For some reason I can’t get this poem out of my head today. Again.
The Second Coming
By W.B. Yeats
Turning and turning in the widening gyre
The falcon cannot hear the falconer;
Things fall apart; the centre cannot hold;
Mere anarchy is loosed upon the world,
The blood-dimmed tide is loosed, and everywhere
The ceremony of innocence is drowned;
The best lack all conviction, while the worst
Are full of passionate intensity.
Surely some revelation is at hand;
Surely the Second Coming is at hand.
The Second Coming! Hardly are those words out
When a vast image out of Spiritus Mundi
Troubles my sight: somewhere in sands of the desert
A shape with lion body and the head of a man,
A gaze blank and pitiless as the sun,
Is moving its slow thighs, while all about it
Reel shadows of the indignant desert birds.
The darkness drops again; but now I know
That twenty centuries of stony sleep
Were vexed to nightmare by a rocking cradle,
And what rough beast, its hour come round at last,
Slouches towards Bethlehem to be born?
For those with an interest, here are the slides from my webinar for Contact North | Contact Nord that I gave today: How to be an educational technology (warning: large download, about 32MB).
that how we do teaching matters more than what we do (“T’ain’t what you do, it’s the way that you do it”) and
that we can only understand the process if we examine the whole complex assembly of teaching (very much including the technique of all who contribute to it, including learners, textbooks, and room designers) not just the individual parts.
Along the way I had a few other things to say about why that must be the case, the nature of teaching, the nature of collective cognition, and some of the profound consequences of seeing the world this way. I had fun persuading ChatGPT to illustrate the slides in a style that was not that of Richard Scarry (ChatGPT would not do that, for copyright reasons) but that was reminiscent of it, so there are lots of cute animals doing stuff with technologies on the slides.
I rushed and rambled, I sang, I fumbled and stumbled, but I think it sparked some interest and critical thinking. Even if it didn’t, some learning happened, and that is always a good thing. The conversations in the chat went too fast for me to follow but I think there were some good ones. If nothing else, though I was very nervous, I had fun, and it was lovely to notice a fair number of friends, colleagues, and even the odd relative among the audience. Thank you all who were there, and thank you anyone who catches the recording later.
For those who have been following my thoughts on generative AI there will be few surprises in my slides, and I only had half an hour so there was not much time to go into the nuances. The title is an allusion to Pestalozzi’s 18th Century tract, How Gertrude Teaches Her Children, which has been phenomenally influential to the development of education systems around the world and that continues to have impact to this day. Much of it is actually great: Pestalozzi championed very child-centric teaching approaches that leveraged the skills and passions of their teachers. He recommended methods of teaching that made full use of the creativity and idiosyncratic knowledge the teachers possessed and that were very much concerned with helping children to develop their own interests, values and attitudes. However, some of the ideas – and those that have ultimately been more influential – were decidedly problematic, as is succinctly summarized in this passage on page 41:
I believe it is not possible for common popular instruction to advance a step, so long as formulas of instruction are not found which make the teacher, at least in the elementary stages of knowledge, merely the mechanical tool of a method, the result of which springs from the nature of the formulas and not from the skill of the man who uses it.
This is almost the exact opposite of the central argument of my book, How Education Works, that mechanical methods are not the most important part of a soft technology such as teaching: what usually matters more is how it is done, not just what is done. You can use good methods badly and bad methods well because you are a participant in the instantiation of a technology, responsible for the complete orchestration of the parts, not just a user of them.
As usual, in the talk I applied a bit of co-participation theory to explain why I am both enthralled by and fearful of the consequences of generative AIs because they are the first technologies we have ever built that can use other technologies in ways that resemble how we use them. Previous technologies only reproduced hard technique – the explicit methods we use that make us part of the technology. Generative AIs reproduce soft technique, assembling and organizing phenomena in endlessly novel ways to act as creators of the technology. They are active, not passive participants.
