Tool-using tools – Perceptions and misperceptions of generative AI (slides from my keynote for the Global AI Summit, 2025, at Bennett University, India)

Posted November 24, 2025 by Jon Dron

Here are the slides from the first of my two keynotes last week, Tool-using tools – Perceptions and misperceptions of generative AI. This one was for the Global AI Summit 2025, hosted at Bennett University in India.

The talk covered ground that I’ve already blogged about. My big point is that it is not just inaccurate but misleading to think of genAIs as tools: it grants us too much agency. If you have to use an existing term then I think “appliance” is a much more accurate label because they are technologies that do thinking for us, much as refrigerators do cooling for us, or dishwashers wash our dishes. Just as some skill is needed to use a dishwasher or fridge, some skill is needed to get a genAI to think: it’s OK to think of prompts as tools for that purpose. However, it is not our thinking, and that matters. GenAIs are unlike any prior technology because they are, like us, tool users and creators. It is possible to ask genAIs to act as (or at least create and host) tools. It’s just not what we usually use them for. I think “metatool” is a better term.

I gave this talk online, at 4am Wednesday morning, finishing less than an hour before I had to leave for the airport for Japan, where I was due to give my second keynote of the week, on generative vs degenerative AI, so I might not have been at the top of my game!

Generative vs Degenerative AI (my ICEEL 2025 keynote slides)

Posted November 23, 2025, updated onNovember 24, 2025 by Jon Dron

I gave my second keynote of the week last week (in person!) at the excellent ICEEL conference in Tokyo. Here are the slides: Generative AI vs degenerative AI: steps towards the constructive transformation of education in the digital age. The conference theme was “AI-Powered Learning: Transforming Education in the Digital Age”, so this is roughly what I talked about…

Transformation in (especially higher) education is quite difficult to achieve. There is gradual evolution, for sure, and the occasional innovation, but the basic themes, motifs, and patterns – the stuff universities do and the ways they do it – have barely changed in nigh-on a millennium. A mediaeval professor or student would likely feel right at home in most modern institutions, now and then right down to the clothing. There are lots of path dependencies that have led to this, but a big part of the reason is down to the multiple subsystems that have evolved within education, and the vast number of supersystems in which education participates. Anything new has to thrive in an ecosystem along with countless other parts that have co-evolved together over the last thousand years. There aren’t a lot of new niches, the incumbents are very well established, and they are very deeply enmeshed.

There are several reasons that things may be different now that generative AI has joined the mix. Firstly, generative AIs are genuinely different – not tools but cognitive Santa Claus machines, a bit like appliances, a bit like partners, capable of becoming but not really the same as anything else we’ve ever created. Let’s call them metatools, manifestations of our collective intelligence and generators of it. One consequence of this is that they are really good at doing what humans can do, including teaching, and students are turning to them in droves because they already teach the explicit stuff (the measurable skills and knowledge we tend to assess, as opposed to the values, attitudes, motivational and socially connected stuff that we rarely even notice) better than most human teachers. Secondly, genAI has been highly disruptive to traditional assessment approaches: change (not necessarily positive change) must happen. Thirdly, our cognition itself is changed by this new kind of technology for better or worse, creating a hybrid intelligence we are only beginning to understand but that cannot be ignored for long without rendering education irrelevant. Finally genAI really is changing everything everywhere all at once: everyone needs to adapt to it, across the globe and at every scale, ecosystem-wide.

There are huge risks that it can (and plentiful evidence that it already does) reinforce the worst of the worst of education by simply replacing what we already do with something that hardens it further, that does the bad things more efficiently, and more pervasively, that revives obscene forms of assessment and archaic teaching practices, but without any of the saving graces and intricacies that make educational systems work despite their apparent dysfunctionality. This is the most likely outcome, sadly. If we follow this path, it ends in model collapse for not just LLMs but for human cognition. However, just perhaps, how we respond to it could change the way we teach in good if not excellent ways. It can do so as long as human teachers are able to focus on the tacit, the relational, the social, and the immeasurable aspects of what education does rather than the objectives-led, credential-driven, instrumentalist stuff that currently drives it and that genAI can replace very efficiently, reliably, and economically. In the past, the tacit came for free when we did the explicit thing because the explicit thing could not easily be achieved without it. When humans teach, no matter how terribly, they teach ways of being human. Now, if we want it to happen (and of course we do, because education is ultimately more about learning to be than learning to do), we need to pay considerably more deliberate attention to it.

