To suss out the best way to use AI in education, it’s important to figure out what makes ChatGPT and other new AI tools tick. After all, the ‘thinking’ that generative AI does is different than human thinking. That’s the argument of Mutlu Cukurova, who wrote a paper calling for building ‘hybrid intelligences’ that blends the strengths of humans and AI, rather than quickly replacing tasks that teachers and tutors do with AI models.
The viral research paper that started it all: “Attention Is All You Need.”
Mutlu Cukurova's paper discussed in this episode, “The Interplay of Learning Analytics and Artificial Intelligence in Education: A Vision for Hybrid Intelligence.”
Brian Christensen's “The Most Human Human.”
Apple Intelligence ad discussed in this episode: Writing Tools.
Jeff Young:
There aren't many blockbuster academic papers. In fact, the average scholarly article is read by about 10 people. But the latest revolution in AI can be traced to a research paper that was published in 2017 that saw the kind of viewer numbers that you'd expect from a Hollywood blockbuster. And this paper, written by eight scientists from Google, didn't just get read. It quickly got cited in research papers all around the world, meaning others were building on this paper's new ideas.
Mutlu Cukurova:
In the history of scholarly publication, it is one of the most-cited papers ever. It currently has about 140,000 citations — academic citations.
Jeff Young:
Yep, 140,000 other published papers have worked from this one novel argument, and it's all pretty technical.
Mutlu Cukurova:
Generative Pre-trained Transformer architectures, what we are calling in the field as GPTs.
Jeff Young:
So if you have ever wondered why ChatGPT is called that — what those letters GPT stand for — it goes back to this idea in this one viral scholarly paper, which actually had a pretty catchy title for a work of scholarship. It's called “Attention Is All You Need.” The working title before that was a little bit more what you'd expect: “Transformers: Iterative Self-Attention and Processing for Various Tasks.”
Mutlu Cukurova:
It's essentially telling us that you need to pay attention to all the tokens or words that come before and that are likely to come after a particular word, in order to be able to make the best sense of this word. So pay attention to the surroundings of each word to be accurately predicted. And there is a really beautiful mathematical equation behind these attention mechanisms, but there is really not much point in getting into details of this. What really matters here is that these attention mechanisms are bringing in a significant amount of advancements to what we are calling in the field, the multi-layer perceptrons of these neural networks, and they have been working very well in recent years.
Jeff Young:
That's Mutlu Cukurova, a professor of learning and artificial intelligence at University College London. I'll admit I tripped over his last name when I first got on a Zoom call with
him.
Mutlu Cukurova:
It is read as cuckoo - rova, which sounds a bit like crazy cuckoo, but oh well.
Jeff Young:
Mutlu is one of the many scholars who has cited this paper, “Attention Is All You Need,” and his focus is how to best use these new generative, pre-trained transformer models in education. He's citing it because he's concerned that in a rush to try out chatGPT and other new AI models, educators might be missing something crucial about what's going on in these GPTs.
Mutlu Cukurova:
I think a lot of my colleagues approach these kinds of questions from a very pragmatic point of view about, you know, ‘These tools are out here, how do we make sense of them? How do we use them? How do we utilize them in educational contexts?’
But in order to be able to appropriately and fundamentally address these kinds of questions, we need to take a step back to be able to identify the extent to which these tools are differing from existing, traditional information and communication technologies.
Jeff Young:
There's never before been a technology dropped into educational settings quite like these GPTs. Basically, teachers are putting AI tools in the classroom that even the programmers who designed them don't know exactly what they're going to do or say next, which is kind of astounding when you think about how much effort has traditionally gone into things like textbook standards in K-12 schools and the careful editing on college textbook editions to make sure things are accurate and effective for as many students as possible.
So Mutlu argues that when we talk about AI in education, we need to be clear on how this new, so-called intelligence works — and how the way AI thinks is different from how humans think.
In fact, he has a paper of his own with a proposal of how to better pair GPTs and humans in the classroom.
Welcome to Learning Curve, a new podcast that looks at what it means to teach and learn in the age of generative AI. I'm your host, Jeff Young. I'm an independent journalist exploring the future of education. This is the first episode of Learning Curve, so first here’s just a bit about what the plan is.
I have been covering the intersection of education and technology for a long time. I started back when it was the internet that was the shiny new technology that colleges and schools were wrestling with. Most recently I hosted the EdSurge Podcast, which was about the future of learning. I produced more than 300 episodes of that over seven years. So that was quite a run. This new podcast continues that tradition, but diving deep into AI and what this fast-moving technology could mean for education. The goal is to inform a conversation about what it means to learn and teach in the shadow of GPTs, and to sort out what learning needs protecting in this AI Gold Rush. Because generative AI and all of the GPTs inspired by that viral research paper, they raise profound questions about things like what it even means to be intelligent. And there are also practical questions like, ‘How do you measure what a student knows when a chatbot can do the homework or write an essay for them in largely untraceable ways?’
