AI in Education #8: Five Things I Learned from Becky Allen about Why the Apps Are Coming but Schools Will Mostly Stay the Same
This is the eigth 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.
Becky Allen trained as an economics teacher, taught some maths to fill up her timetable (she still feels guilty about how badly she taught it), and has spent the last 20 years as an education researcher. She co-founded Teacher Tapp, the app that asks over 10,000 teachers a day what is going on in their schools. She co-authored The Teacher Gap, which I think is one of the most underrated education books of the last decade. And she now spends most of her time at Alpha School, evaluating learning apps, writing specifications grounded in the science of learning, and working on assessment.
She is also one of the most genuinely optimistic people about AI in education that I have spoken to in this series. But her optimism has edges to it that I was not expecting.
It was an absolute pleasure to have Becky back on the show, and I wanted to share five takeaways from our conversation that I think every classroom teacher and school leader will find interesting.
1. Learning apps will replicate best practice at scale – but they will not discover new ways to teach
Here is where Becky’s optimism starts, and it is worth understanding precisely what she is and is not claiming.
Becky is claiming that AI will allow us to take what we already understand about effective teaching and build it into apps that can deliver that practice to every student, consistently, at scale. In a profession she describes as a leaky bucket of expertise – where we lose enormous numbers of teachers every year, struggle to explain to new teachers how they should teach, and watch them gradually learn through trial and error before many of them leave again – that is not a small thing.
Becky is not claiming that AI will figure out how to teach better than we currently know how. Why? Because education has a verification problem.
In chess, a machine can try a million moves and instantly verify which ones lead to winning. In driving, a car can try a manoeuvre and immediately check whether it crashed. This is what makes reinforcement learning – the technology behind most of AI’s breakthroughs – so powerful. Try something, check if it worked, do more of what works.
But in education, we cannot cut open a student’s brain and check what happened. We can run an immediate assessment, but it cannot measure everything. Crucially, a student can perform well in the moment and forget everything a week later. The things we really care about – deep understanding, flexible knowledge, transfer – play out over weeks, months, years. You cannot run reinforcement learning cycles on that timescale.
This is why, as Becky pointed out, the teaching profession itself has not cracked the problem of “Why do we not know how to teach everything by now?” We do not, because we have always had a verification problem. And so do the machines.
I found this a useful addition to Carl Hendrick’s prediction that AI-powered platforms would discover things about learning that we have been trying to figure out for decades. Becky’s position is more cautious: the platforms will be excellent at implementing what we already know, but the hard problem of verifying whether something actually worked for a particular student over time is not going away.
2. The best things AI can do right now are the things a great one-to-one tutor would do
I asked Becky what she has actually seen that impressed her – not promises, not prototypes, but things working right now with real students. She gave me two examples.
The first is prerequisite knowledge activation. Before a lesson on circle geometry, the app asks the student: “We are about to learn something new about circles, and in order to do this, we need to remember something called Pythagoras’ theorem. Just tell me anything you can remember about it”. The student responds in free text. If they remember everything, the conversation pushes further to find the limits of their knowledge. If they say they cannot remember anything, a follow-up prompt narrows it down: Pythagoras’ theorem is about a particular type of triangle, a right-angled triangle – does that ring a bell?
This is what a good one-to-one tutor would do. It takes about three minutes. And it is so much more productive than either a passive reminder or a single multiple-choice question.
I pushed back. In my experience, the best teachers do check prerequisite knowledge – either through a whole-class starter on mini-whiteboards or by analysing homework data before the lesson. Becky agreed that those are good approaches, but pointed out that the app version is adaptive in a way that whole-class checks cannot be. The conversation with each student can last three minutes or thirty seconds, depending on what they already know. In a class of 30, you cannot do that.
The second example Becky cited is guided worked examples. Instead of showing a student a complete worked example on screen – which Becky says students tend to scan with their eyes darting around rather than working through step by step – the app breaks it up. It shows the first part. Then it asks: right, so what are we doing now? The student says: dividing both sides by six. The app says: off you go, tell me when you have the answer. Then we move to the next stage.
I see the static version of this going wrong in classrooms all the time. A teacher puts a worked example on the board, maybe talks through it, and then the students copy it down. But as I wrote in my Myth of Copying Things Down piece, nothing magical happens in the process of transferring something from a board to a book. The app version forces the student to engage with each step before they can see the next one. That is a genuine improvement.
3. Khanmigo failed because students will always take the path of least resistance – and you have to design for that
Sal Khan stood on a stage and told the world that Khanmigo would revolutionise learning. As Dan Meyer pointed out in our conversation, Khan has since admitted it did not do what they hoped.
Becky was not surprised. She has a 15-year-old daughter who finds maths difficult. Becky spends a lot of time watching her do homework on Sparx Maths. Her daughter’s go-to when stuck is to rewatch the video. She could open ChatGPT and ask for help. She does not – not because she thinks it would be wrong, but because she wants the shortest possible route to getting the questions done. And rewatching a two-minute video is currently shorter than typing a question into a chatbot.