The table below, copied from the slides, summarizes some of the ways we might productively divide the teaching role between humans and AIs:

	Human Role (e.g.)	AI role (e.g.)
Relationships	Interacting, role modelling, expressing, reacting.	Nurturing human relationships, discussion catalyzing/summarizing
Values	Establishing values through actions, discussion, and policy.	Staying out of this as much as possible!
Information	Helping learners to see the personal relevance, meaning, and value of what they are learning. Caring.	Helping learners to acquire the information. Providing the information.
Feedback	Discussing and planning, making salient, challenging. Caring.	Analyzing objective strengths and weaknesses, helping with subgoals, offering support, explaining.
Credentialling	Responsibility, qualitative evaluation.	Tracking progress, identifying unprespecified outcomes, discussion with human teachers.
Organizing	Goal setting, reacting, responding.	Scheduling, adaptive delivery, supporting, reminding.
Ways of being	Modelling, responding, interacting, reflecting.	Staying out of this as much as possible!

I don’t think this is a particularly tall order but it does demand a major shift in culture, process, design, and attitude. Achieving that from scratch would be simple. Making it happen within existing institutions without breaking them is going to be hard, and the transition is going to be complex and painful. Failing to do so, though, doesn’t bear thinking of.

Abstract

In all of its nearly 1000-year history, university education has never truly been transformed. Rather, the institution has gradually evolved in incremental steps, each step building on but almost never eliminating the last. As a result, a mediaeval professor dropped into a modern university would still find plenty that was familiar, including courses, semesters, assessments, methods of teaching and perhaps, once or twice a year, scholars dressed like him. Even such hugely disruptive innovations as the printing press or the Internet have not transformed so much as reinforced and amplified what institutions have always done. What chance, then, does generative AI have of achieving transformation, and what would such transformation look like?
In this keynote I will discuss some of the ways that, perhaps, it really is different this time: for instance, that generative AIs are the first technologies ever invented that can themselves invent new technologies; that the unprecedented rate and breadth of adoption is sufficient to disrupt stabilizing structures at every scale; that their disruption to credentialing roles may push the system past a tipping point; and that, as cognitive Santa Claus machines, they are bringing sweeping changes to our individual and collective cognition, whether we like it or not, that education cannot help but accommodate. However, complex path dependencies make it at least as likely that AI will reinforce the existing patterns of higher education as disrupt them. Already, a surge in regressive throwbacks like oral and written exams is leading us to double down on what ought to be transformed while rendering vestigial the creative, relational and tacit aspects of our institutions that never should. Together, we will explore ways to avoid this fate and to bring about constructive transformation at every layer, from the individual learner to the institution itself.

Paper: Cognitive Santa Claus Machines and the Tacit Curriculum

Posted September 26, 2025 by Jon Dron

This is my contribution to the inaugural issue of AACE’s new journal of AI-Enhanced Learning, Cognitive Santa Claus Machines and the Tacit Curriculum. If the title sounds vaguely familiar, it might be because you might have seen my post offering some further thoughts on cognitive Santa Claus machines written not long after I had submitted this paper.

The paper itself delves a bit into the theory and dynamics of genAI, cognition, and education. It draws heavily from how the theory in my last book, has evolved, adding a few of its own refinements here and there, most notably in its distinction of use-as-purpose vs use-as-process. Because genAIs are not tools but cognitive Santa Claus machines, this helps to explain how the use of genAI can simultaneously enhance and diminish learning, both individually and collectively, to varying degrees that range from cognitive apocalypse to cognitive nirvana, depending on what we define learning to be, whose learning we care about, and what kind of learning gets enhanced or diminished. A fair portion of the paper is taken up with explaining why, in a traditional credentials-driven, fixed-outcomes-focused institutional context, generative AI will usually fail to enhance learning and, in many typical learning and institutional designs, may even diminish our individual (and ultimately collective) capacity to do so. As always, it is only the whole assembly that matters, especially the larger structural elements, and genAI can easily short-circuit a few of those, making the whole seem more effective (courses seem to work better, students seem to display better evidence of success) but the things that actually matter get left out of the circuit.

The conclusion describes the broad characteristics of educational paths that will tend to lead towards learning enhancement by, first of all, focusing our energies on education’s social role in building and sharing tacit knowledge, then on ways of using genAI to do more that we could do alone, and, underpinning this, on expanding our definitions of what “learning” means beyond the narrow confines of “individuals meeting measurable learning outcomes”. The devil is in the detail and there are certainly other ways to get there than by the broad paths I recommend but I think that, if we start with the assumption that our students are neither products nor consumers nor vessels for learning outcomes, but co-participants in our richly complex, ever evolving, technologically intertwingled learning communities, we probably won’t go too far wrong.

Abstract:

Every technology we create, from this sentence to the Internet, changes us but, through generative AI (genAI), we can now access a kind of cognitive Santa Claus machine that invents other technologies, so the rate of change is exponentially rising. Educators struggle to maintain a balance between sustaining pre-genAI values and skills, and using the new possibilities genAIs offer. This paper provides a conceptual lens for understanding and responding to this tension. It argues that, on the one hand, educators must acknowledge and embrace the changes genAI brings to our extended cognition while, on the other, that we must valorize and double-down on the tacit curriculum, through which we learn ways of being human in the world.