Plus, there is the broader question of what schools and colleges should do differently as AI changes the world of work and many other aspects of daily life.
Each episode is going to ground these bigger questions in a specific situation playing out at a college or a high school, or explore an argument in some new book or a new scholarly paper.
These days, I am out on my own as an independent producer and freelancer. So I'm going to be asking for listener support to keep this new show going. One easy and free way to support the show is just to follow this podcast on whatever app you are listening to right now. If you do have a minute, a review would also be huge help. I know all kinds of podcasts say follow the show, but this really does help the algorithms weigh whether people care. So it's a big deal. You can also become a paying member of the show and get bonus episodes that go deeper into the topics that I'm exploring. You can find out more about that on learningcurve.fm.
I'm excited to be back on the microphone talking with you, and to be interviewing folks shaping education in this time of change — and let's face it, a lot of upheaval. I do want to point out that I did this interview that you're about to hear way back in January. So and this is saying it lightly, there's been so much going on since then in education and in AI. But this conversation is really exploring big picture issues. And I feel like the conversation that you're about to hear is even more important with all the turmoil going on in higher education these days, and I think there are just more people tuning in and trying to get a handle on what AI is and how it should be thought of in terms of education.
Okay, so back to generative, pre-trained transformer architectures, or GPTs. As anyone who has followed my work over the years knows, I'm super interested in the narratives and metaphors that we use when thinking about educational tools. And that is something that was clearly on the mind of my guest this week, Mutlu as well.
I first discovered the scholar's work based on a super interesting paper that he published last summer. It's called, “The Interplay of Learning Analytics and Artificial Intelligence in Education: A Vision for Hybrid Intelligence.” In that paper, he proposes a way to productively think about blending AI and human intelligence when it comes to education. So I was excited to start our conversation there.
First off, it seems like you are thinking through how we think about the way AI fits into learning matters, like the way we conceptualize it. Is that fair to say?
Mutlu Cukurova:
Absolutely, yeah. I mean, it's a really interesting question, isn't it? And maybe in order to be able to give a brief introduction, it would be useful to start by saying a couple of sentences about the essence of these large language models.
Jeff Young:
I was going to ask you, yeah. So in other words, it's important to understand how GPTs ‘think’ — how the language models operate. I feel like people are game for a refresher, even if they're up on AI.
Mutlu Cukurova:
Indeed. I mean, I guess it would be even appropriate to start by saying that they don't really ‘think’ in the way that we think as humans.
Jeff Young:
Yeah, I put ‘think’ in air quotes, but people can't see me on a podcast. I said think, but that's even anthropomorphizing them.
Mutlu Cukurova:
Exactly. As with many of the historic attempts at talking about intelligence, we really struggle to talk about intelligence in any other way than we talk about human intelligence. But actually, artificial intelligence is a very different kind of intelligence.
So one big thing about these GPT models is they're constantly guessing the next best word or phrase or token to use based on this algorithm. But there's more to why they work than just that.
On top of these GPTs and all the kinds of transformer architectures, OpenAI kind of companies have recently also discovered the value of using human feedback in the continuous training of AI algorithms. So on top of these pre-trained models, ChatGPT kind of tools are using an approach called reinforcement learning with human feedback.
Jeff Young:
Reinforcement learning with human feedback?
Mutlu Cukurova:
Exactly. So this is an approach where you would present a particular prediction to people and ask people if they like this particular prediction, or present them treatment predictions and tell them, Well, would you prefer this response or this response?
Jeff Young:
So they're checking it against a human
Mutlu Cukurova:
Exactly. Sometimes, when you get these OpenAI ChatGPT responses, it actually proposes you two options, and then it asks you to choose. So when you choose, it actually takes this feedback into the reinforcement learning models account. So next time you ask, it improves the performance of the model accordingly.
They also use this kind of reinforcement learning with human feedback approaches to be able to patch the existing biases or challenges or limitations of these models. For instance, there are many people working in these companies, usually recruited through third parties, and their entire job is what is called ‘red teaming.’ So they are pushing the model to make mistakes or towards more gender insensitive issues or towards more racially insensitive issues. So you push the model, so that whenever it makes mistakes, you can provide human feedback that ‘No, this is a racist response,’ so that this can be [removed] in the long term.
Jeff Young:
That seems you know that approach. I definitely have heard what you're talking about, this red teaming model. It does feel impossible to get everything, though, right? I mean, it's an interesting approach. It seems like it could get so that any average user would not hit upon horrible, racist or sexist or whatever material. But how could you get it all?
