AI didn't break education.
It exposed what was already fragile.
If a tool can generate a decent five-paragraph essay in 20 seconds, the essay was never the point.
It was a proxy.
And like every proxy in schooling, it worked… until it didn't.
That's the moment many international school teams are living through now.
A student submits "flawless" writing that doesn't sound like them.
A teacher tries an AI detector, gets a scary percentage, and then hesitates—because the student is an English language learner and the teacher knows false accusations destroy trust.
A curriculum leader drafts a policy, but it feels like writing rules for the weather.
You can't legislate a wave.
You can only learn to teach in the ocean.
The real problem isn't AI. It's what we've been rewarding.
You're not worried that students will "use tools."
Students always use tools.
You're worried they'll skip thinking.
Because so much of school has been built on a silent deal:
"I'll give you the task."
"You'll produce the product."
"I'll grade the product."
Generative AI breaks that deal.
Not because it makes cheating easier.
Because it makes product-only assessment meaningless in any task where the product is mostly language.
And that includes far more than English—history essays, business reports, TOK reflections, science explanations—even parts of Maths, once students can ask for "a worked solution with reasoning."
So the crisis isn't "students using AI."
The crisis is: we've been confusing writing with thinking, and now the difference is visible.
The consequence if you don't redesign: an arms race you will lose
If your response is "ban it," you will spend the year enforcing something you can't enforce.
If your response is "detect it," you'll spend the year accusing students with tools that can't provide certainty.
Stanford researchers have warned that AI detectors can be biased against non-native English writers—exactly the students many international schools serve.
Even institutions that already used Turnitin at scale have stepped back from AI detection because the stakes are too high.
Vanderbilt publicly disabled Turnitin's AI detection tool, citing lack of transparency, false positives, and the risk of harm—using the simple math that even a 1% false positive rate becomes hundreds of wrongly flagged papers at scale.
That's the long-term consequence of the "policing" path: more stress, more conflict, more performative compliance, less learning—and a quiet collapse of trust, the one currency you cannot replace with policy.
Proof that this isn't new: calculators didn't kill maths either
When calculators became common, maths teachers didn't "lose."
But they did lose one thing:
The ability to treat manual calculation as the proof of understanding.
So the profession moved—more estimation, more modelling, more complex problems, more emphasis on reasoning over arithmetic stamina.
AI is that shift again.
Except it touches every subject that uses language as a container for thinking.
The solution: assess the thinking that produced the work (not the work alone)
Here's the redesign that holds up in real classrooms.
Not because it's trendy.
Because it's aligned with what AI can't reliably fake under live conditions: judgment, reasoning, and ownership.
Step 1: Make "process evidence" part of the grade
Stop asking only for a final product.
Ask for a trace of thinking.
Use a Decision Log.
One page.
Simple prompts:
- What did you ask AI?
- What did you reject?
- What did you verify?
- What changed after feedback?
If a student can't explain their choices, they don't own the work.
This shifts the accountability from "did you use AI?" to "did you think?"
Step 2: Add an oral layer to written work
Keep the essay.
But add a short viva.
Two minutes.
One question.
One follow-up.
Not to intimidate.
To validate authorship and understanding.
In international schools, this is a gift.
It rewards students who can explain ideas even if their writing is still developing.
And it's naturally resistant to copy-paste workflows.
Step 3: Redesign tasks so AI becomes a tool inside learning, not a shortcut around it
The goal isn't "AI-proof assignments."
That's an arms race.
The goal is AI-amplified learning.
Examples that work across curricula:
- AI as a debate opponent: "Argue the opposite of my claim. Make it strong."
- AI as a misconception detector: "Find the weakest assumption in my reasoning."
- AI as a coach: "Ask me five questions before you give any suggestions."
Step 4: Teach AI literacy explicitly, or you create equity gaps
When you don't teach it, students still use it.
But only the confident, well-resourced, and English-fluent use it well.
This is why governance matters.
UNESCO's guidance pushes for human-centered, ethical adoption and capacity-building—especially for teachers and systems, not just students.
A practical classroom move:
Create a shared "AI use agreement" with three columns:
- Allowed with disclosure
- Allowed in draft stage
- Not allowed
Clarity reduces conflict.
Step 5: Use AI to give teachers time back—then reinvest it in human work
The U.S. Department of Education frames AI around "humans in the loop," equity, safety, and transparency—rejecting the idea of AI replacing teachers.
So use AI for what drains you:
- Drafting exemplars
- Creating differentiation
- Generating question banks
- Summarizing texts
Then invest the saved time into what only you can do:
- Conferencing
- Coaching
- Feedback on reasoning
- Community
Objection: "But my school needs a clear rule. Parents will demand it."
Yes.
And the rule can't be "no AI."
That's not enforceable.
The rule needs to be:
Students may use AI, but they must disclose use and demonstrate ownership.
That's defensible to parents because it mirrors the real world.
Professionals use tools.
Professionals are still accountable.
Also, it aligns with what credible institutions are concluding: detection is shaky, but redesign is durable.
AI is already strong at "school-like" tasks—so the shift is inevitable
OpenAI's GPT-4 technical report showed performance across many exam-style benchmarks, including a simulated bar exam score reported around the top 10% of test takers.
Whether we like it or not, "answering questions" and "producing competent text" is now partially automatable.
So if we keep rewarding only those outputs, we train students to outsource.
If we reward reasoning, we train them to lead the tool.
What changes when you do this well
Imagine six weeks from now.
A student submits an essay.
But attached is a decision log showing how their claim evolved.
You do a two-minute viva.
They defend the argument.
They admit what AI helped with.
They explain what they rejected.
You don't feel like a detective.
You feel like a coach again.
And students stop treating AI like a cheating machine.
They start treating it like a thinking partner—with boundaries.
Getting started
Pick one assessment you'll set in the next two weeks.
Then make just three edits:
- Add a one-page Decision Log requirement
- Add a two-minute oral defense (during class or as a recorded response)
- Add a disclosure line: "How did AI support your process (if at all)?"
Keep everything else the same for now.
Small change.
Big shift.