Venturing into the Unknown: Critical Insights into Grey Areas and Pioneering Future Directions in Educational Generative AI Research | TechTrends

Posted February 28, 2025 by Jon Dron

The latest paper I can proudly add to my list of publications, Venturing into the Unknown: Critical Insights into Grey Areas and Pioneering Future Directions in Educational Generative AI Research has been published in the (unfortunately) closed journal TechTrends. Here’s a direct link to the paper that should hopefully bypass the paywall, if it has not been used too often.

I’m 16th of 47 coauthors, led by the truly wonderful Junhong Xiao, who is the primary orchestrator and mastermind behind it. This is a companion piece to our Manifesto for Teaching and Learning in a Time of Generative AI and it starts where the other paper left off, delving further into what we don’t know (or at least do not agree that we know) about and (taking up most of the paper) what we might do about that lack of knowledge. I think this presents a pretty useful and wide-ranging research agenda for anyone with an interest in AI and education.

Methodologically, it emerged through a collaborative writing process between a very multinational group of international researchers in open, digital, and online learning. It’s not a random sample of people who happen to know one another: the huge group represents a rich mix of (extremely) well-established and (excellent) emerging researchers from a broad set of cultural backgrounds, covering a wide range of research interests in the field. Junhong does a great job of extracting the themes and organizing all of that into a coherent narrative.

In many ways I like this paper more than its companion piece. I think this is because, though its findings are – as the title implies – less well-defined than the first, I am more closely aligned with the underlying assumptions, attitudes and values that underpin the analysis. It grapples more firmly with the wicked problems and it goes deeper into the broader, situated, human nature of the systems in which generative AI is necessarily intertwingled, skimming over the more simplistic conversations about cheating, reliability, and so on to get at some meatier but more fundamental issues that, ultimately, relate to how and why we do this education thing in the first place.

Abstract

Advocates of AI in Education (AIEd) assert that the current generation of technologies, collectively dubbed artificial intelligence, including generative artificial intelligence (GenAI), promise results that can transform our conceptions of what education looks like. Therefore, it is imperative to investigate how educators perceive GenAI and its potential use and future impact on education. Adopting the methodology of collective writing as an inquiry, this study reports on the participating educators’ perceived grey areas (i.e. issues that are unclear and/or controversial) and recommendations on future research. The grey areas reported cover decision-making on the use of GenAI, AI ethics, appropriate levels of use of GenAI in education, impact on learning and teaching, policy, data, GenAI outputs, humans in the loop and public–private partnerships. Recommended directions for future research include learning and teaching, ethical and legal implications, ownership/authorship, funding, technology, research support, AI metaphor and types of research. Each theme or subtheme is presented in the form of a statement, followed by a justification. These findings serve as a call to action to encourage a continuing debate around GenAI and to engage more educators in research. The paper concludes that unless we can ask the right questions now, we may find that, in the pursuit of greater efficiency, we have lost the very essence of what it means to educate and learn.

Reference

Xiao, J., Bozkurt, A., Nichols, M., Pazurek, A., Stracke, C. M., Bai, J. Y. H., Farrow, R., Mulligan, D., Nerantzi, C., Sharma, R. C., Singh, L., Frumin, I., Swindell, A., Honeychurch, S., Bond, M., Dron, J., Moore, S., Leng, J., van Tryon, P. J. S., … Themeli, C. (2025). Venturing into the Unknown: Critical Insights into Grey Areas and Pioneering Future Directions in Educational Generative AI Research. TechTrends. https://doi.org/10.1007/s11528-025-01060-6

The collective ochlotecture of large language models: slides from my talk at CI.edu, 2024

Posted December 10, 2024 by Jon Dron

Here are my slides from the 1st International Symposium on Educating for Collective Intelligence, last week, here is my paper on which it was based, and here is the video of the talk itself:

You can find this and videos of the rest of the stunning line-up of speakers at https://www.youtube.com/playlist?list=PLcS9QDvS_uS6kGxefLFr3kFToVIvIpisn It was an incredibly engaging and energizing event: the chat alone was a masterclass in collective intelligence that was difficult to follow at times but that was filled with rich insights and enlightening debates. The symposium site, that has all this and more, is at https://cic.uts.edu.au/events/collective-intelligence-edu-2024/

