Hello, everyone, and welcome to the third event in our public seminar series this term.
My name's Rebecca Owen and I'm an associate professor here at the department.
And I co-convener the New Technologies Research Group with Professor Winters.
But before I introduce our speaker today and I've just got to do a little bit of housekeeping.
So if the fire alarm does go off, then it won't be a drill.
So please leave by the mark exits and the gathering point is at the car park and 34 gardens.
Okay, so with this housekeeping out of the way, I'm really delighted to introduce our Dr. Sanjay Nathan Grey and Saunders,
an associate professor in education at the Department of Curriculum,
Pedagogy and Assessment University College London, and her research focuses on the social,
ethical issues surrounding contemporary identity, biometrics, A.I. and algorithms.
So within this domain, Sandra has written numerous articles and books, including most recently and physically blighted the deterioration of childhood
and also curriculum reform in European schools towards a 21st century vision.
Sandra is a member of the Privacy Expert Group of the Biometrics Institute.
She's a senior member at Wolfson College at the University of Cambridge,
and she's on the advisory boards and the Digital Fund Me, which is a child privacy advocacy organisation.
What's also very exciting about sound,
just like she's also chair of the relatively new phones Artificial and Human Intelligence Special Interest Group,
which is part of the British Education Research Association. And this special interest group is really exciting,
and I really do encourage you all to have a look at it and take part in it and see all of the events that they've got coming up.
So within the Learning, a new technologies research group here at the department,
we really try and apply a critical stance towards the use of technology in learning and education,
regardless of life, stage, context or indeed, technology.
And Sandra really shares that commitment in her work and the combination of her teaching, background and application.
Psychological theory, I think, makes her work really important, and I'm particularly exciting.
So given this, I was just so excited when she accepted our invitation to come and talk with us and tell us about her work and so on.
Now and every soldier who talk about A.I. and social relations in schools.
So thank you so much, Sanjay. Thank you. I'm really, really delighted to be here.
It's a really busy time in the AI and education world because all of a sudden people are seeing the light a little bit.
So until now, there's been a lot of technological development,
but there's been less of an emphasis on things like the ethics and philosophy that underpins the use of AI in an educational context.
And so suddenly, a lot of things have converged at once, so we have industry being more interested in pursuing that.
We certainly have academia looking to do more of that kind of work.
And so it's it's a sort of very fortunate constellation and a very good time, I think,
to start asking ourselves some really tough questions about what's going on and what kind of future we want A.I. and education to give us.
So that's going to be the thrust of what I'm talking about today.
So I'm going to talk in a general sense about I'm going to do a sort of intellectual mapping of the types
of AI that are out there and I'm going to try and relate them to different functions in education.
And if there's any technologists in the room, you'll find a lot of flaws in this because of course, the terms that we use overlap so much for A.I.
So it's a rather artificial division,
but it's a kind of taste of taste of the smorgasbord of delights that artificial intelligence and education can offer,
and whilst also allowing us to start thinking, OK.
So if it means that in terms of social relations, what do we have to do to make sure that we are steering it and it is not leading us?
And so that's the sort of main sort of framework of what I'm trying to talk about.
And then I'll theorise a little bit more such think about some of the big
sociological questions about the human condition in the light of these categories.
So the main point of trying to think about artificial artificial intelligence is trying to decide what
we're doing right by today's children because we have a huge responsibility to the next generation.
And for most of us in this room, I imagine it's we get up in the morning, we don't get up in the morning to think about how to make their lives worse.
We get up in the morning to try to think about how to make everybody's lives better and for
people to have a more enriching and fulfilled future for society to be a better place.
So social relations is a really good way of using that as a lens for understanding how this can happen and the reason that we need to do it,
particularly for artificial intelligence, is because no technology can ever be properly neutral.
Everything has got a cult of either a cognitive bias or a set of preconceptions or a social bias or a social context for
its use and every development that we have in terms of technology has significant repercussions for the future of society.
And I mean that in various contexts. So the political, economic and cultural aspects of society.
And one of the things that we perhaps don't talk about enough in relation to this is the idea of trust.
