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▶ I got air: interview with Terry Greene

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Since 2018, Terry Greene has been producing a wonderful series of podcast interviews with open and online learning researchers and practitioners called Getting Air. Prompted by the publication of How Education Works, (Terry is also responsible for the musical version of the book, so I think he likes it) this week’s episode features an interview with me.

I probably should have been better prepared. Terry asked some probing, well-informed, and sometimes disarming questions, most of which led to me rambling more than I might have done if I’d thought about them in advance. It was fun, though, drifting through a broad range of topics from the nature of technology to music to the perils of generative AI (of course).

I hope that Terry does call his PhD dissertation “Getting rid of instructional designers”.

Journal of Imaginary Research, Volume 9 (including a piece by me)

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Since 2015 Kay Guccione and Matthew Cheeseman have been editing the wonderful Journal of Imaginary Research (tagline “Writing Without Discipline”) that, once a year, publishes fictional research abstracts by fictional researchers. Each issue has a theme, and Volume 9’s is “Deal or Dealing”.  I have an abstract in it.

As well as providing some entertaining and often very funny short reads, there is a serious academic intent behind all of this. As Guccione and Cheeseman put it,

“In producing these short, exploratory pieces, we seek to help writers establish a new relationship with writing; less driven by the demands of
productivity. Writing fiction in a familiar format helps us reflect on how we can creatively communicate our research projects, and how we can find the joy of creativity in all our writing. Many of the pieces we receive, whilst fictional, have a basis in a real observation or experience; almost all take a fresh look at a problem, frustration or constraint experienced by the researchers who crafted them.”

My own contribution (well, that of Dr Dorian Faust Jr, an assistant professor in the Faculty of Arbitrary Studies at the University of New Catatonia) is one of two that investigate the economic value of a soul. Mine is less about soul-selling than it is about the misapplication of quantitative research to things that cannot be quantified, as well as offering a broader critique of systems driving academia in general. It’s the work of less than an hour and I suspect that it might not make much of a contribution to my h-index but, self-referentially, that’s not going to stop me from listing it as a journal publication for my annual performance review.

Educational ends and means: McNamara’s Fallacy and the coming robot apocalypse (presentation for TAMK)

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These are the slides that I used for my talk with a delightful group of educational leadership students from TAMK University of Applied Sciences in Tampere, Finland at (for me) a somewhat ungodly hour Wednesday night/Thursday morning after a long day. If you were in attendance, sorry for any bleariness on my part. If not, or if you just want to re-live the moment, here is the video of the session (thanks Mark!)man shaking hands with a robot

The brief that I was given was to talk about what generative AI means for education and, if you have been following any of my reflections on this topic then you’ll already have a pretty good idea of what kinds of issues I raised about that. My real agenda, though, was not so much to talk about generative AI as to reflect on the nature and roles of education and educational systems because, like all technologies, the technology that matters in any given situation is the enacted whole rather than any of its assembled parts. My concerns about uses of generative AI in education are not due to inherent issues with generative AIs (plentiful though those may be) but to inherent issues with educational systems that come to the fore when you mash the two together at a grand scale.

The crux of this argument is that, as long as we think of the central purposes of education as being the attainment of measurable learning outcomes or the achievement of credentials, especially if the focus is on training people for a hypothetical workplace, the long-term societal effects of inserting generative AIs into the teaching process are likely to be dystopian. That’s where Robert McNamara comes into the picture. The McNamara Fallacy is what happens when you pick an aspect of a system to measure, usually because it is easy, and then you use that measure to define success, choosing to ignore or to treat as irrelevant anything that cannot be measured. It gets its name from Robert McNamara, US Secretary of Defense during the Vietnam war, who famously measured who was winning by body count, which is probably among the main reasons that the US lost the war.

My concern is that measurable learning outcomes (and still less the credentials that signify having achieved them) are not the ends that matter most. They are, more, means to achieve far more complex, situated, personal and social ends that lead to happy, safe, productive societies and richer lives for those within them. While it does play an important role in developing skills and knowledge, education is thus more fundamentally concerned with developing values, attitudes, ways of thinking, ways of seeing, ways of relating to others, ways of understanding and knowing what matters to ourselves and others, and finding how we fit into the social, cultural, technological, and physical worlds that we inhabit. These critical social, cultural, technological, and personal roles have always been implicit in our educational systems but, at least in in-person institutions, it seldom needs to be made explicit because it is inherent in the structures and processes that have evolved over many centuries to meet this need. This is why naive attempts to simply replicate the in-person learning experience online usually fail: they replicate the intentional teaching activities but neglect to cater for the vast amounts of learning that occur simply due to being in a space with other people, and all that emerges as a result of that. It is for much the same reasons that simply inserting generative AI into existing educational structures and systems is so dangerous.