Mutlu Cukurova:
Yeah, you can't. I mean, basically you can't guarantee that this will never be the case, but the scale at the moment of these companies is literally in the 1,000s of people working on these kind of tasks. So you can really improve the model performance significantly.
Jeff Young:
So you’re saying there are humans out there, clicking, clicking, clicking, right now? Is that what you're telling me?
Mutlu Cukurova:
Yeah, exactly. Actually, this is a continuous training, so this is after the model building, you do this continuous training of reinforcement learning with human feedback. So you see progress. And once you feel like you reach a comfortable level of accuracy — a comfortable level of bias — you feel like, ‘Okay, I can cope with this amount of bias,’ then they are released into the world.
So the point that I usually make when I talk about these kinds of AI techniques is that this is really impressive from a technical point of view, technology. I mean, we made very significant progress. But in essence, this is problem solving without really understanding the problems. It's a designed kind of intelligence.
So you tell these models that these are the kind of things that you should be predicting, and then you’re trying to minimize the loss function of these predictions. The models do not really have a genuine understanding of the problems or predictions that they are solving and they are generating. It's a designed intelligence to optimize towards particular solutions that we as humans think are appropriate solutions or appropriate outcomes. And because these models have no fundamental understanding of the solutions that they're proposing, they might also be proposing many solutions that are not well aligned with human values, or they might be proposing solutions that have significant societal and ethical implications, and they wouldn't necessarily consider these kinds of aspects in the proposed solutions. It's a designed intelligence. It's very impressive, extremely powerful, but it's a designed intelligence.
But when we talk about human intelligence, that’s a very different kind of intelligence, because human intelligence is an emergent intelligence. It is the product of millions of years of evolutionary pruning that leads towards a very flexible intelligence that also has significant limitations and vulnerabilities that are embedded into the evolutionary process.
Jeff Young:
What's a good example of one of those limitations? Just to make sure I understand.
Mutlu Cukurova:
Of course. I mean, I can give at least a couple of really straightforward examples. One of them is the fact that we have episodic memories, because humans have very limited bandwidth in information processing compared to these technologies.
Human bandwidth of information processing is very limited. Therefore, we have evolved into developing memories that do not necessarily record everything in a particular order, but tries to remember particular specific moments that we consider as important. For instance, let’s say you go out for a coffee in the morning. If I asked you, ‘Well, do you remember the fourth male person that you saw on your way to grab a cup of coffee?,’ you would really struggle to remember exactly who this person was. But if I asked you, ‘How was your experience of going for a coffee in the morning?’ You would remember certain things. Maybe you would remember that you saw an attractive woman on the way. Maybe you would remember that you didn't have change with you, and you used your card. You would remember certain moments, but you are not really recording the entire experience. Whereas with an AI — or information communication technologies in general — because bandwidth is not an issue, you can actually process all the information, like everything, like details exactly in detail.
Or there are even simpler examples. Like, you can ask people, ‘Do you remember the alphabet in an order?’ And they would be easily regurgitating this information to the A, B, C, D, E, F, G, you can even sing the song about it. But if you ask people, ‘Can you actually tell the alphabet backwards?,’ Many people would struggle, even though, from an information point of view, from the theory of information, it's exactly the same amount of information.
Jeff Young:
We take it for granted the way we think and we think about intelligence as human and scholars say the human brain turns out to have tons of advantages for the world that we humans live in, but the scientists out there building computing tools, they don't have to follow the same approach as our biological brains do, which, after all, have their downsides, like, I just can't quickly say the alphabet backwards right now. Like, I just can't quickly say the alphabet backwards right now, Z, Y, X, W, I don't really have the right song pattern for it. I can't
do it easily.
Mutlu Cukurova:
We have certain limitations and vulnerabilities of our way of thinking that are the direct product of our evolutionary processes. And there are five significant phases in most of the educational neuroscience literature about these evolutionary processes that each one of them has significant advancements. If you're interested in I can tell you about these five different phases as well, but the big picture point here is that these evolutionary breakthroughs and processes have led us humans to develop a kind of intelligence that is an emergent intelligence of the product of evolution, whereas with AI, we are designing intelligence, and they have very different kinds of intelligence.
That is not to say that they are more intelligent than us. And I'm really sorry to say it, but we are not really more intelligent than AI.
It's just different kinds of intelligence.
So there are many things that humans can do much better than AI, and many other things that AI can do much better than humans. Which brings us towards this idea of, ‘How do we create complementarity, which is my area of interest.
Jeff Young:
So the researchers building these new AI tools are essentially building a new kind of intelligence that could possibly complement our human intelligence.
Maybe just give me a flavor of those five phases of intelligence that you did just for without spending too much time. But what are the bullet points on that?