Now, trust is starting to become a more frequently used term in in tech industry developments and so on,
and it's something that I'd like us to talk about a lot more.
And we're talking about mutual trust here amongst different stakeholders in the educational process.
So we're talking about not just developers, we're not talking about necessarily just clients,
but also pupils, teachers, parents and everybody who's got a vested interest.
Every stakeholder that's got a vested interest, which is pretty much all of us.
But sometimes that word trust gets really lost in technological translation, so it can end up meaning something fairly bland and prosaic.
So whether I've given you a 36 page version of the terms and conditions document that you're supposed to have read and then you take, you've read it.
And do you trust me that in that document that you're only really pretending to have read that I won't do anything nefarious with your data?
It also means the idea of operating technological systems reliably and transparently and with everybody's best interests at the centre,
so it being about more than the sustainability or growth of a business and individual business.
And that's something I'll talk about later as well. And Boyden Crawford in their work, described this as a form of empowerment.
This idea that collectively there's a sort of collective human endeavour and that within that individual actors or people have
the you ought to describe them have have got some kind of ability to influence outcomes and events and direction and so on.
And to think about technology like this is very different from thinking about
technology solely in terms of making things cheaper or easier for other people.
So one of the things I'm very rude about at work, for example, is anonymized master's dissertations because it seems to me that, you know,
we've gone from a system where somebody gave me a nice pre bounce thing that I read and marked very nice,
tidy people have been doing that for hundreds of years. Suddenly, I downloaded the assignment.
I printed the assignment because I can't mark that much on screen. I found the assignment.
I then had to sort of pretend I didn't know who'd written it, even though I'd seen them once a month for the whole academic year.
And then I have to upload the marks and it's what I call platform writers.
So somebody had the great idea that putting all of this paperless was going to make it the world a better place.
But now the students are anxious about whether it's been uploaded, and I'm anxious about whether I've done all the uploading.
And you can multiply this by the dozen or so platforms I encounter in daily work, the sort of shadow work aspect of of being a university lecturer.
And the problem with this sort of thing and I think we're in the very early days of this kind of technology.
So somebody said this would be a good idea, but there hasn't been any kind of conversation about what really is involved in the job and what really is
involved in being a student and what we're all trying to do and whether it makes life difficult for people,
for example, if they have a visual impairment or muscular problems and they can't spend a long time looking at computers and so on.
And it may be that something that we think is very fair,
like having a platform where it doesn't know necessarily which students submitted what apart from from a digit string.
We don't know whether that really is fair,
because if it's making life difficult for particular groups and we haven't tracked that or asked ourselves that question,
then perhaps we haven't done our job properly enough in looking beyond just adopting the first technology
that comes to light that we think is exciting purely on efficiency or intellectual augmentation grounds.
So another example might be perhaps tracking students in particular ways or trying to offer courses in new ways and so on,
or for doing university governance or business governance and so on.
And so we end up with a situation where there's a lot going on,
but we're not giving ourselves the time to think through what it means of the societal level to be doing these things.
So I'm going to talk quite a lot about that in a minute.
So I suppose this this talk is really a manifesto to kind of urge people to think about adopting the best
characteristics of humanity when thinking about new technologies in the broadest possible societal benefit,
rather than just thinking about commercial interests.
And I suppose this would particularly relate to things like surveillance technologies in schools in particular.
But that sounds very negative, and this is actually really about having an enormous moment of opportunity.
So we're finding that for the first time,
machines have got an immense power to influence learning and teaching and everything that goes on in ways that operate sort of many,
many orders of brilliance better than even the sort of most enormous group of human minds.
So the human capability of the operators has been completely superseded because the scale of it is just so enormous,
particularly when we're talking about very large scale datasets sets.
So we no longer have to work on a hypothesis based approach to research with testing our ideas.
For example, the machine could perhaps find it for us. That's quite scary for a researcher.
At what point do they replace me with a robot?
And a lot of people have talked about the data fixation of education in this sense and the idea that, you know,
you've got this huge scale of operations and it doesn't matter how much effort the human species would put into trying to replicate that.