If we choose to measure the success or failure of an educational system by the extent to which learners achieve explicit learning outcomes and credentials, then the case for using generative AIs to teach is extremely compelling. Already, they are far more knowledgeable, far more patient, far more objective, far better able to adapt their teaching to support individual student learning, and far, far cheaper than human teachers. They will get better. Much better. As long as we focus only on the easily measurable outcomes and the extrinsic targets, simple economics combined with their measurably greater effectiveness means that generative AIs will increasingly replace teachers in the majority of teaching roles.  That would not be so bad – as Arthur C. Clarke observed, any teacher that can be replaced by a machine should be – were it not for all the other more important roles that education plays, and that it will continue to play, except that now we will be learning those ways of being human from things that are not human and that, in more or less subtle ways, do not behave like humans. If this occurs at scale – as it is bound to do – the consequences for future generations may not be great. And, for the most part, the AIs will be better able to achieve those learning outcomes themselves – what is distinctive about them is that they are, like us, tool users, not simply tools – so why bother teaching fallible, inconsistent, unreliable humans to achieve them? In fact, why bother with humans at all? There are, almost certainly, already large numbers of instances in which at least part of the teaching process is generated by an AI and where generative AIs are used by students to create work that is assessed by AIs.

It doesn’t have to be this way. We can choose to recognize the more important roles of our educational systems and redesign them accordingly, as many educational thinkers have been recommending for considerably more than a century. I provide a few thoughts on that in the last few slides that are far from revolutionary but that’s really the point: we don’t need much novel thinking about how to accommodate generative AI into our existing systems. We just need to make those systems work the way we have known they should work for a very long time.

Download the slides | Watch the video

Stories that matter and stories that don’t: some thoughts on appropriate teaching roles for generative AIs

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robot reading a bedtime story to a child Well, this was definitely going to happen.

The system discussed in this Wired article is a bot (not available to the general public) that takes characters from the absurdly popular Bluey cartoon series and creates personalized bedtime stories involving them for its creator’s children using ChatGPT+. This is something anyone could do – it doesn’t take a prompt-wizard or specialized bot to do this. You could easily make any reasonably proficient LLM incorporate your child’s interests, friends, family, and characteristics and churn out a decent enough story from it. With copyright-free material you could make the writing style and scenes very similar to the original. A little editorial control may be needed here and there but I think that, with a smart enough prompt, it would do a fairly good, average sort of a job, at least as readable as what an average human might produce, in a fraction of the time. I find this to be hugely problematic, though, and not for the reasons given in the article, though there are certainly some legal and ethical concerns, especially around copyright and privacy as well as the potential for generating dubious, disturbing, or otherwise poor content.

Why stories matter

The thing that bothers me most about this is not the quality of the stories but the quality of the relationship between the author and the reader (or listener).  Stories are the most human of artifacts, the ways that we create and express meaning, no matter how banal. They act as hooks that bind us together, whether invented by a parent or shared across whole cultures. They are a big part of how we learn and establish our relationships with the world and with one another. They are glimpses into how another person thinks and feels: they teach us what it means to be human, in all its rich diversity. They reflect the best and the worst of us, and they teach us about what matters.

My children were in part formed by the stories I made up or read to them 30 or more years ago, and it matters that none were made by machines. The language that I used, the ways that I wove in people and things that were meaningful to them, the attitudes I expressed, the love that went into them, all mattered.  I wish I’d recorded one or two, or jotted down the plots of at least some of the very many Lemmie the Suicidal Lemming stories that were a particular favourite. These were not as dark as they sound – Lemmie was a cheerful creature who just happened to be prone to putting himself in life-threatening situations, usually as a result of following others. Now that they have children of their own, both my kids have deliciously dark but fundamentally compassionate senses of humour and a fierce independence that I’d like to think may, in small part, be a result of such tales.