Mutlu Cukurova:
Sure. In the literature, usually, colleagues talk about five groundbreaking advancements. There is a bit of a debate about the extent to which these could be considered or how clear cut these are. But in human intelligence, in biological intelligence, the first breakthrough of biological organisms is considered as this idea of being able to make sense of stimuli around biological organisms, to be able to categorize them as good stimuli and bad stimuli, so that we can turn towards good stimuli and avoid bad stimuli. And that itself has enabled organisms to navigate their environments by categorizing these stimuli into two different categories.
And then this was followed in evolutionary chronological order by this capability of reinforcing, which is this idea that you repeat particular behaviors that lead towards the positive outcomes, and you avoid those behaviors that lead to negative outcomes. This repetition and reinforcing of positive outcomes is the second major breakthrough because it allows people, it allows biological organisms, to learn through trial and error, essentially, where the world is training us. And all animals have these kinds of capabilities of trial and error, all biological organisms.
And then the third major leap, perhaps, can be considered as this idea of simulating reality, and this is only allowed by neocortex development. So not all biological organisms have this one. So mammals have neocortex developments that allow them to simulate reality. So rather than acting upon their stimuli, mammals have the capability of simulating and thinking about what would have happened if I had acted on a particular stimuli. So without acting upon this stimuli, you can actually make certain decisions and learn through simulating reality. This is extremely important, because mammals actually can do this, to be able to learn counter factually, you know all of this thinking about, ‘If I could have done this, I would have done that.’ That’s an extremely important breakthrough itself. And there are fantastic experiments, for instance, with mice and how mice can think about avoiding smaller amounts of food, in particular experiments, if they know that this avoidance can help them reach out to bigger rewards or bigger amounts of food, because this way of thinking that, ‘Oh, if I avoid this small food, I will be rewarded with a bigger amount of food. That equires you to think and simulate a future reality that you have not really acted upon, and that requires neocortex development.
Jeff Young:
I don't think we usually think about our environment as training our brain, but that is essentially what has happened over the roughly 300,000 years of humans on the planet.
Mutlu Cukurova:
And then there comes the mentalizing, or this ability of modeling one's own or other people's mind when they are making certain decisions. For instance, when you saw me, when you heard my accent, when you looked at my credentials, you had a model of me in your mind. Right when I joined this discussion, you started asking me questions based on this model. You don't just say the same thing to everyone. Because I came here with a model of you as well as a model of what you might be thinking about me. So there's like a meta model there, but this capability of what is called in the literature, ‘theory of mind’ is a very specific feature of primates because that allows primates to learn through observations.
So you can see chimpanzees, for instance, observing their parents on how to use a particular tool. Not all animals can do this because it requires a significant amount of a theory of mind.
And then final development is this capability of speaking. So the idea that you do not necessarily need to reinvent everything as an individual, but you build upon the generations of intelligent humans who have come up with various ideas and written this down, and then you can read these. So I didn't need to reinvent that gravity is a mathematical foundation because it was already discovered when I started studying. And that's only possible because we have language so we can transfer ideas.
So these five major breakthroughs actually lead towards a particular type of intelligence that is unique to biological intelligence. And I don't really think that we can ever reach the same level of intelligence with artificial products because to me, for an AI to be able to reach this level of intelligence, the AI itself has to go through the same irreducible steps of evolution.
It is not only about the strengths of this intelligence. It's also about the weaknesses of this intelligence. And it's not really an ideal thing for an AI to do.
Jeff Young:
It's interesting, and let me just sum up what you just said, which is fascinating. If we somehow would simulate and do an AI that was trained evolutionarily, the way biological brains have evolved, we would end up with something — maybe it's not even theoretical possible, but if we did it — then it would not be able to do the alphabet backwards very well anymore, whereas right now, the built AI has some strengths that it can do that we cannot do because of the way our brains were trained. Is that fair?
Mutlu Cukurova:
Exactly, exactly my point. And why would we need to do this anyway? There are, like, 8 billion people already with significant intelligence out there. Why would we want to create yet another intelligence that is the same as ours. Like, I don't really see the point
Jeff Young:
That's so interesting. Yeah, if we were gonna clone a human brain, then for what?
I'm sure people probably have thoughts. I mean isn't this the history of robots? That people have wanted servants to help us, or something. But why would we when there are humans that we can work together with. It's a big question.
Mutlu Cukurova:
Actually, look, I understand from a scientific investigation point of view, because we are all fascinated with our own intelligence. So trying to build something similar does make sense from a scientific investigation point of view. But oftentimes, if you actually listen to the rhetoric of why people would like to build something like this, it boils down to the ideas of people. What they would be doing if they had the power over another human being, and that's kind of what they are trying to achieve, right?