All that mind power can't hold all of that data, and it's the first time in history that's really happened.
You know, until now, we could manually do a lot of operations if we chose to, which is very expensive and time consuming.
It needs a lot of people with which surpassed that now. We surpassed that quite a while ago.
And so we can't replicate, so they're on their own. So we have no systems that are interrogating each other,
and that brings new questions about whether they are deciding direction for things different from what we would decide.
And this is why the human is so important in the middle of this. So no longer are machines just responding to a set of instructions that we give them.
They're thinking about their own instructions. And that's the essence of A.I. and this is a scaling up of data collection and analysis.
Absolutely unprecedented. It's particularly piloted on a lot of children and young people, which is why it's of particular concern to educators.
It's relatively uncontrolled and unregulated, and a lot of us are trying to do things about that.
And the commercial sense sector is at the centre of this development, not the university and not the the kind of altruistic, a societal wing of that.
There's a difference there. So we have a commercial thrust and we have a commercial push towards monopolisation in particular.
And again, that's something that I'll be talking about in a minute.
So we have real big impacts on privacy because of this and the humans are being taken out of the equation.
You have massive implications for child and teacher relations and also the idea that you have
a cohesive national or international education system for this being compromised by this.
This is a profound change that I can't overemphasise. And so that's a key theoretical focus of what I'm talking about today.
So what I thought I would do now when I've got rid of whatever this is,
is to talk a little bit about the sort of basics of A.I. and just take through.
I've tried to formulate a framework that is simplistic in some senses,
but gives us ways of thinking about different categories in an educational context.
Now I'm starting with this very interesting press release extract from Pierce.
Now Pearson are pushing really, really hard to be the monopoly provider in education.
So as it says, unlike other sectors, education is yet to fully realise the benefits of digital and advanced A.I. techniques,
and there are great opportunities to improve learning outcomes and to enable better teaching.
Well, that's the sort of invoking of a sort of rhetoric of virtue.
In a sense, person is committing to transforming the learning experience and becoming the digital winner in education.
So there's obviously only one winner. So that says Monopoly.
So I'm going to try and play a film clip manner of people saying the BBC programme years and years.
Yeah, it's quite scary, isn't it, so I just want to remind you of a little got a little clip here?
I just wanted to know what the money is likely to catch.
You can tell it is a movie. You know, not for a long time.
You know, been thinking since I was born that I don't belong.
So it's really a great time. And reading up on it and.
You can just see her.
OK, so I guess I'm a little sweaty for my the way we look at the smoking bill, it's going to be confusing for someone to make a mess of it sometimes.
But we give you ice cream.
You always will. I mean, we don't need to rush to go off the time to even talk about this.
And if it turns out that we've got some, it's exorbitant and we'll be happy.
No one more conceptual is look now for some differences.
What do you mean? I'm what transsexual? I'm trans human.
Oh, OK. I'm sorry to keep changing your words in sex.
No, I said, I'm not comfortable with my body, so I want to get this thing with the arms and legs every single bit of it.
I don't want to be flesh. I'm really sorry, but I'm going to sleep this thing and I'm going to do.
What do you mean? They say one thing that happens when you go and you pick your brain into the cloud
and opposing you snack into the air so you could live forever as information,
because that's what trans humans are more male or female.
That's why the innocent life or death is only fatal.
I will be later. She acts it so beautifully, doesn't she?
So. You know, we're at a time of sort of major change, really.
And the question is, I mean, for me, that's a really interesting example because you've got the Snapchat filter,
that's virtual reality and you've got so many layers that people are not really understanding what's going on,
not understanding about social change and so on. So I'm going to break this down now into a really sort of basic set of definitions.
So the first thing I wanted to talk about was the idea of predictive analysis.
Now, if every bit of A.I. is going to have a degree of predictive analysis in it,
so it's an artificial division, but I've called this the digital school bursar.
So it's the idea that you have something going on behind the scenes, assessing things like admissions and finance and resourcing and so on.
And it's a very general term. It dates back to the good touring probability estimate.
So we have touring decoding messages in World War Two by assessing the likelihood of whether a word is what you think it's going to be.