The books I (or, as they grew, we, and then they) chose probably mattered more. Some had been read to me by my own parents and at least a couple were read to them by their own parents. Like my children, I learned to read very young, largely because my imagination was fired by those stories, and fired by how much they mattered to my parents and siblings. As much as the people around me, the people who wrote and inhabited the books I listened to and later read made me who I am, and taught me much of what I still know today – not just facts to recall in a pub quiz but ways of thinking and understanding the world, and not just because of the values they shared but because of my responses to them, that increasingly challenged those values. Unlike AI-generated tales, these were shared cultural artifacts, read by vast numbers of people, creating a shared cultural context, values, and meanings that helped to sustain and unite the society I lived in. You may not have read many of the same books I read as a middle class boy growing up in 1960s Britain but, even if you are not of my generation or cultural background, you might have read (or seen video adaptations of) one or more children’s works by A.A. Milne, Enid Blyton, C.S. Lewis, J.R.R.Tolkein, Hans Christian Anderson, Charles Dickens, Lewis Caroll, Kenneth Grahame, Rev. W. Awdry, T.S. Eliot, the Brothers Grimm, Norton Juster, Edward Lear, Hugh Lofting, Dr. Seuss, and so on. That matters, and it matters that I can still name them. These were real authors with attitudes, beliefs, ideas, and styles unlike any other. They were products and producers of the times and places they lived in. Many of their attitudes and values are, looking back, troublesome, and that was true even then. So many racist and sexist stereotypes and assumptions, so many false beliefs, so many values and attitudes that had no place in the 1960s, let alone now. And that was good, because it introduced me to a diversity of ways of being and thinking, and allowed me to compare them with my own values and those of other authors, and it prepared me for changes to come because I had noticed the differences between their context and mine, and questioned the reasons.

With careful prompting, generative AIs are already capable of producing work of similar quality and originality to fan fiction or corporate franchise output around the characters and themes of these and many other creative works, and maybe there is a place for that. It couldn’t be much worse than (say) the welter of appallingly sickly, anodyne, Americanized, cookie-cutter, committee-written Thomas the Tank Engine stories that my grandchildren get to watch and read, that bear as little resemblance to Rev. W. Awdry’s sublimely stuffy Railway Stories as Star Wars. It would soften the sting when kids reach the end of a much loved series, perhaps. And, while it is a novelty, a personalized story might be very appealing, albeit that there is something rather distasteful about making a child feel special with the unconscious output of a machine to which nothing matters. But this is not just about value to individuals, living with the histories and habits we have acquired in pre-AI times. This is something that is happening at a ubiquitous and massive scale, everywhere. When this is no longer a novelty but the norm it will change us, and change our societies, in ways that make me shiver. I fear that mass-individualization will in fact be mass-blandification, a myriad of pale shadows that neither challenge nor offend, that shut down rather than open up debate, that reinforce norms that never change and are never challenged (because who else will have read them?), that look back rather than forward, that teach us average ways of thinking, that learn what we like and enclose us in our own private filter bubble, keeping us from evolving, that only surprise us when they go wrong. This is in the nature of generative AIs because all they have to learn from is our own deliberate outputs and, increasingly, the outputs of prior generative AIs, not from any kind of lived experience. They are averaging mirrors whose warped distortions can convince us they are true reflections. Introducing AI-generated stories to very young children, at scale, seems to me to be an awful gamble with very high stakes for their futures. We are performing uncontrolled experiments with stuff that forms minds, values, attitudes, expectations, and meanings that these kids will carry with them for the rest of their lives, and there is at least some reason to suspect that the harm may be greater than the good, both on an individual and a societal level. At the very least, there is a need for a large amount of editorial control, but how many parents of young children have the time or the energy for that?

That said…

Generating, not consuming output

I do see great value in working with and supporting the kids in creating the prompts for those stories themselves. While the technology is moving too fast for these evanescent skills to be describable as generative AI literacies, the techniques they learn and discoveries they make while doing so may help them to understand the strengths and limitations of the tools as they continue to develop, and the outputs will matter more because they contributed to creating them. Plus, it is a great fun way to learn. My nearly 7-year-old grandchild, with the help of their father, has enjoyed and learned a lot from creating images with DALL-E, for instance, and has been doing so long enough to see massive improvements in its capabilities, so has learned some great meta-lessons about the nature of technological evolution too. This has not stopped them from developing their own artistic skills, including with the help of iPads and AI-assisted drawing tools, which offer excellent points of comparison and affordances to reflect on the differences. It has given them critical insight into the nature of the output and the processes that led to it, and it has challenged them to bend the machine to do what they want it to do. This kind of mindful use of the tools as complementary partners, rather than consumption of their products, makes sense to me.