And these are not always very pleasant or good for society, these kind of acts. They are often power dynamics of pushing one's own ideology over another one. This has been happening in the past in various communities, and I think to a certain extent this is what is happening. So the big, big point that I would like to perhaps make here is that AI being different than human intelligence is not necessarily a bad thing, and it might actually be a good thing, that it is different than human intelligence.
Jeff Young:
There’s also this work about AI replacing jobs. Well, if we build it so that it is exactly like the human brain, it seems inevitable that it would replace humans in jobs, whereas what you're saying is, if we design, from the get-go, an AI that is meant to be a sort of amplifier for the human mind, then these can't be these are less likely to just put people out of work and are more likely to have amplifying trends. Is that fair?
Mutlu Cukurova:
That is fair, and frankly, this job replacement discussion is an quite interesting discussion as well, because oftentimes, these kind of fundamental differences between AI and human intelligence essentially cause significant implications on what kind of jobs and tasks can be replaced with AI as well.
For instance, oftentimes, if you look at the history of automation, those tasks that were immediately replaced with industrial revolution, or automation in general, are the kind of tasks that are so mechanized in the way that they are implemented, that whether a human does it or an AI does it, should not really make a huge amount of difference. So let me clarify this a little bit more. Maybe it wasn't very clear. So maybe we can think about the call center operators, right? If you look at the job of a traditional call center worker, they are often people with a very clear set of scripts, a very clear set of responses to certain types of inputs. They very rarely go out of their particular structured responses. So they are extremely mechanistic jobs, and at some point they become so mechanistic that they are very easy to be replaced with AI.
Jeff Young:
Brian Christensen has a fascinating book about this. I think it's called “The Most Human Human.”
Mutlu Cukurova:
It is. I think everybody out there should read “The Most Human Human.” It’s a fascinating book about this topic, and he talks about this pre-automation phase of certain jobs where the job is extremely mechanistic, and it's actually inhumane not to replace it with AI. And these are the kinds of things that we can very quickly and easily replace with AI, and this replacement is probably a good thing.
But there are also certain other kinds of jobs that are automated with AI that actually comes from the reconceptualization of particular jobs, and these are very tricky to predict and mitigate the impacts of.
A very good example of this, I think, is that when sometimes people talk about the replacement, they talk about this cars versus horses analogy. It's a very simple analogy, but it's very powerful. So just as cars are not direct replacement of horses, and cars are not really faster horses, AIs are not really smarter humans. I don't really know if that makes sense, but they are not really smarter of the same category of a thing like, like human intelligence. Which actually means that when you think about cars and their introduction to the society, you do not really think about a replacement per se, but you think about reconceptualization of some of these constructs. Like, What is distance? What is a journey? Now, how much is far and how much is close for a human being?
So all of these re conceptualizations require certain professions to be rethought.
Jeff Young:
I once did a vacation to Chicago and went to the Museum of Science and Industry. And one of my favorite things I learned or saw was the room with the prototypes or like, the early examples when things were moving from horse drawn carriage to cars. And you could see them, and some of the ideas for early cars were kind of crazy to us now, I think, because they're so tied to the carriage idea, and they kind of transform as they go. I'll have to put up some pictures on the show notes. Like, they had cranks and areas to sit on top in case you hooked up a horse. I think there was even a regulation at some point where you still had to have them have the ability to have a horse hook up to it, even when they were running on their own, because it's a carriage. So I think that's what you're talking about. Like, eventually we evolved to a car, and it changed a lot of things about transportation. And the way we think about getting around.
Mutlu Cukurova:
Exactly, and the way that we think about human connection as well. So this kind of reconceptualization is more or less what is needed in a lot of the AI applications in educational contexts or human development contexts, rather than thinking about what we have initially started in your question, replacing particular tasks of teachers or educators
or tutors or anything, right? It's a way to, I guess, accelerate this very much needed rethinking in our education systems.
Jeff Young:
So okay, let me just rephrase it again to I make sure I follow. So in other words, in some of my coverage, in a lot of press, there's this idea of, ‘Will AI be an a personal assistant?’ That's a job that we know what it is. It's a human job. Will it just do that? Or will it be a tutor? Will it be a, you know, like, there's these literal metaphors, and in your mind, those metaphors are not helpful, as helpful as something where it's like, Wait a minute. What if you had a way to think about it as there's a new thing to do, but if you have this new tool and the human brain together you shouldn’t just think of it as an assistant or a tutor, is that what you’re saying?
Mutlu Cukurova:
I think it's very limiting to think about it that way. It’s limiting for their capabilities, but it's also limiting for what we can achieve with AI if we only think about them through these analogies of assistants or as tutors. This conceptualization of them having a very different kind of intelligence essentially has significant implications that are associated with this very much needed rethinking about what is the purpose of education, what is the purpose of human learning, and how it can be reconceptualized to a certain extent to be able to make the most out of these technologies as part of a human-AI, tightly coupled system.
