It arrived like clockwork. The same comment on my school report card, every single time.
“David’s cavalier attitude has led him to underperform again this year.”
And fair enough – it wasn’t entirely wrong. I spent most of school disengaged, underwhelmed, and half-asleep. Not because I was lazy, but because the system was.
It made learning feel like a chore. On the whole, I was bored, unchallenged, and unsupported by teachers who failed to bring subjects to life.
If AI had been around in the 1980s, I’d absolutely have used it to write my homework. But I’d also have used it to explain the textbooks that weren’t written for dyslexic kids with a short attention span.
Because AI tools aren’t simply cheating engines – they can also be a lifeline. The difference is in how you use them. Or perhaps more importantly, whether you’re even taught how.
Which Dumb Extreme Do You Choose?
The world isn’t very good at nuance these days. And it’s no different when it comes to opinions about AI in education.
In one corner, we have the Just Say No brigade – schools and universities frantically banning AI tools like it’s 1999 and someone just discovered calculators. In the other corner, we have the Digital Kool-Aid crowd, waving AI around like it’s a pedagogical panacea.
Neither approach helps students learn. The ban avoids the issue entirely. The embrace outsources it brainlessly.
And in the middle, we have the poor students. Still trying to figure out how to learn, how to think, and how not to get caught in a system that either punishes curiosity or replaces it with copy-paste convenience.
The Real Problem: We’re Letting Our Brains Power Down
Learning isn’t magic. It’s mental weight-training. It requires attention, struggle, reflection, and the occasional mutter of swear words at a frustrating problem the last lecture didn’t prepare you for.
Cognitive science tells us that deep learning comes from effortful engagement – wrestling with ideas, retrieving facts, summarising in your own words, failing and trying again.
But here’s the problem: a combination of AI and poor teaching techniques enables people to look as if they’re progressing without truly engaging their brains.
Students are now inundated with tools that do the thinking for them. And they use them not because they’re lazy, but because the systems around them don’t demand – or inspire – anything else.
It’s not just what a teacher says that matters; it’s how they say it. When lessons fail to capture a learner’s imagination or light a fire inside their belly, they’re not going to feel motivated to push themselves beyond the bare minimum.
And that bare minimum can now be less than ever before.
A recent (and much discussed) study from MIT shows that when students use ChatGPT to complete writing tasks, their brain activity drops significantly, especially in areas related to memory and reasoning. This isn’t just about convenience. It’s about cognitive offloading – outsourcing the struggle, and with it, the learning.
Of course, there’s nothing truly surprising about this. I’ve been talking about the issue for a couple of years – this is just the scientific rubber stamp.
But it’s why we need a better approach to learning.
Educational institutions are stubbornly holding on to the way they’ve always done things. Instead of keeping up with the changing world around them, they complain how everything’s changed and point the finger of responsibility elsewhere.
It’s like an accountancy firm tenaciously sticking to abacuses and criticizing anyone who uses a calculator.
The difference between poor and powerful teaching is not about the technology involved; it’s about how well it activates the students’ brains.
Brains-Off learning is passive and outsourceable.
Brains-On learning is active and involving.
A Brains-On approach doesn’t mean banning AI. It means knowing when it sparks thinking and when it short-circuits it. When it elevates learning – and when AI erases it.
It’s a tool, not a substitute. A training partner, not a stunt double.
The Brains-On Learning Model
So in an attempt to move us beyond polarising extremist points of view, I thought I’d share something I’ve been working on for the past few months. It’s an approach that elevates the argument above technology-focused bickering. It’s about defining the role of education as preparing young brains for a productive and purposeful life.
By that, I mean it’s the role of educators to build valuable neural structures in the heads of their students, and to strengthen the neural pathways that will serve them in their lives and careers.
I know there are educators who will hate everything I’ve just said, and I’m happy to hear their objections. Because I’m putting this out there for discussion and refinement. The clay of this approach has not yet been fired in the kiln.
There are things that I’ve not fully considered at this stage. Like, how to measure the success of this approach (short of putting everyone through an fMRI to see how much neural matter they’ve accumulated). And whether it applies to Accountancy as well as it does to Graphic Design, or Structural Engineering, or Viticulture and Oenology (I’ve no idea what that actually is).
But focusing on growing brains feels like a good approach to me. Especially when the default way many people use AI tools will naturally lead to the opposite.
There couldn’t be a more important time for us to rethink our approach. Because AI is fundamentally changing our relationship with the very act of thinking.
Let’s Take It From Theory to Practice
It’s all very well developing a framework that only lives in the theoretical plane. But it can only make a difference when there are use cases and practical applications. So that’s what I’ve been mulling recently. (If you want to endorse me for ‘mulling’ on LinkedIn, please do. It would sit neatly between my endorsements for ‘swearing’ and ‘jam making’.)