I think the lessons carry forward to adult learning, too. I have huge misgivings about giving generative AIs a didactic role, for the same reasons that having them tell stories to children worry me. However, they can be great teachers for those that make use of them to create output, rather than being targets of the output they have created. For instance I have been really enjoying using ChatGPT+ to help me write an Elgg plugin over the past few weeks, intended to deal with a couple of show-stopping bugs in an upgrade to the Landing that I had been struggling with for about 3 years, on and (mostly) off. I had come to see the problems as intractable, especially as a fair number of far smarter Elgg developers than I had looked at them and failed to see where the problems lay. ChatGPT+ let me try out a lot more ideas than even a large team of developers would have been able to come up with alone, and it took care of some of the mundane repetitive work that made the process slow.  Though none of it was bad, little of its code was particularly good: it made up stuff, omitted stuff, and did things inefficiently. It was really good, though, at putting in explanatory comments and documenting what it was doing. This was great, because the things I had to do to fix the flaws taught me a lot more than I would have learned had they been perfect solutions. Nearly always, it was good enough and well-documented enough to set me on the right path, but the ways it failed drove me to look at source documentation, query the underlying database (now knowing what to look for), follow conversations on GitHub, and examine human-created plugins, from which I learned a lot more and got further inspiration about what to ask the LLM to do next. Because it made different mistakes each time, it helped me to slowly develop a clearer model of how it should really have happened, so I got better and better at solving the problems myself, meanwhile learning a whole raft of useful tricks from the code that worked and at least as much from figuring out why it didn’t. It was very iterative: each attempt sparked ideas for the next attempt. It gave me just enough scaffolding to help me do what I could not do alone. About half way through I discovered the cause of the problem – a single changed word in the 150,000+ lines of code in the core engine, that was intended to better suit the new notification system, but that resulted in the existing 20m+ notification messages in the system failing to display correctly. This gave me ideas for some better prompts, the results of which taught me more. As a result, I am now a better Elgg coder than I was when I began, and I have a solution to a problem that has held up vital improvements to an ailing site used by more than 16,000 people for many years (though there are still a few hurdles to overcome before it reaches the production site).

Filling the right gaps

The final solution actually uses no code from ChatGPT+ at all, but it would not have been possible to get to that point without it. The skills it provided were different to and complementary to my own, and I think that is the critical point. To play an effective teaching role, a teacher has to leave the right kind of gaps for the learner to fill. If they are too large or too small, the learner learns little or nothing. The to and fro between me and the machine, and the ease with which I could try out different ideas, eventually led to those gaps being just the right size so that, instead of being an overwhelming problem, it became an achievable challenge. And that is the story that matters here.

The same is true of the stories that inspire: they leave the right sized gaps for the reader or listener to fill with their own imaginations while providing sufficient scaffolding to guide them, surprise them, or support them on the journey. We are participants in the stories, not passive recipients of them, much as I was a participant in the development of the Elgg plugin and, similarly, we learn through that participation. But there is a crucial difference. While I was learning the mechanical skills of coding from this process (as well as independently developing the soft skills to use them well), the listener to or reader of a story is learning the social, cultural, and emotional skills of being human (as well as, potentially, absorbing a few hard facts and the skills of telling their own stories). A story can be seen as a kind of machine in its own right: one that is designed to make us think and feel in ways that matter to the author. And that, in a nutshell, is why a story produced by a generative AI is such a problematic idea for the reader, but the use of a generative AI to help produce that story can be such a good idea for the writer.

Originally posted at: https://landing.athabascau.ca/bookmarks/view/21680600/stories-that-matter-and-stories-that-dont-some-thoughts-on-appropriate-teaching-roles-for-generative-ais

Presentation – Generative AIs in Learning & Teaching: the Case Against

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Here are the slides from my presentation at AU’s Lunch ‘n’ Learn session today. The presentation itself took 20 minutes and was followed by a wonderfully lively and thoughtful conversation for another 40 minutes, though it was only scheduled for half an hour. Thanks to all who attended for a very enjoyable discussion! self portrait of chatGPT, showing an androgynous human face overlaid with circuits

The arguments made in this were mostly derived from my recent paper on the subject (Dron, J. (2023). The Human Nature of Generative AIs and the Technological Nature of Humanity: Implications for Education. Digital, 3(4), 319–335. https://doi.org/10.3390/digital3040020) but, despite the title, my point was not to reject the use of generative AIs at all. The central message I was hoping to get across was a simpler and more important one: to encourage attendees to think about what education is for, and what we would like it to be. As the slides suggest, I believe that is only partially to do with the objectives and outcomes we set out to achieve,  that it is nothing much at all to do with the products of the system such as grades and credentials, and that focus on those mechanical aspects of the system often creates obstacles to the achievement of it. Beyond those easily measured things, education is about the values, beliefs, attitudes, relationships, and development of humans and their societies.  It’s about ways of being, not just capacity to do stuff. It’s about developing humans, not (just) developing skills. My hope is that the disruptions caused by generative AIs are encouraging us to think like the Amish, and to place greater value on the things we cannot measure. These are good conversations to have.