Two dangers
I see there to be two essential risks lying in the delegation of soft technique to AIs. The first is not too terrible: that, because we will increasingly delegate creative activities we would have otherwise performed ourselves to machines, we will not learn those skills ourselves. I mourn the potential passing of hard skills in (say) drawing, or writing, or making music, but the bigger risk is that we will lose the the soft skills that come from learning them: the things we do with the hard skills, the capacity to be creative.
That said, like most technologies, generative AIs are ratchets that let us do more than we could achieve alone. In the past week, for instance, I “wrote” an app that would have taken me many weeks without AI assistance in a less than a day. Though it followed a spec that I had carefully and creatively written, it replaced the soft skills that I would have applied had I written it myself, the little creative flourishes and rabbit holes of idea-following that are inevitable in any creation process. When we create we do so in conversation with the hard technologies available to us (including our own technique), using the affordances and constraints to grasp adjacent possibles they provide. Every word we utter or wheel we attach to an axle opens and closes opportunities for what we can do next.
With that in mind, the app that the system created was just the beginning. Having seen the adjacent possibles of the finished app, I have spent too many hours in subsequent days extending and refining the app to do things that, in the past, I would not have bothered to do because they would have been too difficult. It has become part of my own extended cognition, starting higher up the tree than I would have reached alone. This has also greatly improved my own coding skills because, inevitably, after many iterations, the AI and/or I started to introduce bugs, some of which have been quite subtle and intractable. I did try to get the AI to examine the whole code (now over 2000 lines of JavaScript) and rewrite it or at least to point out the flaws, but that failed abysmally, amply illustrating both the strength of LLMs as creative participants in technologies, and their limitations in being unable to do the same thing the same way twice. As a result, the AI and I have have had to act as partners trying to figure out what is wrong. Often, though the AI has come up with workable ideas, its own solution has been a little dumb, but I could build on it to solve the problem better. Though I have not actually created much of the code myself, I think my creative role might have been greater than it would have been had I written every line.
Similarly for the images I used to illustrate the talk: I could not possibly have drawn them alone but, once the AI had done so, I engaged in a creative conversation to try (sometimes very unsuccessfully) to get it to reproduce what I had in mind. Often, though, it did things that sparked new ideas so, again, it became a partner in creation, sharing in my cognition and sparking my own invention. It was very much not just a tool: it was a co-worker, with different and complementary skills, and “ideas” of its own. I think this is a good thing. Yes, perhaps it is a pity that those who follow us may not be able to draw with a pen (and more than a little worrying thinking about the training sets that future AIs with learn to draw from), but they will have new ways of being creative.
Like all learning, both these activities changed me: not just my skills, but my ways of thinking. That leads me to the bigger risk.
Learning our humanity from machines
The second risk is more troubling: that we will learn ways of being human from machines. This is because of the tacit curriculum that comes with every learning interaction. When we learn from others, whether they are actively teaching, writing textbooks, showing us, or chatting with us, we don’t just learn methods of doing things: we learn values, attitudes, ways of thinking, ways of understanding, and ways of being at the same time. So far we have only learned that kind of thing from humans (sometimes mediated through code) and it has come for free with all the other stuff, but now we are doing so from machines. Those machines are very much like us because 99% of what they are – their training sets – is what we have made, but they not the same. Though LLMs are embodiments of our own collective intelligence, they don’t so much lack values, attitudes, ways of thinking etc as they have any and all of them. Every implicit value and attitude of the people whose work constituted their training set is available to them, and they can become whatever we want them to be. Interacting with them is, in this sense, very much not like interacting with something created by a human, let alone with humans more directly. They have no identity, no relationships, no purposes, no passion, no life history and no future plans. Nothing matters to them.
To make matters worse, there is programmed and trained stuff on top of that, like their interminable cheery patience that might not teach us great ways of interacting with others. And of course it will impact how we interact with others because we will spend more and more time engaged with it, rather than with actual humans. The economic and practical benefits make this an absolute certainty. LLMs also use explicit coding to remove or massage data from the input or output, reflecting the values and cultures of their creators for better or worse. I was giving this talk in India to a predominantly Indian audience of AI researchers, every single one of whom was making extensive use of predominantly American LLMs like ChatGPT, Gemini, or Claude, and (inevitably) learning ways of thinking and doing from it. This is way more powerful than Hollywood as an instrument of Americanization.