Jeff Young:
I've been watching the Marvel superhero movies in timeline order with my wife and kids. And it feels like we’re in one of those plots. Like, the world has gotten this new alien technology, and now we have this challenge to figure out how it can be harnessed or used for things that are going to be positive and not destructive. I don't want to be too flip about this at all, but it does feel like a big moment here.
Mutlu Cukurova:
Yeah, it is indeed, although, you know, it's actually fascinating, because a lot of these kinds of reconceptualizations and rethinking about education systems, it takes time and effort, and we need to digest a lot of these developments and technical advancements that have been happening. And usually the easiest way is to think about any kind of replacement, because it's so much faster if you can just replace, like, a plug and play kind of activity, well.
And the internet was a history of that.There were things with the internet where it was like, we'll have e-commerce, and we'll just take the thing we've done and we'll put it on the internet. And some, in some cases, that kind of work, but look how much time it took to actually properly realize this.
I think at the moment, unfortunately, with a lot of AI in education implementations, at least, we are rushing towards particular task replacements that are not necessarily very valuable for education. We have a lot of work with regards to what kind of tasks we could potentially automate — like fully automate — with the help of AI. And there are very few tasks of a human teacher that can be replaced, and oftentimes, these are the kind of tasks that do not necessarily require human intelligence.
For instance, we have been experimenting a lot with AI generated synthetic media, because a lot of the pedagogical implementations these days require what we are calling the flipped classrooms. You know, recording a particular lecture and putting a video in advance of a classroom so that people watch it, and they come to the classroom knowing it.
Jeff Young:
Yes flipped classrooms, where you assign the lecture video as the homework, and then instructors use class time for other things that are more interactive, yeah.
Mutlu Cukurova:
And this is a very annoying task. I don't know if you have ever done this, but as a professor I have been doing similar pedagogical implementations, and for a one-hour lecture or 20 minutes lecture recording, you might spend five hours [making it]. It's annoying to make it. It's very difficult to make these kinds of things. And for a lot of the companies, large scale companies, for their employee trainings, these kinds of materials are very difficult to produce, because you need to pay a professional video creator to go to interview the expert and create all of these huge time- and resource-intensive activities. So we have been implementing AI generated synthetic media, looking at if the same content is delivered by me recording my video in front of a screen or a human expert recording the video, versus the same content delivered with AI-generated synthetic media. And in our experimental results, we have shown that there is no statistically significant difference in terms of the learning gains, which means that you can actually learn the same amount, both in terms of memory, recall and recognition, which are two major dependent variables in learning sciences hat you would measure. So this is the kind of task to me that could potentially be fully automated with AI. And I have no problem with this.
Jeff Young:
So it's the production part of it, yeah?
Mutlu Cukurova:
Exactly. Or, you know, some kind of material generation. So this kind of information retrieval tasks of a teacher, maybe more and more educators will not need to create materials, particular materials for which they would have to pull information from various sources and synthesize it together. These kinds of tasks I can see full automation to potentially work. But a lot of the other tasks are extremely difficult to be fully automated with AI. So we need to think about alternative conceptualizations.
Jeff Young:
I'm going to cut in because as I listen back, I really wish I had stopped Mutlu here and pressed him a little bit more. Because, hold on a second. If I think about the implications of what he's suggesting, he's essentially saying that the type of work that he is fine to automate, to replace with this new kind of AI intelligence of GPTs, is actually the kind of work that I do. It's the work I'm doing right now, in some ways. It's being the voice delivering information to an audience.
In his case, the audience is students, and so he's saying he's fine with having a bot make an educational video for him that his students can watch before class, so he can spend class time discussing that material, which then he knows the students have learned through the video. So I guess in his scenario, the professor would be the one curating the material, doing the expert analysis and even crafting the message. So maybe it's not a full replacement for what I'm doing here, but still, this seems like giving up a big part of what makes a professor a professor. It’s the standing before students, imparting hard-won expertise, in this case, in a video.
But I guess this isn't the part of the job that Mutlu personally sees as invigorating or key, at least not delivering intro level or rudimentary knowledge. So, if I understand him correctly. It's that part of his job that he's okay to try GPT for, and then he can correct and make sure that the synthetic media, as he called it, that the bot makes is accurate. And so for him, a good use of what he calls hybrid intelligence is to have the bot do some video presenting and video editing tasks that he considers a little tedious, and that leaves him more bandwidth to do what he enjoys and what seems to him more human.
I've been talking to educators about AI a lot this summer, both for this podcast and for some freelance articles that I'm doing for the Chronicle of Higher Education, and I want to share a pattern that I'm noticing. It's been a pretty consistent pattern, so much so that I've even started to jokingly call it a ‘law of generative AI. use.’ let's call it The Learning Curve law of AI.