With just 10 minutes to make the case and 10 minutes for discussion, none of us were able to go into much depth in our talks. In mine I introduced the term “ochlotecture”, from the Classical Greek ὄχλος (ochlos), meaning “multitude” and τέκτων (tektōn) meaning “builder” to describe the structures and processes that define the stuff that gives shape and form to collections of people and their interactions. I think we need such a term because there are virtually infinite ways that such things can be configured, and the configuration makes all the difference. We blithely talk of things like groups, teams, clubs, companies, squads, and, of course, collectives, assuming that others will share an understanding of what we mean when, of course, they don’t. There were at least half a dozen quite distinct uses of the term “collective intelligence” in this symposium alone. I’m still working on a big paper on this subject that goes into some depth on the various dimensions of interest as they pertain to a wide range of social organizations but, for this talk, I was only concerned with the ochlotecture of collectives (a term I much prefer to “collective intelligence” because intelligence is such a slippery word, and collective stupidity is at least as common). From an ochlotectural perspective, these consist of a means of collecting crowd-generated information, processing it, and presenting the processed results back to the crowd. Human collective ochlotectures often contain other elements – group norms, structural hierarchies, schedules, digital media, etc – but I think those are the defining features. If I am right then large language models (LLMs) are collectives, too, because that is exactly what they do. Unlike most other collectives, though (a collectively driven search engine like Google Search being one of a few partial exceptions) the processing is unique to each run of the cycle, generated via a prompt or similar input. This is what makes them so powerful, and it is what makes their mimicry of human soft technique so compelling.

I did eventually get around to the theme of the conference. I spent a while discussing why LLMs are troubling – the fact that we learn values, attitudes, ways of being, etc from interacting with them; the risks to our collective intelligence caused by them being part of the crowd, not just aggregators and processors of its outputs; and the potential loss of the soft, creative skills they can replace – and ended with what that implies for how we should act as educators: essentially, to focus on the tacit curriculum that has, till now, always come from free; to focus on community because learning to be human from and with other humans is what it is all about; and to decouple credentials so as to reduce the focus on measurable outcomes that AIs can both teach and achieve better than an average human. I also suggested a couple of principles for dealing with generative AIs: to treat them as partners rather than tools, and to use them to support and nurture human connections, as ochlotects as much as parts of the ochlotecture.

I had a point to make in a short time, so the way I presented it was a bit of a caricature of my more considered views on the matter. If you want a more balanced view, and to get a bit more of the theoretical backdrop to all this, Tim Fawns’s talk (that follows mine and that will probably play automatically after it if you play the video above) says it all, with far greater erudition and lucidity, and adds a few very valuable layers of its own. Though he uses different words and explains it far better than I, his notion of entanglement closely echoes my own ideas about the nature of technology and the roles it plays in our cognition. I like the word “intertwingled” more than “entangled” because of its more positive associations and the sense of emergent order it conveys, but we mean substantially the same thing: in fact, the example he gave of a car is one that I have frequently used myself, in exactly the same way.

New paper: The Manifesto for Teaching and Learning in a Time of Generative AI: A Critical Collective Stance to Better Navigate the Future

Posted November 29, 2024, updated onNovember 30, 2024 by Jon Dron

I’m proud to be the 7th of 47 authors on this excellent new paper, led by the indefatigable Aras Bozkurt and featuring some of the most distinguished contemporary researchers in online, open, mobile, distance, e- and [insert almost any cognate sub-discipline here] learning, as well as a few of us hanging on their coat tails like me.

AI negaive As the title suggests, it is a manifesto: it makes a series of statements (divided into 15 positive and 20 negative themes) about what is or what should be, and it is underpinned by a firm set of humanist pedagogical and ethical attitudes that are anything but neutral. What makes it interesting to me, though, can mostly be found in the critical insights that accompany each theme, that capture a little of the complexity of the discussions that led to them, and that add a lot of nuance. The research methodology, a modified and super-iterative Delphi design in which all participants are also authors is, I think, an incredibly powerful approach to research in the technology of education (broadly construed) that provides rigour and accountability without succumbing to science-envy.

AI-positive Notwithstanding the lion’s share of the work of leading, assembling, editing, and submitting the paper being taken on by Aras and Junhong, it was a truly collective effort so I have very little idea about what percentage of it could be described as my work. We were thinking and writing together. Being a part of that was a fantastic learning experience for many of us, that stretched the limits of what can be done with tracked changes and comments in a Google Doc, with contributions coming in at all times of day and night and just about every timezone, over weeks. The depth and breadth of dialogue was remarkable, as much an organic process of evolution and emergence as intelligent design, and one in which the document itself played a significant participant role. I felt a strong sense of belonging, not so much as part of a community but as part of a connectome.

For me, this epitomizes what learning technologies are all about. It would be difficult if not impossible to do this in an in-person setting: even if the researchers worked together on an online document, the simple fact that they met in person would utterly change the social dynamics, the pacing, and the structure. Indeed, even online, replicating this in a formal institutional context would be very difficult because of the power relationships, assessment requirements, motivational complexities and artificial schedules that formal institutions add to the assembly. This was an online-native way of learning of a sort I aspire to but seldom achieve in my own teaching.