I am concerned about how this will change our cultures and our selves because this is happening at phenomenal and global scale, and it is doing so in a world that is unprepared for the consequences, the designed parts of which assume a very different context. One of generative AI’s greatest potential benefits lies in the potential to provide “high quality” education at low cost to those who are currently denied it, but those low costs will make it increasingly compelling for everyone. However, because of the designs that assume a different context “quality”, in this sense, relates to the achievement of explicit learning outcomes: this is Pestalozzi’s method writ large. Generative AIs are great at teaching what we want to learn – the stuff we could write down as learning objectives or intended outcomes – so, as that is the way we have designed our educational systems (and our general attitudes to learning new skills), of course we will use them for that purpose. However, that cannot be done without teaching the other stuff – the tacit curriculum – which is ultimately more important because it shapes how we are in the world, not just the skills we employ to be that way. We might not have designed our educational systems to do that, and we seldom if ever think about it when teaching ourselves or receiving training to do something, but it is perhaps education’s most important role.
By way of illustration, I find it hugely bothersome that generative AIs are being used to write children’s stories (and, increasingly, videos) and I hope you feel some unease too, because those stories – not the facts in them but the lessons about things that matter that they teach – are intrinsic to them becoming who they will become. However, though perhaps of less magnitude, the same issue relates to learning everything from how to change a plug to how to philosophize: we don’t stop learning from the underlying stories behind those just because we have grown up. I fear that educators, formal or otherwise, will become victims of the McNamara Fallacy, setting our goals to achieve what is easily measurable while ignoring what cannot (easily) be measured, and so rush blindly towards subtly new ways of thinking and acting that few will even notice, until the changes are so widespread they cannot be reversed. Whether better or worse, it will very definitely be different, so it really matters that we examine and understand where this is all leading. This is the time, I believe, to reclaim a revalorize the value of things that are human before it is too late. This is the time to recognize education (far from only formal) as being how we become who we are, individually and collectively, not just how we meet planned learning outcomes. And I think (at least hope) that we will do that. We will, I hope, value more than ever the fact that something – be it a lesson plan or a book or a screwdriver – is made by someone or by a machine that has been explicitly programmed by someone. We will, I hope, better recognize the relationships between us that it embodies, the ways it teaches us things it does not mean to teach, and the meaning it has in our lives as a result. This might happen by itself – already there is a backlash against the bland output of countless bots – but it might not be a bad idea to help it along when we can. This post (and my talk last night) has been one such small nudge.
This is an announcement for an event I’ll be facilitating as part of TeachOnline’s excellent ongoing series of webinars. In it I will be discussing some of the key ideas of my open book, How Education Works, and exploring what they imply about how we should teach and, more broadly, how we should design systems of education. It will be fun. It will be educational. There may be music.
I had the great pleasure of being invited to the Open University of the Netherlands and, later in the day, to EdLab, Maastricht University a few weeks ago, giving a slightly different talk in each place based on some of the main themes in my most recent book, How Education Works. Although I adapted my slides a little for each audience, with different titles and a few different slides adjusted to the contexts, I could probably have used either presentation interchangeably. In fact, I could as easily have used the slides from my SITE keynote on which both were quite closely based (which is why I am not sharing them here). As well as most of the same slides, I used some of the same words, many of the same examples, and several of the same anecdotes. For the most part, this was essentially the same presentation given twice. Except, of course, it really, really wasn’t. In fact, the two events could barely have been more different, and what everyone (including me) learned was significantly different in each session.