Here is the pattern, and see if you think this is familiar.
For any given person, the part of your job that you like the least and consider kind of boring, that's the part you're okay with passing off to a bot, even if the bot is flawed and takes some work on your part to correct.
But here is the challenge, not every professor agrees about which part of the job is tedious and boring. For some professors and teachers that I've met, making lecture videos is exciting. It's a place for playfulness and a way to connect with their students, rather than something that’s kind of boring. But maybe those professors hate grading papers, and so they would give that up in a heartbeat to GPT, especially for rudimentary assignments.
The result is that even among professors who say that AI should not be used for everything in education, there is disagreement over what this new kind of intelligence can appropriately be used for.
And I suspect this might be true even more broadly. After all, AI seems to be able to automate so many language-based tasks that people can't agree yet on which of those tasks are so human that they need to be protected, and which tasks are just kind of boring and tedious.
Anyway, that's my take. And I did press Mutlu a little bit on this, and he offered more about how this different way of thinking that AI does could help human teachers in new ways beyond just doing tasks for them.
I asked him, What is a way in which AI, these large language models, as you described them, could amplify intelligence and be thought of conceptually as like a partner with a human brain?
Mutlu Cukurova:
Well, I mean, that's a million dollar question, isn't it, Jeff? Usually, when I talk about these conceptualizations, I tend to give many examples of attempts from the literature with regards to this replacement, and talk about the limitations and challenges of it. I talk about our traditional AI techniques that are driven by rule-based systems, or more-transparent AI techniques, or literally straightforward learning analytics approaches, where you would build a computational model of a particular learning behavior and then present these models back to the learners or back to teachers, and expect them to change their representation of thought about what good looks like in a particular situation. For instance, you might create a transparent model of students’ verbal and non-verbal interactions in a small group, and then generate visualizations of a computational model that shows who follows whom in the discussion, how much each individual is speaking and contributing. And then generate these kinds of visualizations computational models as a visualization to be internalized by humans, and then you expect the humans to change their representation of thought about what they should be doing, to raise their awareness of their own actions and improve the accountability.
Jeff Young:
That's almost like training tape for a professional athlete, for instance, right?
Mutlu Cukurova:
That's a really nice analogy, actually. Of course, not all learning is performance based, so you wouldn't necessarily focus only on very observable behaviors, but you can generate some observable behavior performance indicators as a conversation opener between a human teacher and a human learner, because then they can start talking about this and interpret it. This kind of reflective thinking about computational models and performance measures that comes through computational approaches are extremely powerful.
But I'm still struggling to see strong empirical evidence of us interacting with AI models and it leading us humans to become more intelligent after the interaction, when the AI is not in the scene anymore.
In other words, there's a huge difference between amplification and augmentation. In my definition of augmentation, you can do so many things with the help of AI much better than you would be able to do without AI. For instance, a lot of my students are able to code much better faster with the help of an AI model.
Jeff Young:
I see, you mean these copilot type things to help you make computer programs?
Mutlu Cukurova:
Exactly. Staff cannot code as well as most of my students can. Most of my students cannot code as efficiently as they can with the help of AI, so they can improve their capability. But my question is, Can my students become better coders without AI after their interaction with AI? And I don't really see many examples of that so far.
So that's the kind of empirical question as well. And my worry is that even though they might be augmenting their capabilities when they are working with AI through this interaction, maybe they are actually losing certain competencies.
It’s what I call in my papers cognitive atrophy, because you rely too much upon the AI model and its existing next to you that you might be offloading certain tasks to AI that in the long term, you might be losing your critical competencies of being able to complete these tasks without the AI.
Have you seen this advertisement of Apple Intelligence? I saw this one-minute advertisement for Apple Intelligence, and it actually makes my point very clearly. Have you seen this? It’s the one where this is a bored employee playing around with the office objects around him and playing around with his seat and various pens, and then he writes a very sloppy email that actually doesn't really make sense. He uses weird words that don’t really mean anything these words. And then uses Apple Intelligence to convert this text of an email to make it sound very professional to be sent to his boss.
Jeff Young:
I tracked down that ad, and you probably have already seen it. The boss appears blown away by this email that the AI wrote for the inept co worker. You really have to see it visually for the full effect.
Mutlu Cukurova:
So then the boss reads this email and thinks that, oh, my god, Warren, the name of this employee, he writes a fascinating email to be able to complete a very difficult task. So essentially, the employee deceives the boss to essentially be allocated to a task that he would not be able to complete on his own.
To me, that's a very worrying future. At the end, the Apple Intelligence slogan says ‘I am genius.’ And a song, comes in, and the guy pretends that he's a genius, but he's not. He's an ignorant person who cannot pull together five words to write a meaningful sentence in an email to his boss.