And then it was later refined in the sixties by good to allow for incomplete data sets.
So that's the maths of it. And so what it allows us to do is to predict future events by working out probabilistic statistical calculations.
So where do we see it used at the moment? Well, it's happening a lot, even if you're not aware.
So it is in the background of a lot of financial products that you'll be receiving or using, and it's also used a lot in the insurance sector.
So if you phone up for car insurance, it's in the background working out what you're going to have to pay.
Through various sets of algorithms, and it also will be on your phone,
perhaps when it's predicting likes or making recommendations for things that you might want to see on social media or online purchases and so on,
and the sets of algorithms in the background there as well. It's part of your autocomplete function on your phone,
and you can see how well that works from when you send a text and you've put entirely the wrong word in it,
which is always amusing and things like crime and health trend prediction very prominent there in education,
which is what we're all here to think about, is says things like the likelihood of young people dropping out of courses,
whether they'll succeed on different academic programmes and things like that.
And it's early days in that respect. Now that might seem a very early worthy way of doing things because you don't want to
have young people going on the wrong course or having to drop out and getting upset.
You want human misery to you. But on the other hand, we say John King John.
So it depends on you how you've configured the algorithm, which determines how successful it's going to be at doing what you want it to do.
So if you make a mistake in that, it might start screening out the wrong kinds of people,
which is exactly what happened with organisations such as Google using it for human resources functions where it was saying,
Well, this this person is female and most developers are male.
Therefore, if it's a female applying for a developer job, it's not likely to match what we think is a good developer.
So you get these inadvertent forms of discrimination and that could happen in education, too.
So this we call adverse selection, which is a technical term from the insurance industry,
where deprived of vulnerable groups are deliberately rejected from a group through automated decision making.
So another example of this in education is the intelligent zoning engine,
which they've used since 2017 in Berlin district of Tempelhof shown a bag where they've used it to German school admissions and catchment areas.
And what the algorithms try to do is look at time and travel distance and determine which is the most efficient way of getting kids to school,
which is the least use of resource. And this being Germany, of course, they would want it very efficient.
I can say that some half German, so they're very, you know, doing something that sounds very worthwhile, very good guarding resources and so on.
But the risk of a system like this is that it might inadvertently entrench disadvantage
because you're putting you're putting children's educational futures at stake,
depending on what they happen to live next to. So if you're in an area of poor social housing and you'll always be at school with children who attend,
who live in poor social housing, and if you're in an affluent suburb,
you'll always be at school with kids from the affluent suburb and there's no mixing,
which you could say is not very helpful if you're trying to design the perfect system.
So it's kind of scuppering other aspects of education policy where mixing is thought to be a bit more desirable.
And another example might be the open university. So they have a system called you analyse,
and they track students through the university to see which students are likely to need extra toward intervention.
So that sounds a very clever thing to do, particularly on a large scale basis like the Open University would need to do.
And this is fine. You know, they're very pleased with it and it seems to be working very well, but it's trained on an atypical.
What we see is that a typical university population, so if you see that, you know, in a typical university,
you might have half the students being full time undergraduates, for example, who have left school a year or two previously.
If it's trained on mature part time students, the big mistake would be to take that saying Oh,
that works brilliantly in university and then say, Come and use it at UCL, where I work, where the population is completely different.
So you get so many false referrals and you'd be missing a lot of people that are having problems because you've tried it on the wrong population.
So it's really important to try to to match that as closely as possible that it's likely that if you did buy a product like that,
you'd have to retrain it completely.
And we know this because there's been work done in, for example, screening in child protection cases where there's notable accuracy problems,
where it's been done on the wrong population, so it becomes racially biased or assumptions are made of stone.
So that's absolutely key to doing these things well. So the next excitement in artificial intelligence is the idea of deep learning.
Another word that we bandy about a lot that is sometimes vague in its use.
Colonel, that was the last slide. And I've labelled this is the digital music writing room, so this allows for sort of creative acts.
And I find this quite exciting, actually.
So the algorithm itself will keep looping to calculate higher order features, yet high level features, so it will try to, for example,
number two faces and then it might look at what an AI is and might look at different kinds of face it tries to refine itself over time.