The paper offers a foundational model or framework on which to build or situate further work as well as providing a moderately succinct summary of a very significant percentage of the issues relating to generative AI and education as they exist today. Even if it only ever gets referred to by each of its 47 authors this will get more citations than most of my papers, but the paper is highly cite-able in its own right, whether you agree with its statements or not. I know I am biased but, if you’re interested in the impacts of generative AI on education, I think it is a must-read.

The Manifesto for Teaching and Learning in a Time of Generative AI: A Critical Collective Stance to Better Navigate the Future

Bozkurt, A., Xiao, J., Farrow, R., Bai, J. Y. H., Nerantzi, C., Moore, S., Dron, J., … Asino, T. I. (2024). The Manifesto for Teaching and Learning in a Time of Generative AI: A Critical Collective Stance to Better Navigate the Future. Open Praxis, 16(4), 487–513. https://doi.org/10.55982/openpraxis.16.4.777

Full list of authors:

Aras Bozkurt
Junhong Xiao
Robert Farrow
John Y. H. Bai
Chrissi Nerantzi
Stephanie Moore
Jon Dron
Christian M. Stracke
Lenandlar Singh
Helen Crompton
Apostolos Koutropoulos
Evgenii Terentev
Angelica Pazurek
Mark Nichols
Alexander M. Sidorkin
Eamon Costello
Steven Watson
Dónal Mulligan
Sarah Honeychurch
Charles B. Hodges
Mike Sharples
Andrew Swindell
Isak Frumin
Ahmed Tlili
Patricia J. Slagter van Tryon
Melissa Bond
Maha Bali
Jing Leng
Kai Zhang
Mutlu Cukurova
Thomas K. F. Chiu
Kyungmee Lee
Stefan Hrastinski
Manuel B. Garcia
Ramesh Chander Sharma
Bryan Alexander
Olaf Zawacki-Richter
Henk Huijser
Petar Jandrić
Chanjin Zheng
Peter Shea
Josep M. Duart
Chryssa Themeli
Anton Vorochkov
Sunagül Sani-Bozkurt
Robert L. Moore
Tutaleni Iita Asino

Abstract

This manifesto critically examines the unfolding integration of Generative AI (GenAI), chatbots, and algorithms into higher education, using a collective and thoughtful approach to navigate the future of teaching and learning. GenAI, while celebrated for its potential to personalize learning, enhance efficiency, and expand educational accessibility, is far from a neutral tool. Algorithms now shape human interaction, communication, and content creation, raising profound questions about human agency and biases and values embedded in their designs. As GenAI continues to evolve, we face critical challenges in maintaining human oversight, safeguarding equity, and facilitating meaningful, authentic learning experiences. This manifesto emphasizes that GenAI is not ideologically and culturally neutral. Instead, it reflects worldviews that can reinforce existing biases and marginalize diverse voices. Furthermore, as the use of GenAI reshapes education, it risks eroding essential human elements—creativity, critical thinking, and empathy—and could displace meaningful human interactions with algorithmic solutions. This manifesto calls for robust, evidence-based research and conscious decision-making to ensure that GenAI enhances, rather than diminishes, human agency and ethical responsibility in education.

Slides from my ICEEL ’24 Keynote: “No Teacher Left Behind: Surviving Transformation”

Posted November 24, 2024, updated onNovember 24, 2024 by Jon Dron

Here are the slides from from my keynote at the 8th International Conference on Education and E-Learning in Tokyo yesterday. Sadly I was not actually in Tokyo for this but the online integration was well done and there was some good audience interaction. I am also the conference chair (an honorary title) so I may be a bit biased, but I think it’s a really good conference, with an increasingly rare blend of both the tech and the pedagogical aspects of the field, and some wonderfully diverse keynotes ranging in subject matter from the hardest computer science to reflections on literature and love (thanks to its collocation with ICLLL, a literature and linguistics conference). My keynote was somewhere in between, and deliberately targeted at the conference theme, “Transformative Learning in the Digital Era: Navigating Innovation and Inclusion.”

As my starting point for the talk I introduced the concept of the technological connectome, about which I have just written a paper (currently under revision, hopefully due for publication in a forthcoming issue of the new Journal of Open, Distance, and Digital Education), which is essentially a way of talking about extended cognition from a technological rather than a cognitive perspective. From there I moved on to the adjacent possible and the exponential growth in technology that has, over the past century or so, reached such a breakneck rate of change that innovations such as generative AI, the transformation I particularly focused on (because it is topical), can transform vast swathes of culture and practice in months if not in weeks. This is a bit of a problem for traditional educators, who are as unprepared as anyone else for it, but who find themselves in a system that could not be more vulnerable to the consequences. At the very least it disrupts the learning outcomes-driven teacher-centric model of teaching that still massively dominates institutional learning the world over, both in the mockery it makes of traditional assessment practices and in the fact that generative AIs make far better teachers if all you care about are the measurable outcomes.