This is the behavioural science at work, and it connects to everything Bibi Groot told me about the research on unconstrained AI tutors. The Turkish high school study. The chess study. Students who had access to AI help did worse on the final test because they had offloaded the cognitive work. Students who could request hints whenever they wanted could not stop themselves from doing so, even when they knew it was hurting their learning. Michael Pershan’s line from the Dan Meyer episode keeps coming back to me: any tutor that the tutee can control is not a tutor.
Becky’s conclusion is the same as the one we arrived at with Bibi and Adam Boxer, but she puts it in a different way: learning platforms have to be highly controlled. Students should have relatively little autonomy in how they navigate the app, because when you ask children what they want to do, they do not choose the most efficient and productive activities for learning. At Alpha, the model is highly constrained. The AI decides what the student works on, in what order, and for how long.
This, Becky said, was why Khanmigo was largely a dead end. It gave students a chatbot. Students had to choose to use it. Most did not, because there was always an easier alternative. The system has to control the learning pathway. There is no way around that.
4. Alpha School is a benign laboratory – and that is exactly what it should be
I asked Becky the question that I know is on many people’s minds when they hear about Alpha School: can you learn anything useful from a school that charges top-of-market tuition and enrols children of wealthy, highly motivated parents?
Her answer was more interesting than I expected. She agreed the students would probably be successful regardless of where they went to school. But she argued that is exactly why you should be building there. When you are working in an uncertain environment with rapidly changing technology, you should build in benign circumstances to figure out what works without worrying that you are damaging vulnerable children.
The critics are right that Alpha raises unanswered questions about what happens when you try this with different types of children, families, and less well-resourced schools. But Becky’s point is that you have to answer the “can this work at all?” question before you answer the “can this work for everyone?” question.
I asked if she would send her own kids. She said no – but not for the reasons I expected. Her children are 12 and 15 and thriving at a large comprehensive. Her daughter has found her gang, sings in two pop bands. You cannot do that in a school of 50 students. The smallness of Alpha is, for Becky, the weird part – not the screens, not the lack of traditional teachers, but having so few peers to find your niche among.
I asked what Alpha has got wrong. She said there is a gap between the two-hour morning learning block and the afternoon project-based activities. For students who love a subject, there is no space to go deeper – to learn about the history of maths, or how a scientific discovery was made, or to explore ideas beyond the core curriculum. She thinks they will figure it out, but it has not been figured out yet.
5. Mainstream schools will mostly stay the same – but marking will get solved and micro schools will appear at the margins
I asked Becky to make a prediction. Her answer surprised me, because after everything she had said about how optimistic she is about learning apps, I expected a bolder vision for how schools would change.
She does not think mainstream schools will change much. She wrote a whole book about this during the pandemic – The Next Big Thing in School Improvement with Matt Evans and Ben White – and her argument is that schools are complex systems that cannot simply adopt a new operational model overnight. You do not have the infrastructure, the buildings, the adults, or the political will to ship Alpha’s model into a local comprehensive.
What she does think will happen is that learning platforms for independent study will get much better. Marking will get solved – not eliminated, but reduced to a point where a teacher looks at a few scripts, the machine handles the rest, and the teacher gets a summary of whole-class feedback to deliver. Given that Teacher Tapp data shows overwhelmingly that teachers dislike marking, this alone would be a meaningful improvement in quality of life.
And at the margins, she sees micro-schools appearing. Small schools of 50 to 100 students, funded initially in the private sector and as alternative provision, offering a viable option for the 20% of students classified as persistently absent and the one and a half percent in elective home education. These are children for whom mainstream secondary school is not working – not because they are incapable, but because they cannot deal with the environment. Software-powered micro-schools could give them a structured education in a setting they can manage.
Becky also made a point I had not considered. At the moment, if a school shrinks below a certain size, it can no longer afford subject specialists in every area. But with learning apps, a student could be the only person in their school taking psychology or Further Maths and still receive a high-quality course. You end up with a mixed economy: the traditional school model for most students, supplemented by online provision for niche subjects where the numbers do not add up.
Adam Boxer predicted that AI would not revolutionise education because nothing ever does. Becky agrees – at least for existing mainstream schools. But she sees something different growing at the edges. Not a revolution. More like a slow, quiet expansion of what is possible for the students the current system cannot serve.
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 Becky’s Substack (Prof Becky Allen), her assessment-focused Substack with Matt Evans (100% Assessment), and of course Teacher Tapp if you are a teacher in England and not already on it.
This is the eighth in a series of conversations I am having with leading thinkers about AI in education. Here are the others in the series:
I would love to know: Does Becky’s vision of micro-schools appearing at the margins resonate with you? And do you agree that mainstream schools will mostly stay the same, or do you think the pressure will build faster than she expects?
Thanks so much for reading.
Craig
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