AI in Education #10: Five Things I Learned from Kris Boulton about Alpha School, Paying Kids, and Where AI Actually Fits

This is the tenth in a series of AI in Education specials, where I speak to the world’s leading experts about how AI is changing teaching and learning.

Kris Boulton once told me you should never ask a student a question they do not already know the answer to. He has spent the best part of 15 years building maths teaching that makes that possible — sequences of examples and non-examples so carefully ordered that failure is, in his words, designed out. He now runs Unstoppable Learning with Naveen Rizvi, working with schools from Australia to Texas.

So when Kris came on the podcast and told me he is now having more conversations with AI than with real people, and that he had just spent a working day inside Alpha School in New York, I knew this one was going to be good.

It was an absolute pleasure to have Kris back on the show. Here are five takeaways from our conversation that I think every classroom teacher and school leader will find interesting.

1. Alpha School has no teachers — and that is the whole point

Before I spoke to Kris, I had heard Alpha described as a school where the teachers are called “guides.” Kris stopped me right there. They do not call their teachers guides. They have no teachers. The guides are adult supports — coaches and mentors — and they are not permitted to help a child with an academic question.

Why on earth not? Kris quoted Joe Liemandt, the principal of Alpha School: they want the cleanest data set they possibly can get. If a child is not learning, they need to know that it is because the app is not good enough. If a guide quietly steps in and fixes a problem, that signal is lost. So the bet is enormous — that the app can be made good enough on its own, with the human role stripped right back to motivation.

Kris suggested that this approach might be more appealing to primary teachers — ask a primary teacher what they teach, and many will say “I teach children, not subjects.” But secondary teachers tend to love their subject. We love sharing it with someone meeting it for the first time. At Alpha, you do not get to do that at all. As Kris put it, you do not get to share any of the things you love and know about mathematics with anyone.

2. “Rich parents are rich parents. Rich kids are just kids.”

The standard objection to Alpha is the one I have made myself: How much can we learn from a school that charges top-of-market fees and selects for wealthy, motivated families?

Kris argued that whilst rich parents are rich parents, rich kids are just kids, with everything that comes with being a child.

He saw it first-hand. One lad, around five or six, was not having any of it — dysregulated, struggling, not engaging. The wealth of the parents did not magic that away.

So why does this matter? Because the easy dismissal — “it only works because the kids are loaded” — does not survive contact with the place. The harder questions remain. Does it work for children whose parents are not bought in? Does it work without the fees? But Becky’s point from her episode holds: you answer “Can this work at all?” before you answer “Can this work for everyone?”

3. They pay the kids — and the thing that bothers us is not the thing that should

Here is the Alpha detail nobody else in the series mentioned… the kids get paid. Hit your daily minimums, and you earn somewhere between one and two dollars a day. For the little ones, it is “Alpha bucks” for a curated store. For the older ones, it goes onto a debit card they can spend anywhere in New York.

I can feel some of you bristling. I bristled too. But Kris made me think harder about why.

The usual objection is that extrinsic rewards crush intrinsic motivation — pay a kid to do maths, and they will only ever do maths for money. Kris thinks the research and his own experience do not bear that out. Get genuinely interested in something, and you keep wanting to know more, paid or not.

So what is the discomfort? Kris’s sharper read is that paying kids pokes at a buried sense of gratitude. Thousands of pounds are already being spent on a child’s education, for their benefit — and now we are paying them a few dollars on top just to turn up and accept it. That feels off. But then he turned it on us: how many teachers grumble about attending CPD that is entirely for their own benefit? The delayed, abstract reward of “this will make you better” is, it turns out, just as hard for adults as for kids.

Whatever your moral position, Kris’s point is the one you cannot argue with: as a way of motivating people to show up and do the work, it is effective. For more on motivation and rewards, check out one of my favourite EconTalk episodes with Roland Fryer.

So, there are three additional points about Alpha School to add to those that we’ve heard throughout this series. Carl Hendrick was generous about the Alpha model. Becky Allen called it a benign laboratory worth building in. Adam Boxer said it could lead to the death of expert teachers. Where do you sit on that spectrum?

4. AI has cracked the practice — but not the teaching

This is where the conversation turned from a bit geeky, to full-on geek mode… and I loved it. It’s time to dive into the wonderful world of Atomisation, and how it relates to AI.

As long-time listeners to my podcast will know, Kris splits the learning experience into stages: initial instruction, initial testing, expansion (applying the idea to ever more novel cases), and then a long tail of practice over days and weeks. And his verdict on where AI sits is precise.