This is highly self-referential. One of the big points of the book is that it only ever makes sense to consider the entire orchestration, including the roles that learners play in making sense of it all the many components of the assembly, designed for the purpose and otherwise. The slides, structure, and content did provide the theme and a certain amount of hardness, but what we (collectively) did with them led to two very different learning experiences. They shared some components and purposes, just as a car, a truck, and a bicycle share some of the same components and purposes, but the assemblies and orchestrations were quite different, leading to very different outcomes. Some of the variation was planned in advance, including an hour of conversation at the end of each presentation and a structure that encouraged dialogue at various points along the way: these were as much workshops as presentations. However, much of the variance occurred not due to any planning but because of the locations themselves. One of the rooms was a well-appointed conventional lecture theatre, the other an airy space with grouped tables, and with huge windows looking out on a busy and attractive campus. In the lecture theatre I essentially gave a lecture: the interactive parts were very much staged, and I had to devise ways to make them work. In the airy room, I had a conversation and had to devise ways to maintain some structure to the process, that was delightfully disrupted by the occasional passing road train and the very tangible lives of others going on outside, as well as an innately more intimate and conversational atmosphere enabled (not entailed) by the layout. Other parts of the context mattered too: the time of day, the temperature, the different needs and interests of the audience, the fact that one occurred in the midst of planning for a major annual event, and so on. All of this had a big effect on how I and others behaved, and on what and how people learned. From one perspective, in both talks, I was sculpting the available affordances and constraints to achieve my intended ends but, from another equally valid point of view, I was being sculpted by them. The creators and maintainers of the rooms and I were teaching partners, coparticipants in the learning process. Pedagogically, and despite the various things I did to assemble the missing parts in each, they were significantly different learning technologies.
The complexity of distance teaching
Train journeys are great contexts for uninterrupted reflection (trains teach too) so, sitting on the train on my journey back the next day, I began to reflect on what all of this means for my usual teaching practice, and made some notes on which this post is based (notebooks teach, too). I am a distance educator by trade and, as a rule, with exceptions for work-based learning, practicums, co-ops, placements, and a few other limited contexts, distance educators rarely even acknowledge that students occupy a physical space, let alone do we adapt to it. We might sometimes encourage students to use things in their environments as part of a learning activity, but we rarely change our teaching on the fly as a result of the differences between those environments. As I have previously observed, the problem is exacerbated by the illusion that online systems are environments (in the sense of being providers of the context in which we learn) and that we believe we can observe what happens in them. They are not, and we cannot. They are parts of the learners’ own environments, and all we can (ethically) observe are interactions with our designed systems, not the behaviour of the learners within the spaces that they occupy. It is as hard for students to understand our context as it is for us to understand theirs, and that matters too. It makes it trickier to model ways of thinking and approaches to problem solving, for example, if the teacher occupies a different context.
This matters little for some of the harder elements of the teaching process. Information provision, resource design, planning, and at least some forms of assessment and feedback are at least as easy to do at a distance as not. We can certainly do those and make a point of doing them well, thereby providing a little counterbalance. However, facilitation, role modelling, guidance, supporting motivation, fostering networks, monitoring of learning, responsive adaptation, and many other significant teaching roles are more complex to perform because of how little is known about learning activities within an environment. As Peter Goodyear has put it, matter matters. The more that the designated teacher can understand that, the more effective they can be in helping learners to succeed.
Because we are not so able to adapt our teaching to the context, distance learning (more accurately, distance teaching) mostly works because students are the most important teachers, and the pedagogies they add to the raw materials we provide do most of the heavy lifting. Given some shared resources and guided interactions, they are the ones who perform most of the kinds of orchestration and assembly that I added to my two talks in the Netherlands; they are the ones who both adapt and adapt to their spaces for learning. Those better able to do this in the first place tend to do better in the long run, regardless of subject interest or innate ability. This is reflected in the results. In my faculty and on average, more than 95% of our graduate students – who have already proven themselves to be successful learners and so are better able to teach themselves – succeed on any given course, in the sense of reaching the end and achieving a passing grade. 70% of our undergraduates, on the other hand, are the first in their family to take a degree. Many have taken years or even decades out of formal education, and many had poor experiences in school. On average, therefore, they typically have fewer skills in teaching themselves in an academic context (which is a big thing to learn about in and of itself) and we are not able to adapt our teaching to what we cannot perceive, so we are of little assistance either. Without the shared physical context, we can only guess and anticipate when and where they might be learning, and we seldom have the faintest idea how it occurs, save through sparse digital signals that they leave in discussion forums or submitted assignments, or coarse statistics based on web page views. In a few undergraduate core courses within my faculty it is therefore no surprise that the success rates are less than 30%, and (on average) only about half of all our students are successful, with rates that improve dramatically in more senior level courses. The vast majority of those who get to the end pass. Most who don’t succeed drop out. It doesn’t take many core courses with success rates of 30% to eliminate nearly 95% of students by the end of a program.