So I listed out many of the key educational tasks and looked at them through the lenses of Brains-Off and Brains-On approaches. To get the most value from each of these learning tasks you need to apply cognitive effort. AI can help – but only if it keeps the student doing the mental work.
Here are the tasks:
1. Comprehension (Understanding New Content)
- Brains-Off: Students skip the reading and ask AI for the summary.
- Brains-On: AI acts as a coach, rephrasing hard bits, simulating a character (yes, Gatsby can now explain the green light himself), or prompting questions as students read. See how adaptive scaffolding works.
2. Memorization and Recall (Retaining Knowledge)
- Brains-Off: Why remember anything when ChatGPT’s right there?
- Brains-On: Use AI tools that implement spaced repetition and active recall, like smart flashcards and adaptive quizzes. The aim is for AI to train the brain, not replace it.
3. Note-Taking (Recording and Structuring Information)
- Brains-Off: AI transcribes everything. Students can drift off during lectures.
- Brains-On: Students take their own notes, then ask AI to highlight gaps, suggest structure, or convert them into concept maps. Generative note tools can help if they’re used after the student writes, not instead of.
4. Summarisation (Distilling Ideas)
- Brains-Off: TL;DR everything via an AI shortcut.
- Brains-On: Students write their own summary, then compare it to AI’s. What did they miss? Did they include too much? This feedback loop builds real summarising skills. Here’s why summarising matters.
5. Critical Thinking (Evaluating and Analyzing Information)
- Brains-Off: Ask AI for “an argument about climate change” and accept it blindly.
- Brains-On: AI challenges your position, plays devil’s advocate, or offers flawed arguments for students to critique. Studies show this use actually builds reasoning – when students remain in charge of judgment.
6. Application (Using Knowledge in Context)
- Brains-Off: Type a maths problem in. Copy the answer out. Done.
- Brains-On: AI nudges rather than solves. It breaks the problem into steps and prompts the student to try each one before giving hints. This tutor-style approach keeps thinking alive.
7. Creative Thinking (Generating Ideas and Making Connections)
- Brains-Off: “AI, write me a story.”
- Brains-On: Student generates their ideas first. Then use AI to expand, remix, or push their thinking further. Research warns that leading with AI limits originality – so save it for iteration, not inspiration.
8. Discussion and Debate (Collaborative Thinking)
- Brains-Off: Students read out AI-generated points during group work.
- Brains-On: AI helps students prepare through practice and feedback. AI tools simulate opposing views or offer neutral summaries to build on. Human-to-human dialogue remains irreplaceable. AI should scaffold it, not replace it.
9. Reflection and Metacognition (Thinking About Learning)
- Brains-Off: AI tracks everything. Students reflect on nothing.
- Brains-On: AI prompts students to review their mistakes, identify what worked, and plan their next steps. Metacognition is key, and AI can support it without replacing human introspection.
Let Students Help Write the Rules
I don’t believe the Brains-On model will work well if it’s imposed like another tech policy diktat. And the same applies to any other attempt to control AI use among students. You can’t just hand out a PDF titled “How Not to Cheat with ChatGPT” and expect students to adhere to it.
Research shows that over half of learners will continue to use AI tools even if they’re banned.
Instead, it’s better to involve students in writing their own AI study guidelines. Explain the science. Show them what happens when the brain checks out. Let them see that using AI to think with is empowering. Using it to think instead of is academic atrophy.
If students understand the why, they’ll co-own the how. And they’ll be more likely to use AI tools responsibly.
What Good Looks Like
This is the tl;dr bit where I summarise some of the key parts of the Brains-On approach:
- Teachers and students co-create AI guidelines grounded in learning science
- Assignments reward thinking, not regurgitation
- Teachers are trained in prompt design and AI-aware pedagogy
- Every student understands that the goal isn’t just to deliver a correct answer – it’s about the effort that gets you there
This isn’t about how AI should or shouldn’t be used for education. It’s about how brains should be used for education. It puts the focus on building the long-term skills and knowledge of the students, rather than on how technology is used.
It’s about recalibrating our approach to education so that everyone wins.
I strongly believe we shouldn’t just let students use AI – we should encourage them to do so and educate them on how to use it properly. That means we make damn sure they’re using it with their brains on.
Anything else isn’t education. It’s outsourcing.
This guest post was written by Dave Birss. Dave is co-founder of The Gen AI Academy, where he helps organizations unlock the real value of artificial intelligence. He’s also a popular LinkedIn Learning instructor, where more than a million learners worldwide have tuned in to his practical approach to making sense of AI.
A version of this post was originally published on Dave Birss’s newsletter, Experiments in Intelligence.
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