Here's a new paper (I am author 4 of 7) that challenges some common perceptions of what "smart" means when applied to an educational system. Spoiler: it's not the opposite of stupid. We actually pare it down to two distinct dimensions, one relating adaptivity (smartness), the other relating to the balance of benefit and harm. We call this combined pair of dimensions the "smupidity" of a system, and it gives us four general states to which an education system (from a lesson to a national education system) can be mapped: wise (adaptable and beneficial), benign (static and beneficial), inept (static and harmful) and pernicious (adaptable and harmful). We observe that different stakeholders will perceive the smupidity of a system differently: that the whole system, as enacted, very much including what a learner adds to make sense of it, is all that matters, not the adaptability or benefits of the parts.

I don't think I've ever read any published article by Dave Cormier that I didn't like, and I've read quite a few. This latest blog post, "A human-centred model for discussing AI in assessment" is no exception. In it, Dave describes his framework for discussing AI in assessment with teachers and other course designers, revolving around 3 questions: Did they do it?

I am fifth of 26 authors, led by the remarkable Aras Bozkurt, of "Redefining Educational Technology: A Critical Collaborative Inquiry," published in Open Praxis this month. It's a great paper. The result is not the ultimate definition of "educational technology" and it is certainly not the simplest but, as a snapshot of where we are now, I reckon it works pretty well, and the analysis leading up to it is among the best of the genre. It's well worth a read. This post is mostly a critical reflection on what we achieved and how we achieved it.

In this post I discuss a new paradigm for building plugin-based social applications (e.g. with Elgg or Wordpress) that is grounded in the fact that LLMs are better at creating a plugin from scratch than modifying one that already exists. We don't need to build code, we just need good specs, and LLMs are pretty good at helping us create those, too. This is already very useful, saving thousands of dollars and weeks of time, but the potential, once we get beyond replicating what we can do faster, better, and cheaper, is far greater. The post builds on some ideas from Ben Werdmuller, notably in the form of a specifications manager, which I extend to support ochlotecture (loosely, like a user model but for a community) management. This opens up many novel possibilities, including adaptive social systems that do not just tweak a few parameters based on a user model but, with the assistance and oversight of their communities, actually change their own source code. I think this is a bit of a game changer.

I've just finished giving a brief keynote for IFERP's 3rd EdInnovate conference in Tokyo (sadly, because I love Tokyo in the Spring,  I was online). Here are the slides. The conference was great: they put all of the keynotes and invited talks on a single day, with a very international and cross-disciplinary bunch of thought leaders (and me), and many of us were talking about very closely related themes, of rehumanizing and transforming education, from very different perspectives.

Source: Random Audits as a Scalable Deterrent to Cheating: Using Game Theory to Design Fair and Effective Academic Integrity Systems for the AI Era by David Wiley Though not particularly common, the general principle of only assessing a sample of work with oral exams (viva voces) is well established, and is common practice in a number of institutions (e.g. UC Berkeley…

What does it mean for higher education to work? The problem with claiming (as I sometimes do) that higher education is broken and needs to be transformed is that it begs the question of what it means for higher education to work, and that depends what you think it is for. From the name you'd expect that higher education might be for ...well...

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.

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...

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.  

The Journal of Artificial Intelligence in Education and Learning (AIEL), a diamond open-access journal published under the auspices of AACE and distributed worldwide through LearnTechLib has just launched its inaugural issue, which includes a paper from me (Cognitive Santa Claus Machines and the Tacit Curriculum). This inaugural issue is a great start to what I think will come to be recognized as a leading journal in the field of AI and education. 

For a few minutes the other day I thought that I had invented a new kind of fallacy or, at least, a great term to describe it. Disappointingly, a quick search revealed that it was not only an old idea but one that has been independently invented at least twice before (Berry & Martin, 1974; Weinstock, 1981). Here is its definition from Weinstock (1981):

I've just submitted a journal paper (shameless plug: to AACE's AIEL, of which I am an associate editor) in which I describe generative AIs as cognitive Santa Claus machines. I don't know if it's original but the idea appeals to me. Whatever thought we ask for, genAIs will provide it, mining their deep, deep wells of lossily compressed recorded human knowledge to provide us with skills and knowledge we do not currently have.

At short notice I was invited to be guest of honour and keynote at Bennett University's International Conference on Blockchain for Inclusive and Representative Democracy  yesterday. I was not able to attend the entire conference - my opening keynote was at 9:30pm here in Vancouver and I eventually needed to sleep - but I made it for a few hours. I was impressed with the diversity and breadth of the work going on, mainly in India, and the passionate, smart people in attendance.

Many people brush generative AI aside as being just a tool. ChatGPT describes itself as such (I asked). I think it’s more complicated than that, and this post is going to be an attempt to explain why. I’m not sure about much of what follows and welcome any thoughts you may have on whether this resonates with you and, if not, why not.

Here's a characteristically well-expressed and succinct summary of the complex nature of technologies, our relationships with them, and what that means for education by the ever-wonderful Tim Fawns. I like it a lot, and it expresses much what I have tried to express about the nature and value of technologies, far better than I could do it and in far fewer words.

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.

Here are the slides from my keynote at Thompson Rivers University's Teaching Practices Colloquium this morning. I quite like the mediaeval theme (thanks ChatGPT), which I created to provide a constant reminder that the problems we have to solve are the direct result of decisions made 1000 years ago. There was a lot of stuff from my last book in the talk, framed in terms of Faustian Bargains, intrinsic motivation, counter technologies, and adjacent possibles.

Here are the slides from a talk I just gave to a group of grad students at AU in our ongoing seminar series, on the nature of collectives and ways we can use and abuse them. It's a bit of a sprawl covering some 30 odd years of a particularly geeky, semi-philosophical branch of my research career (not much on learning and teaching in this one, but plenty of termites) and winding up with very much a work in progress.

Dron, J. (2024). Learning: A technological perspective. Journal of Open, Distance, and Digital Education, 1(2), Article 2. My latest paper, Learning: A technological perspective, was published today in the (open) Journal of Open, Distance, and Digital Education.  Methodologically, it provides a connected series of (I think) reasonable and largely uncontroversial assertions about the nature of technology and, for each assertion, offers some examples of why that matters to educators.

Thanks to Stefanie Panke for some great questions and excellent editing in this interview with me for the AACE Review. The content is in fact the product of two discussions, one coming from student questions at the end of a talk that I gave for the Asian University for Women just before Christmas, the other asynchronously with Stefanie herself.

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 It was an incredibly engaging and energizing event: …