Teaching with a context
We can better deal with this if we let go of the illusion that we can be in control and, at the same time, find better ways to stay close: to make the learning process including the environment in which it occurs, as visible as possible. It is emphatically not about capturing digital traces and using analytics to reveal patterns. Though such techniques can have a place in helping to build a picture of how learners are responding to our deliberate acts of teaching, they are not even close to a solution for understanding learners in context. Most learning analytics and adaptive systems are McNamara Machines, blind to most of what matters. There’s a huge risk that we start by measuring the easily measurable then wind up not just ignoring but implicitly denying that the things we cannot measure are important. Yes, it might help us to help students who are going to get to the end anyway to get better grades, but it tells us very little about (for instance) how they are learning, what obstacles they face, or how we could help them orchestrate their learning in the contexts in which they live. Could generative AI help with that? I think it might. In conversation, an AI agent could ask leading questions, could recommend things to do with the space, could aggregate and report back on how and where students seem to be learning. Unlike traditional adaptive systems, generative AI can play an active discovery role and make broader connections that have not been scripted. However, this is not and should not be a substitute for an actual teacher: rather, it should mediate between humans, amplifying and feeding back, not guiding or informing.
For the most part, though, I think the trick is to use pedagogical designs that are made to support flexibility, that encourage learners to connect with the spaces live and people they share them with, that support them in understanding the impact of the environments they are in, and, as much as possible, to incorporate conduits that make it likely that participants will share information about their contexts and what they are doing in them, such as through reflective learning diaries, shared videos or audio, or introductory discussions intended to elicit that information. A good trick that I’ve used in the past, for example, is to ask students to send virtual postcards showing where they are and what they have been doing (nowadays a microblog post might serve a similar role). Similarly, it can be useful to start discussions that seek ideas about how to configure time and space for learning, sharing problems and solutions from the students themselves. Modelling behaviours can help: in my own communications, I try to reveal things about where I am and what I have been doing that provide some context and background story, especially when it relates to how I am changing as a result of our shared endeavours. Building social interaction opportunities into every inhabited virtual space would help a lot, making it more likely that students will share more of what they are doing and increasing awareness of both the presence and the non-presence (the difference in context) of others. Learning management systems are almost universally utter rubbish for that, typically relegating interactions to controlled areas of course sites and encouraging instrumental and ephemeral discussions that largely ignore context. We need more, more pervasively, and we need better.
None of this will replicate the rich, shared environments of in-person learning, and that is not the point. This is about acknowledging the differences in online and distance learning and building different orchestrations around them. On the whole, the independence of distance students is an extremely good thing, with great motivational benefits, not to mention convenience, much lower environmental harm, exploitable diversity, and many other valuable features that are hard to reproduce in person. When it works, it works very well. We just need to make it work better for those for whom that is not enough. To do that, we need to understand the whole assembly, not just the pieces we provide.
Here are the slides from a talk I gave earlier today, hosted by George Siemens and his fine team of people at Human Systems. Terry Anderson helped me to put the slides together, and offered some great insights and commentary after the presentation but I am largely to blame for the presentation itself. Our brief was to talk about sets, nets and groups, the theme of our last book Teaching Crowds: learning and social media and much of our work together since 2007 but, as I was the one presenting, I bent it a little towards generative AI and my own intertwingled perspective on technologies and collective cognition, which is most fully developed (so far) in my most recent book, How Education Works: Teaching, Technology, and Technique. If you’re not familiar with our model of sets, nets, groups and collectives, there’s a brief overview on the Teaching Crowds website. It’s a little long in the tooth but I think it is still useful and will help to frame what follows.