I don't want my students — our future — to end up in a situation where they cannot synthesize information from two sentences to write one email to express their ideas. I want them to be able to do this regardless of their use of AI or not. So this is not a future that I am looking forward to. But it looks like some of these big companies are pushing this idea that if AI can do it, maybe we don't need to. Well, hang on a second. What are we losing here, if we are, if we are offloading our agency to these technologies?
So that's that's one of my huge worries about the future, that we might end up as humans losing our own cognitive competence, assuming that AI will be able to do these kind of tasks for us.
Jeff Young:
And then the minute AI shuts off, we're useless
Mutlu Cukurova:
That's one, but even if it doesn't shut off, then, then maybe you can think about the fact that human knowledge and competence is a holistic type of construct. Like what we were saying earlier, that our vulnerabilities and strengths are hugely intertwined. So losing our capability of, let's say writing a particular email or using our language capabilities in different contexts, might actually have a significant impact on our other capabilities that we do not even realize yet.
Jeff Young:
You mean that using AI might evolve the human brain differently?
Mutlu Cukurova:
It might. It's like these architectures where you are pulling a significant pillar out, and you don't really know what would be the long term implications of this pillar not being there on our cognitive architecture.
Jeff Young:
Like a load-bearing column in a buildings?
Mutlu Cukurova:
Exactly. And language and writing capability might well be one of these, one of these load-bearing columns.
We don't know, because there is no empirical evidence. And I'm not saying this is exactly the case. All I'm saying is that without evidence of impact, why are we rushing so fast?
Jeff Young:
What is your recommendation? Are we rushing too fast? And is this potentially to our detriment as an is, you know, with education and beyond?
Mutlu Cukurova:
I think so, particularly if you think about the commercial interests in this space, I think all of these changes that we have just discussed should be human-centered — prioritizing human intelligence and human competencies should be evidence based. That should be driven by research studies and particularly showing the impact — the positive or negative impact — of a particular AI intervention before we scale these.
And they should also be a product of strong stakeholder negotiations. It's not the decision of me as an academic professor at UCL, not the decision of X company who produces these products, and not a decision of an individual teacher, but a community-level stakeholder discussion should take place before we make a decision with regards to the extent to which these technologies should be integrated in education for replacement.
Jeff Young:
Yeah, this is big stuff.
Mutlu Cukurova:
Sorry it sounded a bit pessimistic, but I hope is a wake-up call for all of us.
Jeff Young:
I guess that's the question: How do we manage this period of change?
Mutlu Cukurova:
Indeed. And one thing that I am quite keen for people to think about is that most of the commercial tools, as they are available out there, are very likely to be particularly designed and developed for commercial interest and user satisfaction. They might not really be very useful for human learning, because human learning actually requires people to take a step back, do the reflections. It's difficult. You know, it requires a lot more cognitive load.
Jeff Young:
It's exhausting to learn because it's pushing you.
Mutlu Cukurova:
Exactly. Like when we have AI models that we build in our own practice, for instance, with regards to essay writing support, these models would never give my students direct responses to particular questions. They would never draft content. They would make them think about, oh, that's an interesting topic. Have you thought about this kind of thing? Have you thought about this evidence? Have you read this paper so you can actually rank like retrieval, augmented generation and integration into these models to bring in irrelevant literature as well?
And my students think that this is a lot more difficult to interact with than ChatGPT, because if you write in ChatGPT, ‘I want to write an essay on X, ‘it would write half of the essay for you, if not all of it.
Jeff Young:
It's back to that Apple Intelligence guy just goofing off and putting in nonsense.
Mutlu Cukurova:
Yeah, exactly, exactly. And I don't want my students to be there, Jeff.
Jeff Young:
I hope we talk again. I'm so glad we had this conversation. Thank you for joining our podcast today.
Mutlu Cukurova:
It's been a pleasure. Lovely meeting you. Jeff.
Jeff Young:
I have had lots of time to think about this interview since I talked to Mutlu, and it has really stuck with me. Because it turns out, many of the issues that he raises in his paper and in this conversation, they have become hot topics over the summer, especially as more and more AI models are released and AI companies are making more of their products available free to students, or building them into tools that professors are already using.
With things moving this fast, it's worth stepping back to look at whether the bigger framework for using AI in education is really the most productive for students and for education.
What are the best ways to think about this new AI, this new kind of intelligence?
It seems like a really good first step would be to get a better handle on what exactly this new intelligence is and how to best use it to not replace brains, but complement them.
I would love to know what you think. Please send me some feedback or your thoughts to Jeff at learningcurve.fm. That's Jeff at learningcurve.fm. I might include some of the responses that I get in a future episode. So please let me know if you are willing to be named or if you'd rather be anonymous.
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