Published in Digital – The Human Nature of Generative AIs and the Technological Nature of Humanity: Implications for Education

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A month or two ago I shared a “warts-and-all” preprint of this paper on the risks of educational uses of generative AIs. The revised, open-access published version, The Human Nature of Generative AIs and the Technological Nature of Humanity: Implications for Education is now available in the Journal Digital.

The process has been a little fraught. Two reviewers really liked the paper and suggested minimal but worthwhile changes. One quite liked it but had a few reasonable suggestions for improvements that mostly helped to make the paper better. The fourth, though, was bothersome in many ways, and clearly wanted me to write a completely different paper altogether. Despite this, I did most of what they asked, even though some of the changes, in my opinion, made the paper a bit worse. However, I drew the line at the point that they demanded (without giving any reason) that I should refer to 8 very mediocre, forgettable, cookie cutter computer science papers which, on closer inspection, had all clearly been written by the reviewer or their team. The big problem I had with this was not so much the poor quality of the papers, nor even the blatant nepotism/self-promotion of the demand, but the fact that none were in any conceivable way relevant to mine, apart from being about AI: they were about algorithm-tweaking, mostly in the context of traffic movements in cities.  It was as ridiculous as a reviewer of a work on Elizabethan literature requiring the author to refer to papers on slightly more efficient manufacturing processes for staples. Though it is normal and acceptable for reviewers to suggest reference to their own papers when it would clearly lead to improvements, this was an utterly shameless abuse of power of a scale and kind that I have never seen before. I politely refused, making it clear that I was on to their game but not directly calling them out on it.

In retrospect, I slightly regret not calling them out. For a grizzly old researcher like me who could probably find another publisher without too much hassle, it doesn’t matter much if I upset a reviewer enough to make them reject my paper. However, for early-career researchers stuck in the publish-or-perish cycle, it would be very much harder to say no. This kind of behaviour is harmful for the author, the publisher, the reader, and the collective intelligence of the human race. The fact that the reviewer was so desperate to get a few more citations for their own team with so little regard for quality or relevance seems to me to be a poor reflection on them and their institution but, more so, a damning indictment of a broken system of academic publishing, and of the reward systems driving academic promotion and recognition. I do blame the reviewer, but I understand the pressures they might have been under to do such a blatantly immoral thing.

As it happens, my paper has more than a thing or two to say about this kind of McNamara phenomenon, whereby the means used to measure success in a system become and warp its purpose, because it is among the main reasons that generative AIs pose such a threat. It is easy to forget that the ways we establish goals and measure success in educational systems are no more than signals of a much more complex phenomenon with far more expansive goals that are concerned with helping humans to be, individually and in their cultures and societies, as much as with helping them to do particular things. Generative AIs are great at both generating and displaying those signals – better than most humans in many cases – but that’s all they do: the signals signify nothing. For well-defined tasks with well-defined goals they provide a lot of opportunities for cost-saving, quality improvement, and efficiency and, in many occupations, that can be really useful. If you want to quickly generate some high quality advertising copy, the intent of which is to sell a product, then it makes good sense to use a generative AI. Not so much in education, though, where it is too easy to forget that learning objectives, learning outcomes, grades, credentials, and so on are not the purposes of learning but just means for and signals of achieving them.

Though there are other big reasons to be very concerned about using generative AIs in education, some of which I explore in the paper, this particular problem is not so much with the AIs themselves as with the technological systems into which they are, piecemeal, inserted. It’s a problem with thinking locally, not globally; of focusing on one part of the technology assembly without acknowledging its role in the whole. Generative AIs could, right now and with little assistance,  perform almost every measurable task in an educational system from (for students) producing essays and exam answers, to (for teachers) writing activities and assignments, or acting as personal tutors. They could do so better than most people. If that is all that matters to us then we might as well therefore remove the teachers and the students from the system because, quite frankly, they only get in the way. This absurd outcome is more or less exactly the end game that will occur though, if we don’t rethink (or double down on existing rethinking of) how education should work and what it is for, beyond the signals that we usually use to evaluate success or intent. Just thinking of ways to use generative AIs to improve our teaching is well-meaning, but it risks destroying the woods by focusing on the trees. We really need to step back a bit and think of why we bother in the first place.

For more on this, and for my tentative partial solutions to these and other related problems, do read the paper!