For the practice — the task sets that come after teaching — AI is there. It is perfect, in his words. Set the constraints, tell it every question must match the form of what was taught, and it will generate thousands of usable tasks in hours instead of the weeks it would take by hand. He could not have built his new Unstoppable Arithmetic programme without it.

But for the initial instruction and the expansion — the carefully sequenced examples, non-examples and categorical atoms that are the heart of his work — he has not seen it work yet. He has fed Claude every script for every video on his platform, whole curricula from Australia and the US, and it still gets the type of atom wrong every time. Not always badly. But too imperfect.

Why the split? Because the sequencing barely matters in the practice phase — every question is one the student has already met under tight instruction. It matters enormously in the instruction phase. And that is exactly the part the AI cannot yet feel its way through.

I have a slightly different view. I’ve managed to get Claude to produce teaching sequences for categorical atoms that meet the standard I can design on my own. And I’m able to do it in a fraction of the time and enjoy the experience much more as I collaborate with my super-smart, super-patient AI colleague. I’ve shared my efforts here.

Of course, my standards are likely to be much lower than Kris’s, so my point may be moot!

5. The average teacher is not listening to this podcast — and that should shape the advice

I put a crude scenario to Kris. A maths teacher has a finite amount of time to prepare for a lesson tomorrow. They have three options: build it by hand, download something already made, or open Claude or ChatGPT and generate it. Which leads to the best outcome?

Kris answered the easy half instantly. The average teacher will take what is already made, deliver it, and move on. And — his line, not mine — the average teacher is not listening to this podcast or reading this newsletter. We forget, constantly, what average in the system really looks like. If you are reading this, you are likely to be top tier!

Then he reframed the question. It is never one lesson tomorrow. It is 23 lessons next week. Whatever you choose has to work across all of them. Build by hand for 23 lessons? Out. Wrestle every one of them out of an AI? Out for most people, because of the time and the cost — we both agree that you need the £75-to-£200 models for this quantity and complexity of work, and this is not something most teachers will fund from their own pocket.

So who should play with AI? Well, like anything, what you get from it is a function of how you already think. Some people race through books and learn little. Others read slowly, argue in the margins, and learn loads. AI is the same. If you have the subject knowledge to catch its errors and push back, and the time and patience to do so, it can supercharge you. If you do not, you are the non-specialist putting wrong answers on the board because you could not spot them — exactly the danger Adam Boxer and Gráinne Hallahan warned about earlier in this series.

Here is what I think is a good way to dip your toes into AI resource creation…

Pick one lesson you’re teaching next week. Plan your own resource from scratch. Prompt the AI to do the same. Then compare the results. What does your resource do better? What does the AI do better? Then feed your conclusions back to the AI, share the resource you created, and see if, together with a few more back and forths, you can come up with something that is greater than the sum of its parts. That back-and-forth is where the learning lives. The trap is letting it do the thinking for you.

Boxer versus Hendrick: where does Kris land?

(That’s my attempt at a clickbaity subheading)

Adam Boxer told me AI will not revolutionise education because nothing ever does. Carl Hendrick told me this time is different. So where does Kris sit?

Roughly with Adam, but for a different reason. He thinks the questions everyone keeps asking — will Alpha scale, will it work in a UK comprehensive — are the wrong questions, because nobody knows. What he is sure of is the cost of getting there: tens of billions in capital, retraining, and the unavoidable question of what happens to all the teachers if you flip the model and there are no teachers in the building? Change, if it comes, will come bit by bit, over decades. And then one day everything is different, and you realise it took 30 years.

Over to you

This was a brilliant conversation, and I have only scratched the surface here. You can listen to or watch the full episode on the Mr Barton Maths Podcast. And do check out the Unstoppable Learning Substack, where Kris and Naveen post nearly every week, and the Unstoppable Learning website.

This is the tenth in a series of conversations I am having with leading thinkers about AI in education. Here are the others in the series:

1. Daisy Christodoulou: podcast and write-up

2. Barbara Oakley: podcast and write-up

3. Adam Boxer: podcast and write-up

4. Carl Hendrick: podcast and write-up

5. Simon Woodhead: podcast and write-up

6. Dan Meyer: podcast and write-up

7. Bibi Groot: podcast and write-up

8. Becky Allen: podcast and write-up

9. Gráinne Hallahan: podcast and write-up

I would love to know: having seen inside Alpha, are you more tempted by the two-hours-and-the-afternoon-back model, or more convinced it could never work here? And on the AI question — are you using it to learn, or letting it do the thinking for you?

Thanks so much for reading, and have a great week.

Craig

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… checked out my brand-new book series: The Tips for Teachers guides to…

And checked out Ultimate Retrieval Tool page on Mr Barton Maths?