So it has a lot of layers to it, moving from pixels to a precise image.
And sometimes it's quite good at that.
So you can try on the internet and see if you're trying to find something like an image that you've already got, and it can be quite accurate.
Or sometimes it isn't. So I tried this recently with a cutlery set and it came came up with images of two,
and they look very similar because there's lots of things in rows in a box, but it got confused with those, too.
So it's quite hard for computers to learn these things, but they can do it and it becomes more precise over time.
So it works in some certain circumstances, but just as with the toolkits and the cutlery boxes, it can be problematic.
So they're often biased towards white men because they work and sometimes Asian men, because they tend to be the developers that the testing is.
And so it's a bit like the learning problem from the Open University, you know,
if you were to test it on one type of face, it's going to struggle with, for example, black faces.
So there's been a lot of scandal in the news about, you know,
Google images and also some sometimes pass passport by biometric recognition systems getting this very wrong.
So people have tried to upload a photograph of themselves to renew their passport, and they're told the eyes are closed because they can't.
You do very surprised, but this was a quite shocking story that came out recently.
And so they're very flawed algorithms and people are quite rightly objecting to this.
So that's the downside.
But the upside is that you can have rudimentary art and music works where they look at the algorithm would look at thousands and thousands of,
say, Van Gogh and similar type pictures and then try and create one of its own.
And that's where it gets really good. So you can't see it very well, but that's sort of a I artwork in the back of that slide.
And it's also worked with music and also literature, and you can use it for things like facial surveillance as well.
So I didn't find school uniform in a crowd, which is a bit nerve wracking.
But then it could also design at the perfect school uniform if it had enough of them to look at.
So what can we do with it in education where we could look at it as a way of encouraging new
forms of curriculum interaction so we could have pupils developing artworks using this,
which I think might be an interest in curriculum development?
You can have things like spatial augmented reality in schools as a project called Future Gems that use with autistic children who are
non-verbal to try to get them to interact with the surroundings so something might appear there and I might interact with it and so on.
And you know, there's that kind of option and you've also got I think I've got a quote here.
Yes. So it's tried to write a novel. I always try to write a novel.
So that's from the first I novel won the road.
It was nine 17 in the morning and the house was heavy, which I think is a brilliant sentence, which I want to read the.
And so you can imagine some kind of literary stimulation for pupils work and so on.
So in that sense, I think it's a nice convergence of science and art and humanities and all sorts of other
things and performance art that could be a very fertile ground for for students at school.
So then we have the digital school inspector. So machine learning, and again, I must emphasise all these terms overlap, they're not perfect.
So that's a term developed by Samuel, and that's the idea.
It was first used to play an online version of the board game checkers.
And so we see that use in different contexts now. And the idea that you have algorithms and statistical models sensing pattern recognition,
allowing for decisions to be made with little human intervention, they're getting awfully good at their so.
So we have our alphabet go. We have this fancy chess functions and that kind of thing quite intimidating.
So how do we use it in education? Well, we have a school inspection.
And so they do things like mapping attainment trends to identify schools in need of inspection, to save a human, having to go through all of the data.
But the problem with this is that it assumes the data are neutral and that everything is kind of equally weighted.
And of course, that's a really major problem. So having been a school governor, you know, we used to tear our hair out if we'd had a bout of illness,
a sort of epidemic of illness just before the SATs exams and the results were death.
And that would make us look very bad. But the danger with a system like this is that it would then trigger some sort of inspection.
What happened was the kids got ill. It didn't mean anything about the way teaching and learning was happening in the schools.
Just bad luck. So because this work would work quite quickly in the background and offset are using techniques like this,
you know, you might be wasting resources really through false positives just because of blips that happen.
The consequence is it a consequence or a subsequent is it a correlation or causation?
So there's there's different questions that need to be asked.
So unless you're questioning the intrinsic value and analysis of data really thoroughly, then I think it's of limited use.
So the next film we have is the idea of a neural network, which I call the digital prefect,
because it was starting to get a little bit hard to think of all the educational terms,
but it's the idea that you have a computer system emulating a biological system like the human brain.