The solutions I presented and that formed the bulk of the talk, largely informed by the model of education presented in How Education Works, were mostly pretty traditional, emphasizing the value of community, and of passion for learning, along with caring about, respecting, and supporting learners. There were also some slightly less conventional but widely held perspectives on assessment, plus a bit of complexivist thinking about celebrating the many teachers and acknowledging the technological connectome as the means, the object and the subject of learning, but nothing Earth-shatteringly novel. I think this is as it should be. We don’t need new values and attitudes; we just need to emphasize those that are learning-positive rather than the increasingly mainstream learning-negative, outcomes-driven, externally regulated approaches that the cult of measurement imposes on us.

Post-secondary institutions have had to grapple with their learning-antagonistic role of summative assessment since not long after their inception so this is not a new problem but, until recent decades, the two roles have largely maintained an uneasy truce. A great deal of the impetus for the shift has come from expanding access to PSE. This has resulted in students who are less able, less willing, and less well-supported than their forebears who were, on average, far more advantaged in ability, motivation, and unencumbered time simply because fewer were able to get in. In the past, teachers hardly needed to teach. The students were already very capable, and had few other demands on their time (like working to get through college), so they just needed to hang out with smart people, some of whom who knew the subject and could guide them through it in order to know what to learn and whether they had been successful, along with the time and resources to support their learning. Teachers could be confident that, as long as students had the resources (libraries, lecture notes, study time, other students) they would be sufficiently driven by the need to pass the assessments and/or intrinsic interest, that they could largely be left to their own devices (OK, a slight caricature, but not far off the reality).

Unfortunately, though this is no longer even close to the norm, it is still the model on which most universities are based. Most of the time professors are still hired because of their research skills, not teaching ability, and it is relatively rare that they are expected to receive more than the most perfunctory training, let alone education, in how to teach. Those with an interest usually have opportunities to develop their skills but, if they do not, there are few consequences. Thanks to the technological connectome, the rewards and punishments of credentials continue to do the job well enough, notwithstanding the vast amounts of cheating, satisficing, student suffering, and lost love of learning that ensues. There are still plenty of teachers: students have textbooks, YouTube tutorials, other students, help sites, and ChatGPT, to name but a few, of which there are more every day. This is probably all that is propping up a fundamentally dysfunctional system. Increasingly, the primary value of post-secondary education comes to lie in its credentialling function.

No one who wants to teach wants this, but virtually all of those who teach in universities are the ones who succeeded in retaining their love of learning for its own sake despite it, so they find it hard to understand students who don’t. Too many (though, I believe, a minority) are positively hostile to their students as a result, believing that most students are lazy, willing to cheat, or to otherwise game the system, and they set up elaborate means of control and gotchas to trap them. The majority who want the best for their students, however, are also to blame, seeing their purpose as to improve grades, using “learning science” (which is like using colour theory to paint – useful, not essential) to develop methods that will, on average, do so more effectively. In fairness, though grades are not the purpose, they are not wrong about the need to teach the measurable stuff well: it does matter to achieve the skills and knowledge that students set out to achieve. However, it is only part of the purpose. Mostly, education is a means to less measurable ends; of forming identities, attitudes, values, ways of relating to others, ways of thinking, and ways of being. You don’t need the best teaching methods to achieve that: you just need to care, and to create environments and structures that support stuff like community, diversity, connection, sharing, openness, collaboration, play, and passion.

The keynote was recorded but I am not sure if or when it will be available. If it is released on a public site, I will share it here.

And now in Chinese: 在线学习环境：隐喻问题与系统改进. And some thoughts on the value of printed texts.

Posted April 13, 2024 by Jon Dron

Warm off the press, and with copious thanks and admiration to Junhong Xiao for the invitation to submit and the translation, here is my paper “The problematic metaphor of the environment in online learning” in Chinese, in the Journal of Open Learning. It is based on my OTESSA Journal paper, originally published as “On the Misappropriation of Spatial Metaphors in Online Learning” at the end of 2022 (a paper I am quite pleased with, though it has yet to receive any citations that I am aware of).

Many thanks, too, to Junhong for sending me the printed version that arrived today, smelling deliciously of ink. I hardly ever read anything longer than a shopping bill on paper any more but there is something rather special about paper that digital versions entirely lack. The particular beauty of a book or journal written in a language and script that I don’t even slightly understand is that, notwithstanding the ease with which I can translate it using my phone, it largely divorces the medium from the message. Even with translation tools my name is unrecognizable to me in this: Google Lens translates it as “Jon Delong”. Although I know it contains a translation of my own words, it is really just a thing: the signs it contains mean nothing to me, in and of themselves. And it is a thing that I like, much as I like the books on my bookshelf.