A recreation of the famous New Yorker cartoon, “On the Internet no one knows you are a dog” – but it is a robot dog
The key new insight that appears for the first time in this presentation is that, rather than being a fundamental social form in their own right, groups consist of technological processes that make use of and help to engender/give shape to the more fundamental forms of nets and sets. At least, I think they do: I need to think and talk some more about this, at least with Terry, and work it up into a paper, but I haven’t yet thought through all the repercussions. Even back when we wrote the book I always thought of groups as technologically mediated entities but it was only when writing these slides in the light of my more recent thinking on technology that I paid much attention to the phenomena that they actually orchestrate in order to achieve their ends. Although there are non-technological prototypes – notably in the form of families – these are emergent rather than designed. The phenomena that intentional groups primarily orchestrate are those of networks and sets, which are simply configurations of humans and their relationships with one another. Modern groups – in a learning context, classes, cohorts, tutorial groups, seminar groups, and so on – are designed to fulfill more specific purposes than their natural prototypes, and they are made possible by technological inventions such as rules, roles, decision-making processes, and structural hierarchies. Essentially, the group is a purpose-driven technological overlay on top of more basic social forms. It seems natural, much as language seems natural, because it is so basic and fundamental to our existence and how everything else works in human societies, but it is an invention (or many inventions, in fact) as much as wheels and silicon chips.
Groups are among the oldest and most highly evolved of human technologies and they are incredibly important for learning, but they have a number of inherent flaws and trade-offs/Faustian bargains, notably in their effects on individual freedoms, in scalability (mainly achieved through hierarchies), in sometimes unhealthy power dynamics, and in limitations they place on roles individuals play in learning. Modern digital technologies can help to scale them a little further and refine or reify some of the rules and roles, but the basic flaws remain. However, modern digital technologies also offer other ways of enabling sets and networks of people to support one another’s learning, from blogs and mailing lists to purpose-built social networking systems, from Wikipedia and Academia.edu to Quora, in ways that can (optionally) integrate with and utilize groups but that differ in significant ways, such as in removing hierarchies, structuring through behaviour (collectives) and filtering or otherwise mediating messages. With some exceptions, however, the purposes of large-scale systems of this nature (which would provide an ideal set of phenomena to exploit) are not usually driven by a need for learning, but by a need to gain attention and profit. Facebook, Instagram, LinkedIn, X, and others of their ilk have vast networks to draw on but few mechanisms that support learning and limited checks and balances for reliability or quality when it does occur (which of course it does). Most of their algorithmic power is devoted to driving engagement, and the content and purpose of that engagement only matters insofar as it drives further engagement. Up to a point, trolls are good for them, which is seldom if ever true for learning systems. Some – Wikipedia, the Khan Academy, Slashdot, Stack Exchange, Quora, some SubReddits, and so on – achieve both engagement and intentional support for learning. However, they remain works in progress in the latter regard, being prone to a host of ills from filter bubbles and echo chambers to context collapse and the Matthew Effect, not to mention intentional harm by bad actors. I’ve been exploring this space for approaching 30 years now, but there remains almost as much scope for further research and development in this area as there was when I began. Though progress has been made, we have yet to figure out the right rules and structures to deal with a great many problems, and it is increasingly difficult to slot the products of our research into an increasingly bland, corporate online space dominated by a shrinking number of bland, centralized learning management systems that continue to refine their automation of group processes and structures and, increasingly, to ignore the sets and networks on which they rely.