We get very muddled in our thinking about human brains and computers, and we often try to think of human brains in a computational sense.
And computers, we try to say, Oh, well, we need to build a computer that can act like a human brain.
And they're very, very different things. So I try and sort of think of a divide like wet and dry intelligence that different intelligences.
The group we've set up for the British Educational Research Association is
artificial and human intelligence because we don't think one emulates another,
necessarily a separate beings. But when you're trying to build the pattern of one, you have the idea of nodes or artificial neurones,
and they're taught to recognise whether something is true or false by a system of algorithms.
So the system can then train independently over time to do this better and more frequently,
a bit like the facial recognition system already talked about. So you could recognise individual pupils in crowds or on social media images.
And indeed, this Chinese social credit system uses this quite a lot.
But in practise, the system would need enormous numbers of samples to do this very well.
So it's not realistic in all but the most extreme circumstances when you've got enormous datasets.
We know that sometimes schools are gamed by individuals because this has happened in the Chinese social credit system,
and we know that there's also the potential of significant privacy violations because people don't necessarily
know that they're being tracked if you're collecting a big enough dataset to do anything useful with it.
So that's a problem. Another problem is inferring things like attentiveness from video streams or sort of live observation of classroom activities.
So what I could do if I was mean, I could make you aware.
One of the brain KO headsets have been in the news.
And then if any of you weren't paying full attention to my talk, then I could make a note for later.
You see, I could get the system to tell your mum and dad if this was a school that you hadn't
been attending in class and then if you didn't do very well and a little more like,
say, well, there were twenty six incidences where you weren't sitting upright and your your brain wasn't responding properly.
So I could do that and I could use emotion analysis to try to see whether you were being attentive.
But it would be a waste of time because as we all probably know,
it doesn't matter really what you look like when you're learning and people do this in different ways.
And the evidence is so weak for the idea that a particular kind of impedance level on a forehead is going to relate to some kind of deep learning.
Taking off in the back of beyond that will be meaningful in terms of the wider school experience so that the evidence is hopeless.
So it's a little bit of a tech led innovation and hence the combination of the doubts about its efficacy and the Chinese parents suspicion of this.
Now it's it's gone a bit too far, even for the relaxed Chinese parents to think that this might be useful.
So it's been withdrawn this week from the schools it was used in.
So that says quite a lot, really. So a lot of experimental work like that going on, but of of dubious potential, I think.
So there have been other attempts to use neural networks to look at just the data side of school and then to link that to data on school,
family and demographic variables to try to see whether you could reduce school dropout rates on this.
Places like Turkey superficial promise.
But again, it depends on what you're telling the algorithm to do, and you have to be very nervous about discrimination in accuracy.
So, you know, you have to ask whether certain categories are being conflated.
So ethnicity and social class, for example, whether they need to be divided.
So it's about, again, it's about the quality of the questions that you ask, determining how well each dataset can be deployed.
So that's just a summary. So the next one is the digital tutor, the expert system,
and that's something that we've probably will come across a lot because it's been around a long time.
This has been around since the 60s and that was developed by somebody called.
Feigenbaum is part of the Stanford Heuristic Programming Project.
It's a software programme that enables emulates human decision making processes by using databases to make expert decisions.
So it's very useful for online tutoring, for remediating and accelerating student knowledge.
And just generally the training context as well. However, not perfect because it's limited by the ability to update its own knowledge.
And if that isn't frequent enough, then you're not having a dynamic enough system,
whereas with a human being, they're often being inadvertently updated on a daily basis.
It's also culturally bound, so the knowledge needs embedding before you start using it, and then that's it.
So unless you're having a complete new version, you're stuck with what you've got.
We see it use quite effectively in many schools for for routine things.
So things like education perfect with their products like language, perfect mathematics, and they've now got something called spelling drome.
So it's really useful for things like drill and identifying where pupils are having particular problems with curriculum issues.
I think there are great ambitions to use it for bigger things.
So for example, you've had the whole old school movement in Silicon Valley where they tried to have a highly personalised curriculum using this
sort of resource to try to uniquely identify on a pupil by pupil basis what they needed to know next and to test things and so on.
