Except when it isn’t.
This is one of those times it is.
STEP 5: Generate the Lesson (Now You Let It Work)
Only now do you ask for content.
Copy‑paste:
Using everything above, generate the Supported Practice phase.
Format:
- Objective
- Teacher modeling script
- Student prompt
- Likely misconception
- Immediate feedback response
Wait for confirmation before moving to the next phase.
What to expect
This will feel… professional.
Because it is.
Common mistake
Letting it generate all phases at once.
Pacing matters. Even with machines.
STEP 6: Build Feedback Like a Human, Not a Bot
Teachers are elite at feedback.
Most AI prompts ignore this entirely.
Copy‑paste:
For each common mistake, provide feedback that:
- Names the error without judgment
- Explains why it occurred
- Gives a next step that stays within the learner’s ability
Do not introduce new content.
What to expect
Feedback that doesn’t sound like a fortune cookie.
Common mistake
“Explain again but simpler.”
That’s not feedback. That’s panic.
STEP 7: Add Differentiation (Quietly)
Differentiation isn’t extra work.
It’s parameter tuning.
Copy‑paste:
Create two variations of this lesson:
- One for a learner who needs more support
- One for a learner ready for extension
For each, specify what changes and what stays the same.
What to expect
Targeted adjustments instead of total rewrites.
Common mistake
Rewriting everything.
Teachers don’t do that. Neither should you.
STEP 8: Save the Prompt Stack (This Is Reuse)
You now have a prompt stack.
Save it.
If you don’t want to rebuild this every time (and you shouldn’t), there are pre-built prompt packs at wowhow.cloud/products that already encode this scaffolding logic. Use code BLOGREADER20 if you want to skip weeks of trial-and-error. Practical tip. Not a sermon.
Common mistake
Treating prompts as disposable.
They are curriculum.
## Why are teachers naturally better at AI prompting?
Because teachers don’t start with instructions.
They start with readiness.
Because teachers think in stages, not outputs.
Because they expect misunderstanding and plan for it.
Because they don’t confuse verbosity with clarity.
Prompt engineering techniques copied from software docs miss this entirely.
They assume the model is the problem.
It isn’t.
The instruction is.
THE RESULT
Here’s what a finished output looks like (excerpt):
Objective: Students will correctly compare two fractions with unlike denominators using visual models.
Teacher Modeling: “Watch how I draw both fractions using the same-sized rectangles…”
Student Prompt: “Now you try with 3/4 and 2/3. Draw before deciding.”
Likely Misconception: Student compares numerators only.
Feedback: “You compared the top numbers, which is common. The issue is the pieces aren’t the same size yet. Let’s fix that first.”
No magic.
No poetry.
Just instruction that works.
That’s chatgpt for education done right.
LEVEL UP
Once this feels natural:
- Turn your prompt stack into subject-specific templates
- Add metacognitive prompts (“Explain why this method works”)
- Use the same structure for emails, rubrics, parent communication
- Train students to prompt within this scaffold (yes, really)
And here’s the contradiction I promised:
Teachers are the best prompt engineers.
Except when they try to sound like engineers.
Stop optimizing words.
Start designing learning.
That’s what AI responds to.
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