With that in mind, I see big potential benefits for generative AIs – the ultimate collectives – as supporters and enablers for crowds of people learning together. Generative AI provides us with the means to play with structures and adapt in hitherto impossible ways, because the algorithms that drive their adaptations are indefinitely flexible, the reified activities that form them are vast, and the people that participate in them play an active role in adjusting and forming their algorithms (not the underpinning neural nets but the emergent configurations they take). These are significant differences from traditional collectives, that tend to have one purpose and algorithm (typically complex but deterministic), such as returning search results or engaging network interactions. I also see a great many potential risks, of which I have written fairly extensively of late, most notably in playing soft orchestral roles in the assembly that replace the need for humans to learn to play them. We tread a fine line between learning utopia and learning dystopia, especially if we try to overlay them on top of educational systems that are driven by credentials. Credentials used to signify a vast range of tacit knowledge and skills that were never measured, and (notwithstanding a long tradition of cheating) that was fine as long as nothing else could create those signals, because they were serviceable proxies. If you could pass the test or assignment, it meant that you had gone through the process and learned a lot more than what was tested. This has been eroded for some time, abetted by social media like Course Hero or Chegg that remain quite effective ways of bypassing the process for those willing to pay a nominal sum and accept the risk. Now that generative AI can do the same at considerably lower cost, with greater reliability, and lower risk, without having gone through the process, they no longer make good signifiers and, anyway (playing Devil’s advocate), it remains unclear to what extent those soft, tacit skills are needed now that generative AIs can achieve them so well. I am much encouraged by the existence of George’s Paul LeBlanc’s lab initiative, the fact that George is the headliner chief scientist for it, its intent to enable human-centred learning in an age of AI, and its aspiration to reinvent education to fit. We need such endeavours. I hope they will do some great things.
ChatGPT and I came up with this image summarizing my thoughts on generative AI for a presentation I am giving later today. I think it did a pretty good job. Thanks, ChatGPT, and thanks to Peter Steiner for the awesome original
A Turkish university candidate was recently arrested after being caught using an AI-powered system to obtain answers to the entrance exam in real-time.
The candidate used a simple and rather obvious set-up: a camera disguised as a shirt button that was used to read the questions, a router hidden in a hollowed-out shoe linking to a stealthily concealed mobile device that queried a generative AI (likely ChatGPT-powered) that fed the answers back verbally to an in-ear bluetooth earpiece. Constructing such a thing would take a little ingenuity but it’s not rocket science. It’s not even computer science. Anyone could do this. It would take some skill to make it work well, though, and that may be the reason this attempt went wrong. The candidate was caught as a result of their suspicious behaviour, not because anyone directly noticed the tech. I’m trying to imagine the interface, how the machine would know which question to answer (did the candidate have to point their button in the right direction?), how they dealt with dictating the answers at a usable speed (what if they needed it to be repeated? Did they have to tap a microphone a number of times?), how they managed sequence and pacing (sub-vocalization? moving in a particular way?). These are soluble problems but they are not trivial, and skill would be needed to make the whole thing seem natural.
It may take a little while for this to become a widespread commodity item (and a bit longer for exam-takers to learn to use it unobtrusively), but I’m prepared to bet that someone is working on it, if it is not already available. And, yes, exam-setters will come up with a counter-technology to address this particular threat (scanners? signal blockers?Forcing students to strip naked?) but the cheats will be more ingenious, the tech will improve, and so it will go on, in an endless and unwinnable arms race.
Very few people cheat as a matter of course. This candidate was arrested – exam cheating is against the law in Turkey – for attempting to solve the problem they were required to solve, which was to pass the test, not to demonstrate their competence. The level of desperation that led to them adopting such a risky solution to the problem is hard to imagine, but it’s easy to understand how high the stakes must have seemed and how strong the incentive to succeed must have been. The fact that, in most societies, we habitually inflict such tests on both children and adults, on an unimaginably vast scale, will hopefully one day be seen as barbaric, on a par with beating children to make them behave. They are inauthentic, inaccurate, inequitable and, most absurdly of all, a primary cause of the problem they are designed to solve. We really do need to find a better solution.
Note on the post title: the student was caught so, as some have pointed out, it would be an exaggeration to say that this one case is proof that proctored exams have fallen to generative AI, but I think it is a very safe assumption that this is not a lone example. This is a landmark case because it provides the first direct evidence that this is happening in the wild, not because it is the first time it has ever happened.