I am not alone in loving paper books, a fact that owners of physical copies of my most recent book (which can be read online for free but that costs about $CAD40 on paper) have had the kindness to mention, e.g. here and here. There is something generational in this, perhaps. For those of us who grew up knowing no other reading medium than ink on paper, there is comfort in the familiar, and we have thousands (perhaps millions) of deeply associated memories in our muscles and brains connected with it, made more precious by the increasing rarity with which those memories are reinforced by actually reading them that way. But, for the most part, I doubt that my grandchildren, at least, will lack that. While they do enjoy and enthusiastically interact with text on screens, from before they have been able to accurately grasp them they have been exposed to printed books, and have loved some of them as much as I did at the same ages.

It is tempting to think that our love of paper might simply be because we don’t have decent e-readers, but I think there is more to it than that. I have some great e-readers in many sizes and types, and I do prefer some of them to read from, for sure: backlighting when I need it, robustness, flexibility, the means to see it in any size or font that works for me, the simple and precise search, the shareable highlights, the lightness of (some) devices, the different ways I can hold them, and so on, make them far more accessible. But paper has its charms, too. Most obviously, something printed on a paper is a thing to own whereas, on the whole, a digital copy tends to just be a licence to read, and ownership matters. I won’t be leaving my e-books to my children. The thingness really matters in other ways, too. Paper is something to handle, something to smell. Pages and book covers have textures – I can recognize some books I know well by touch alone. It affects many senses, and is more salient as a result. It takes up room in an environment so it’s a commitment, and so it has to matter, simply because it is there; a rivalrous object competing with other rivalrous objects for limited space. Paper comes in fixed sizes that may wear down but will never change: it thus keeps its shape in our memories, too. My wife has framed occasional pages from my previously translated work, elevating them to art works, decoupled from their original context, displayed with the same lofty reverence as pages from old atlases. Interestingly, she won’t do that if it is just a printed PDF: it has to come from a published paper journal, so the provenance matters. Paper has a history and a context of its own, beyond what it contains. And paper creates its own context, filled with physical signals and landmarks that make words relative to the medium, not abstractions that can be reflowed, translated into other languages, or converted into other media (notably speech). The result is something that is far more memorable than a reflowable e-text. Over the years I’ve written a little about this here and there, and elsewhere, including a paper on the subject (ironically, a paper that is not available on paper, as it happens), describing an approach to making e-texts more memorable.

After reaching a slightly ridiculous peak in the mid-2000s, and largely as a result of a brutal culling that occurred when I came to Canada nearly 17 years ago, my paper book collection has now diminished to easily fit in a single and not particularly large free-standing IKEA shelving unit. The survivors are mostly ones I might want to refer to or read again, and losing some of them would sadden me a great deal, but I would only (perhaps) run into a burning building to save just a few, including, for instance:

A dictionary from 1936, bound in leather by my father and used in countless games of Scrabble and spelling disputes when I was a boy, and that was used by my whole family to look up words at one time or another.
My original hardback copy of the Phantom Tollbooth (I have a paperback copy for lending), that remains my favourite book of all time, that was first read to me by my father, and that I have read myself many times at many ages, including to my own children.
A boxed set of the complete works of Narnia, that I chose as my school art prize when I was 18 because the family copies had become threadbare (read and abused by me and my four siblings), and that I later read to my own children. How someone with very limited artistic skill came to win the school art prize is a story for another time.
A well-worn original hardback copy of Harold and the Purple Crayon (I have a paperback copy for lending) that my father once displayed for children in his school to read, with the admonition “This is Mr Dron’s book. Please handle with care” (it was not – it was mine).
A scribble-filled, bookmark-laden copy of Kevin Kelly’s Out of Control that strongly influenced my thinking when I was researching my PhD and that still inspires me today. I can remember exactly where I sat when I made some of the margin notes.
A disintegrating copy of Storyland, given to me by my godmother in 1963 and read to me and by me for many years thereafter. There is a double value to this one because we once had two copies of this in our home: the other belonged to my wife, and was also a huge influence on her at similar ages.

These books proudly wear their history and their relationships with me and my loved ones in all their creases, coffee stains, scuffs, and tattered pages. To a greater or lesser extent, the same is true of almost all of the other physical books I have kept. They sit there as a constant reminder of their presence – their physical presence, their emotional presence, their social presence and their cognitive presence – flitting by in my peripheral vision many times a day, connecting me to thoughts and inspirations I had when I read them and, often, with people and places connected with them. None of this is true of my e-books. Nor is it quite the same for other objects of sentimental value, except perhaps (and for very similar reasons) the occasional sculpture or picture, or some musical instruments. Much as I am fond of (say) baby clothes worn by my kids or a battered teddy bear, they are little more than aides memoires for other times and other activities, whereas the books (and a few other objects) latently embody the experiences themselves. If I opened them again (and I sometimes do) it would not be the same experience, but it would enrich and connect with those that I already had.