Abstract and citation

This paper analyzes the ways that the widespread use of generative AIs (GAIs) in education and, more broadly, in contributing to and reflecting the collective intelligence of our species, can and will change us. Methodologically, the paper applies a theoretical model and grounded argument to present a case that GAIs are different in kind from all previous technologies. The model extends Brian Arthur’s insights into the nature of technologies as the orchestration of phenomena to our use by explaining the nature of humans’ participation in their enactment, whether as part of the orchestration (hard technique, where our roles must be performed correctly) or as orchestrators of phenomena (soft technique, performed creatively or idiosyncratically). Education may be seen as a technological process for developing these soft and hard techniques in humans to participate in the technologies, and thus the collective intelligence, of our cultures. Unlike all earlier technologies, by embodying that collective intelligence themselves, GAIs can closely emulate and implement not only the hard technique but also the soft that, until now, was humanity’s sole domain; the very things that technologies enabled us to do can now be done by the technologies themselves. Because they replace things that learners have to do in order to learn and that teachers must do in order to teach, the consequences for what, how, and even whether learning occurs are profound. The paper explores some of these consequences and concludes with theoretically informed approaches that may help us to avert some dangers while benefiting from the strengths of generative AIs. Its distinctive contributions include a novel means of understanding the distinctive differences between GAIs and all other technologies, a characterization of the nature of generative AIs as collectives (forms of collective intelligence), reasons to avoid the use of GAIs to replace teachers, and a theoretically grounded framework to guide adoption of generative AIs in education.

Dron, J. (2023). The Human Nature of Generative AIs and the Technological Nature of Humanity: Implications for Education. Digital, 3(4), 319–335. https://doi.org/10.3390/digital3040020

Originally posted at: https://landing.athabascau.ca/bookmarks/view/21104429/published-in-digital-the-human-nature-of-generative-ais-and-the-technological-nature-of-humanity-implications-for-education

▶ How Education Works, the audio book: now with beats

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My book has been set to music!

Many thanks to Terry Greene for converting How Education Works into the second in his inspired series of podcasts, EZ Learning – Audio Books with Beats. There’s a total of 15 episodes that can be listened to online, subscribed to with your preferred podcast app, or downloaded for later listening, read by a computer-generated voice and accompanied by some cool, soothing beats.

Terry chose a deep North American voice for the reader and Eaters In Coffeeshops Mix 1 by Eaters to accompany my book. I reckon it works really well. It’s bizarre, at first – the soothing robotic voice introduces weird pauses, mispronunciations, and curious emphases, and there are occasional voice parts in the music that can be slightly distracting – but you soon get used to it if you relax into the rhythm, and it leads to the odd serendipitous emphasis that enhances rather than detracts from the text. Oddly, in some ways it almost feels more human as a result. Though it can be a bit disconcerting at times and there’s a fair chance of being lulled to sleep by the gentle rhythm, I have a hunch that the addition of music might make it easier to remember passages from it, for reasons discussed in a paper I wrote with Rory McGreal, VIve Kumar, and Jennifer Davies a year or so ago.

I have been slowly and painfully working on a manually performed audiobook of How Education Works but it is taking much longer than expected thanks to living on the flight path of a surprising number of float planes, being in a city built on a rain forest with a noisy gutter outside my window, having two very vocal cats, and so on, not to mention not having a lot of free time to work on it, so I am very pleased that Terry has done this. I won’t stop working on the human-read version – I think this fills a different and very complementary niche – but it’s great to have something to point people towards when they ask for an audio version.

The first season of Audio Books with Beats, appearing in the feed after the podcasts for my book chapters, was another AU Press book, Terry Anderson’s Theory and Practice of Online Learning which is also well worth a listen – those chapters follow directly from mine in the list of episodes. I hope and expect there will be more seasons to come so, if you are reading this some time after it was posted, you may need to scroll down through other podcasts until you reach the How Education Works. In case it’s hard to find, here’s a list of direct links to the episodes.

Acknowledgements, Prologue, introduction

Chapter 1: A Handful of Anecdotes About Elephants

Chapter 2:  A Handful of Observations About Elephants

Part 1: All About Technology

Chapter 3: Organizing Stuff to Do Stuff

Chapter 4: How Technologies Work

Chapter 5: Participation and Technique

Part II: Education as a Technological System

Chapter 6: A Co-Participation Model of Teaching

Chapter 7: Theories of Teaching

Chapter 8: Technique, Expertise, and Literacy

Part III: Applying the Co-Participation Model

Chapter 9: Revealing Elephants

Chapter 10: How Education Works

Epilogue

Originally posted at: https://landing.athabascau.ca/bookmarks/view/20936998/%E2%96%B6-how-education-works-the-audio-book-now-with-beats

Downes.ca ~ Stephen’s Web ~ How Education Works: Teaching, Technology, and Technique

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Source: Downes.ca ~ Stephen’s Web ~ How Education Works: Teaching, Technology, and Technique

I somehow missed this when it was first posted, despite fairly avidly following OLDaily and keeping my eyes wide open for commentary on How Education Works. My only excuse is that I was travelling the day this was posted, and it was a hectic few days after that.