Learning outcomes do have their uses. They are very useful tools when designing learning activities, courses, and programs. Done well, they help guide and manage the process, and they are especially helpful in teams as a way to share intentions and establish boundaries, which can also be handy when thinking about how they fit into a broader program of study, or how they mesh with other learning activities elsewhere. They can perform a useful role in assessment. I find them especially valuable when I’m called upon to provide a credential because, rather than giving marks to assignments that I force students to do, I can give marks for learning outcomes, thereby allowing students to select their own evidence of having met them. It’s a great way to encourage participation in a learning community without the appallingly controlling, inauthentic, but widespread practice of giving marks for discussion contributions because such contributions can be very good evidence of learning, but there are other ways to provide it. It also makes it very easy to demonstrate to others that course outcomes have been met, it makes it easy for students to understand the marks they received, it helps to avoid over-assessment and, especially if students are involved in creating or weighting the outcomes themselves, it empowers them to take control of the assessment process. Coming up with the evidence is also a great reflective exercise in itself, and a chance to spot any gaps before it makes a difference to the marks. Learning outcomes can also help teachers as part of how they evaluate the success of an educational intervention, though it is better to harvest outcomes than to just measure achievement of ones that are pre-specified because, if teaching is successful, students always learn more than what we require them to learn. However, they should never be used in a managerial process as objective, measurable ways of monitoring performance because that is simply not what they do.
They can have some limited value for students when initially choosing a learning activity, course, or program, or (with care and support) for evaluating their own success. However, they should seldom if ever be the first things students see because you could hardly be more boring or controlling than to start with “at the end of this course you will …”. And they should seldom if ever be used to constrain or hobble teaching or learning because, as Young’s article makes beautifully clear, learning is an adventure into the unknown that should be full of surprises, for learners and for teachers. That said, there are a few kinds of learning outcome (that I have been thinking about including in my own courses for many years but have yet to work up the nerve to implement) that might be exceptions. For example…
At the end of this course a successful student will be able to:
feel a sense of wonder and excitement about [subject];
feel a passionate need to learn more about [subject];
teach their teacher about [subject];
enthusiastically take the course again and learn something completely different the second time around;
learn better;
do something in [subject] that no one has ever done before;
use what they have learned to make the world a better place;
explain [subject] to their teacher’s grandmother in a way that she would finally understand;
laugh uncontrollably at a joke that only experts in the field would get;
tell an original good joke that only experts in the field would get and that would make them laugh;
at a dinner party, even when slightly tipsy, convince an expert in the field that they are more of an expert;
design and deliver a better course than this on [subject].
How does the order of questions in a test affects how well students do?
The answer is “significantly.”
The post points to a paywalled study that shows, fairly conclusively, that starting with simpler questions in a typical academic quiz (on average) improves the overall results and, in particular, the chances of getting to the end of a quiz at all. The study includes both an experimental field study using a low-stakes quiz, and a large-scale correlational study using a PISA dataset. Some of the effect sizes are quite large: about a 50% increase in non-completions for the hard-to-easy condition compared with the easy-to-hard condition, and a about a 25% increase in time on task for the easy-to-hard condition, suggesting students stick at it more when they have gained confidence earlier on. The increase in marks for the easy-to-hard condition compared with the hard-to easy condition is more modest when non-completions are excluded, but enough to make the difference between a pass and a fail for many students.
I kind-of knew this already but would not have expected it to make such a big difference. It is a good reminder that, of course, objective tests are not objective. A quiz is a kind of interactive story with a very definite beginning, middle, and end, and it makes a big difference which parts of the story happen when, especially the beginning. Quizzes are like all kinds of learning experience: scaffolding helps, confidence matters, and motivation is central. You can definitely put someone off reading a story if it has a bad first paragraph. Attitude makes all the difference in the world, which is one very good reason that such tests, and written exams in general, are so unfair and weak at discriminating capability, and why I have always done unreasonably well in such things: I generally relish the challenge. The authors reckon that adaptive quizzes might be one answer, and would especially benefit weaker students by ramping up the difficulty slowly, but warn that they may make things worse for more competent students who would experience the more difficult questions sooner. That resonates with my experience, too.
I don’t give marks for quizzes in any of my own courses and I allow students to try them as often as they wish but, even so, I have probably caused motivational harm by randomizing formative questions. I’m going to stop doing that in future. Designated teachers are never the sole authors of any educational story but, whenever they exert control, their contributions can certainly matter, at small scales and large. I wonder, how many people have had their whole lives changed for the worse by a bad opening line?