And it was quite ineffective. And so a lot of the teachers ended up doing some, you know,
quite a reasonable amount of direct instruction to compensate for the shortcomings of that.
And indeed, the schools have started closing. The parents were losing confidence and so forth,
which raises the question about if you're having these higher level functions for
something like this with the need to for schools to buy expensive subscriptions.
You're talking about then less affluent schools not being able to afford these and perhaps being left behind digitally.
So that's of great concern.
The next one, I would have liked to have brought a robot, and I did have one to bring along, but apparently it was broken, so I couldn't.
So the social robotics, the digital classroom assistant.
So we're talking about something that can be used as a substitute for humans, it might not look like a human,
but something that can do this one particularly fascinating where this has been used in education.
I should do this with two year olds a very long time ago. It started in the 1960s, the idea of the logo programming language,
which then developed into little turtles that could break out of this and move around
the room and the kids do subversive things and they get under your feet kind of thing.
And they were meant to be really subversive. And there's been a fantastic paper on this about how we're supposed to disrupt
a relatively conservative curriculum framework for schools and save the world.
And that kind of thing in whether they did that or not is another question,
because people started to corral the knowledge and try to turn it into something that could be measured and so on.
So the anarchy left and with it, the creativity.
Perhaps so they're not around so much now, but we see robots and robotic knowledge in social robotics as a kind of revolutionary technical fix.
So, you know, in sci fi, you'd probably see it as a classroom assistant replacing a human.
Well, sometimes that can be a threat. And indeed, when the European Commission looked into this and interviewed lots of people
about what they thought and people were quite scared about robots in general,
I think it's too many Terminator movies myself.
So what would do we actually see what we see slow progress towards social robots through neuromorphic computing techniques,
so the idea of emulating the human brain similar to neural networking,
but they're likely to be quite limited to particular domains or particular tasks simply because the public resist them.
And one really interesting thing we found so there's a school in Cambridge that has just been set up a state comprehensive school,
but it decided just to be the most high tech school imaginable on the budget it had.
And one of the things they commissioned was a really nice kind of virtual reality cat.
So go to an open day and you look on the screen, you realise you're actually standing next to a cat that you can't see.
And it's all in 3D and it's all terribly exciting. So on and the cat does lots of interesting things.
It goes around the school and it pops up on computers around school, and all the kids have a laptop.
It pops up on the different platforms and the children's best to feed it and engage with it.
So if you're a bit bored in class, you can kind of virtually straight. The cat is very nice, except except this got a bit nasty, really.
So, you know, some of the teenagers started saying, Well, let's stop the cat and see what happens, what happens if we're means of the cat?
You know, how far can we go with the cat? And so people would say it was horrendous, and I'm thinking, that's cool being a teenager.
I mean, you kind of learning what the boundaries are, right?
And you can't really do this to a real life thing, but you could do it to a virtual world.
But then we get into the question of it's a bit like, do people have to be taught to say please and thank you to Siri?
You know, do we educate people to not be mean to the virtual reality cat?
What point do people start confusing the two?
So the some really big questions about having this kind of stuff flying around in schools and how we approach it,
it's got to be more than the gadget, whether it's potentially sort of cloud robotics or networked robots.
So you could imagine them being usefully used to sort of help with educational assessment in a routine way or reception, that kind of thing.
Maybe an internet of things where basic things are connected by it via embedded devices and they talk to each other.
On the other hand, this leads to a growth in individualised learning, attendance trackers, school environmental controls and so on.
Well, although currently personalisation is quite business driven, so we've sort of allowed it to happen,
but nobody's asking the question, you know, what do we actually want to happen?
So it's quite warm in here, isn't it? You know, so in an ideal world, you'd have some kind of internet of things social robotic thing going on,
and some little white thing would have slid over and adjusted the climate because it would have
known optimally what everybody needs to to be able to to really feel comfortable in the room,
for example. But, you know, it's not something I can order on Amazon yet, is it?
So this is a complicated set up. So.