I’m very pleased that Stephen has some nice things to say about the book, and that he picks up on the fact that it is indeed as much about technology (and our deep, intrinsic intertwingularity with it) as it is about education. Absolutely.

I’m quite attached to the soft-hard metaphor that Stephen is lukewarm about but only, as he hints, because of what it implies about the dynamics of technology. When I started writing the book I used to talk a bit simplistically of soft and hard technologies. I still think that can be a useful distinction and there’s still plenty on the subject in the book. However, any soft technology can, in assembly, be hardened, and any hard technology can, in assembly, be softened, so it is really just another (I think slightly better) way of thinking about affordances of technologies, not about the technologies as they are assembled. For similar reasons, it is only slightly less fuzzy than existing theories of affordances, offering a framework for explaining technologies but not much that is predictive. The thing that led to the first of many rewrites of the book was my growing realization that the more important distinction is between soft and hard technique (the subset of technologies that are enacted by humans). The thing that matters most is the extent to which we are part of a pre-set (hard) orchestration, or we are the orchestrators, in any instantiation of an assembly of technologies. That is a much more precise distinction that both explains and predicts, and it is the basic distinction that (I think) is implicit in most social-constructivist models of technology in society, including Franklin’s distinction between holistic and prescriptive technologies, Boyd’s dominative and liberative technologies, Pinch & Bijker’s interpretive flexibility, and the dynamics of actor-network theory. Understanding the interplay between the rigid and the flexible in any given technology provides us with the means to control what should be controlled, to think about how we are being controlled and, if the hard components lead us down unwanted paths, ways of leaving those paths.  And, of course, it is primarily technique (soft and hard) that education explicitly seeks to develop, so it gives us a very useful tool for understanding the complex nature of education itself.

Recording and slides from my ESET 2023 keynote: Artificial humanity and human artificiality

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Here are the slides from my keynote at ESET23 in Taiwan (I was online, alas, not in Taipei!).

I will try to remember to update this post with a link to the recording, when it is available.

Here’s a recording of the actual keynote.

The themes of my talk will be familiar to anyone who follows my blog or who has read my recent paper on the subject. This is about applying the coparticipation theory from How Education Works to generative AI, raising concerns about the ways it mimics the soft technique of humans, and discussing how problematic that will be if the skills it replaces atrophy or are never learned in the first place, amongst other issues.

This is the abstract:

We are participants in, not just users of technologies. Sometimes we participate as orchestrators (for instance, when choosing words that we write) and sometimes as part of the orchestration (for instance, when spelling those words correctly). Usually, we play both roles.  When we automate aspects of technologies in which we are just parts of the orchestration, it frees us up to be able to orchestrate more, to do creative and problem-solving tasks, while our tools perform the hard, mechanical tasks better, more consistently, and faster than we could ourselves. Collectively and individually, we therefore become smarter. Generative AIs are the first of our technologies to successfully automate those soft, open-ended, creative cognitive tasks. If we lack sufficient time and/or knowledge to do what they do ourselves, they are like tireless, endlessly flexible personal assistants, expanding what we can do alone. If we cannot draw, or draw up a rental agreement, say, an AI will do it for us, so we may get on with other things. Teachers are therefore scrambling to use AIs to assist in their teaching as fast as students use AIs to assist with their assessments.

For achieving measurable learning outcomes, AIs are or will be effective teachers, opening up greater learning opportunities that are more personalized, at lower cost, in ways that are superior to average human teachers.  But human teachers, be they professionals, other students, or authors of websites, do more than help learners to achieve measurable outcomes. They model ways of thinking, ways of being, tacit knowledge, and values: things that make us human. Education is a preparation to participate in human cultures, not just a means of imparting economically valuable skills. What will happen as we increasingly learn those ways of being from a machine? If machines can replicate skills like drawing, reasoning, writing, and planning, will humans need to learn them at all? Are there aspects of those skills that must not atrophy, and what will happen to us at a global scale if we lose them? What parts of our cognition should we allow AIs to replace? What kinds of credentials, if any, will be needed? In this talk I will use the theory presented in my latest book, How Education Works: Teaching, Technology, and Technique to provide a framework for exploring why, how, and for what purpose our educational institutions exist, and what the future may hold for them.