I have hundreds of e-books that are available on many devices, one of which I carry with me at all times, not to mention an Everand (formerly Scribd) account with a long history, not to mention a long and mostly lost history of library borrowing, and I have at least a dozen devices on which to read them, from a 4 inch e-ink reader to a 32 inch monitor and much in between, but my connection with those is far more limited and transient. It is still more limited for books that are locked to a certain duration through DRM (which is one reason they are the scum of the earth). When I look at my devices and open the various reading apps on them I do see a handful of book covers, usually those that I have most recently read, but that is too fleeting and volatile to have much value. And when I open them they don’t fall open on well-thumbed pages. The text is not tangibly connected with the object at all.

As well as smarter landmarks within them, better ways to make e-books more visible would help, which brings me to the real point of this post. For many years I have wanted to paper a wall or two with e-paper (preferably in colour) on which to display e-book covers, but the costs are still prohibitive. It would be fun if the covers would become battered with increasing use, showing the ones that really mattered, and maybe dust could settle on those that were never opened, though it would not have to be so skeuomorphic – fading would work, or glyphs. They could be ordered manually or by (say) reading date, title, author, or subject. Perhaps touching them or scanning a QR code could open them. I would love to get a research grant to do this but I don’t think asking for electronic wallpaper in my office would fly with most funding sources, even if I prettied it up with words like “autoethnography”, and I don’t have a strong enough case, nor can I think of a rigorous enough research methodology to try it in a larger study with other people. Well. Maybe I will try some time. Until the costs of e-paper come down much further, it is not going to be a commercially viable product, either, though prices are now low enough that it might be possible to do it in a limited way with a poster-sized display for a (very) few thousand dollars. It could certainly be done with a large screen TV for well under $1000 but I don’t think a power-hungry glowing screen would be at all the way to go: the value would not be enough to warrant the environmental harm or energy costs, and something that emitted light would be too distracting. I do have a big monitor on my desk, though, which is already doing that so it wouldn’t be any worse, to which I could add a background showing e-book covers or spines. I could easily do this as a static image or slideshow, but I’d rather have something dynamic. It shouldn’t be too hard to extract the metadata from my list of books, swipe the images from the Web or the e-book files, and show them as a backdrop (a screensaver would be trivial). It might even be worth extending this to papers and articles I have read. I already have Pocket open most of the time, displaying web pages that I have recently read or want to read (serving a similar purpose for short-term recollection), and that could be incorporated in this. I think it would be useful, and it would not be too much work to do it – most of the important development could be done in a day or two. If anyone has done this already or feels like coding it, do get in touch!

New article from Gerald Ardito and me – The emergence of autonomy in intertwingled learning environments: a model of teaching and learning

Posted March 8, 2024 by Jon Dron

Here is a paper from the Asia-Pacific Journal of Teacher Education by my friend Gerald Ardito and me that presents a slightly different way of thinking about teaching and learning. We adopt a broadly complexivist stance that sees environments not as a backdrop to learning but as a rich network of dynamic, interwingled relationships between the various parts (including parts played by people), mediated through technologies, enabling and enabled by autonomy. The model that we develop knits together a smorgasbord of theories and models, including Self-Determination Theory (SDT), Connectivism, an assortment of complexity theories, the extended version of Paulsen’s model of cooperative freedoms developed by me and Terry Anderson, Garrison & Baynton’s model of autonomy, and my own coparticipation theory, wrapping up with a bit of social network analysis of a couple of Gerald’s courses that puts it all into perspective. From Gerald’s initial draft the paper took years of very sporadic development and went through many iterations. It seemed to take forever, but we had fun writing it. Looking afresh at the finished article, I think the diagrams might have been clearer, we might have done more to join all the dots, and we might have expressed the ideas a bit less wordily, but I am mostly pleased with the way it turned out, and I am glad to see it finally published. The good bits are all Gerald’s, but I am personally most pleased with the consolidated model of autonomy visualized in figure 4, that connects my own & Terry Anderson’s cooperative freedoms, Garrison & Baynton’s model of autonomy, and SDT.

Reference:

Gerald Ardito & Jon Dron (2024) The emergence of autonomy in intertwingled learning environments: a model of teaching and learning, Asia-Pacific Journal of Teacher Education, DOI: 10.1080/1359866X.2024.2325746

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

Posted January 28, 2024 by Jon Dron

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.