Where we have to sort of think about the theory a little bit more is in relation to forms of power
here and things like globalisation and accountability towards learners and that kind of thing.
And see about the impact on the social relations generally,
so you have the scaling up of data to a massive degree, changing the nature of teacher professionalism.
So you have this mediated at several removes from the pupils, teachers and parents that are at the forefront of learning.
You have globalisation. Is these really big commercial players and supranational education organisations starting to develop in this area?
And I'm not sure what accountability is there towards learners, except in the most general sense.
And I certainly don't see much accountability towards individual learners.
So it's a greater good argument that these organisations are harnessing that perhaps a
little bit difficult to reconcile with the social relations at an individual level.
So we see forms of power shifting as the corporations and the super nationals become increasingly dominant and monopolised.
And the idea that, yeah, we're trying to have new forms of regulation, such as the GDPR regulation that came in last May.
But it's always running behind and perhaps needs to become stronger.
So if we want that to happen and if we want this to be a really positive future for artificial intelligence and education, what do we do?
Well, I've been looking back, said, look forward really when I've been doing work on this,
so I've been thinking about the idea of Burnstein and Pedagogic rights,
which is kind of to the end of his education, symbolic control and identity book.
And it's something that perhaps we haven't given much attention to for a while, but it certainly works here.
And he talks about the idea of education enhancement, participation and inclusion a very personal level.
And I think perhaps that's something that we can embed in our debates about the way
forward for artificial intelligence here as as a way of bringing the human back into it,
really.
And then I've been looking even further back, really at home and social exchange theory, and I've been writing about this in different contexts.
I've been trying to think about how do we know if an exchange is democratically equivalent or whether it's imbalanced?
And I've been using the terms authentic and inauthentic for this,
which are uncomfortable for a lot of technologists because that sounds a bit too much like a technical term.
But it's the idea of do people have a buy in?
Do you have a stakeholder engagement with different things that are happening or is this something that's done to people?
So if you say if you've got the stakeholder buy in, that's authentic. And if it's done to people, it's inauthentic.
What have we actually got? Monopoly situations growing up where people have very little agency, so there's really big questions to ask them.
So to conclude, I think you need to think more about forms of power, we need to think more about things like the nature of consent.
So if something is chosen by a school or institution,
do we have to ask questions about whether consent should be more individual and things like transparency of processing?
How much should providers have to tell us about how they use their algorithms begged them on their commercially sensitive?
This is a really thorny question in the GDPR.
They're required to do this, but they can argue that this would compromise their business model, so that's not very well worked out.
The idea that, you know, how far can pupils give their time and data for free so that these big business models can be built,
that all of these products that I've described today rely upon?
And what rights do people have towards privacy of data in that respect?
So it requires very, some very careful balancing tests that aren't happening at the moment because it's just a bit of a wild west of development.
We have a vast and ungainly educational landscape and a lot of things competing for our attention.
And meanwhile, everything is being refined and reimagined, but not necessarily with a lot of consultation,
even though the users are being rendered increasingly governable by these systems, by these very remote commercial systems.
So we need to think about the future of the society in which this is happening.
And if you think back to the Pearson quotes, there are great opportunities to improve learning outcomes and enable better teaching,
so nobody can really argue with that, transforming the learning experience.
But I would contest that we need to do this together and for it not to be a commercial project
of dominance and to keep enhancement participation and inclusion right at the centre of that.
So. It's this grey area between the authentic and the authentic and the personal and the commercial that the future needs to be mapped out.
And if we don't do this, then we've killed this thing at birth, really, because it'll never do what we want it to do,
and it will be so discriminatory on a social basis that it will cause more problems than it solves.
So this is why the pedagogic rights framework of Bernstein's particularly interesting as a
discipline for thinking about it and by promoting the involvement of users at all of these levels,
it allows us to really build a more robust future. And government needs to go beyond rhetorical flourishes about rights and so on and consent and
have a more solid regulatory position so that it really represents the things that need to happen.
And if we get this right, then it will be very exciting. If it gets it wrong, will have increased social fragmentation, which is very dangerous.
So we need to choose our path very wisely. Thank you.