Pre-conference background reading, including the book, articles, and blog posts on generative AI and education may be found linked from https://howeducationworks.ca

Preprint – The human nature of generative AIs and the technological nature of humanity: implications for education

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Here is a preprint of a paper I just submitted to MDPI’s Digital journal that applies the co-participation model that underpins How Education Works (and a number of my papers over the last few years) to generative AIs (GAIs). I don’t know whether it will be accepted and, even if it is, it is very likely that some changes will be required. This is a warts-and-all raw first submission. It’s fairly long (around 10,000 words).

The central observation around which the paper revolves is that, for the first time in the history of technology, recent generations of GAIs automate (or at least appear to automate) the soft technique that has, till now, been the sole domain of humans. Up until now, every technology we have ever created, be it physically instantiated, cognitive, organizational, structural, or conceptual, has left all of the soft part of the orchestration to human beings.

The fact that GAIs replicate the soft stuff is a matter for some concern when they start to play a role in education, mainly because:

  • the skills they replace may atrophy or never be learned in the first place. This is not even slightly like replacing hard skills of handwriting or arithmetic: we are talking about skills like creativity, problem-solving, critical inquiry, design, and so on. We’re talking about the stuff that GAIs are trained with.
  • the AIs themselves are an amalgam, an embodiment of our collective intelligence, not actual people. You can spin up any kind of persona you like and discard it just as easily. Much of the crucially important hidden/tacit curriculum of education is concerned with relationships, identity, ways of thinking, ways of being, ways of working and playing with others. It’s about learning to be human in a human society. It is therefore quite problematic to delegate how we learn to be human to a machine with (literally and figuratively) no skin in the game, trained on a bunch of signals signifying nothing but more signals.

On the other hand, to not use them in educational systems would be as stupid as to not use writing. These technologies are now parts of our extended cognition, intertwingled with our collective intelligence as much as any other technology, so of course they must be integrated in our educational systems. The big questions are not about whether we should embrace them but how, and what soft skills they might replace that we wish to preserve or develop. I hope that we will value real humans and their inventions more, rather than less, though I fear that, as long as we retain the main structural features of our education systems without significant adjustments to how they work, we will no longer care, and we may lose some of our capacity for caring.

I suggest a few ways we might avert some of the greatest risks by, for instance, treating them as partners/contractors/team members rather than tools, by avoiding methods of “personalization” that simply reinforce existing power imbalances and pedagogies designed for better indoctrination, by using them to help connect us and support human relationships, by doing what we can to reduce extrinsic drivers, by decoupling learning and credentials, and by doubling down on the social aspects of learning. There is also an undeniable explosion in adjacent possibles, leading to new skills to learn, new ways to be creative, and new possibilities for opening up education to more people. The potential paths we might take from now on are unprestatable and multifarious but, once we start down them, resulting path dependencies may lead us into great calamity at least as easily as they may expand our potential. We need to make wise decisions now, while we still have the wisdom to make them.

MDPI invited me to submit this article free of their normal article processing charge (APC). The fact that I accepted is therefore very much not an endorsement of APCs, though I respect MDPI’s willingness to accommodate those who find payment difficult, the good editorial services they provide, and the fact that all they publish is open. I was not previously familiar with the Digital journal itself. It has been publishing 4 articles a year since 2021, mostly offering a mix of reports on application designs and literature reviews. The quality seems good.

Abstract

This paper applies a theoretical model to analyze the ways that widespread use of generative AIs (GAIs) in education and, more broadly, in contributing to and reflecting the collective intelligence of our species, can and will change us. The model extends Brian Arthur’s insights into the nature of technologies as the orchestration of phenomena to our use by explaining the nature of humans participation in their enactment, whether as part of the orchestration (hard technique, where our roles must be performed correctly) or as orchestrators of phenomena (soft technique performed creatively or idiosyncratically). Education may be seen as a technological process for developing the soft and hard techniques of humans to participate in the technologies and thus the collective intelligence of our cultures. Unlike all earlier technologies, by embodying that collective intelligence themselves, GAIs can closely emulate and implement not only the hard technique but also the soft that, until now, was humanity’s sole domain: the very things that technologies enabled us to do can now be done by the technologies themselves. The consequences for what, how, and even whether we learn are profound. The paper explores some of these consequences and concludes with theoretically informed approaches that may help us to avert some dangers while benefiting from the strengths of generative AIs.

Originally posted at: https://landing.athabascau.ca/bookmarks/view/20512771/preprint-the-human-nature-of-generative-ais-and-the-technological-nature-of-humanity-implications-for-education