Edison’s Infinite Workshop: Innovation and education in the age of Cognitive Santa Claus Machines (slides from my keynote for IFERP’s EdInnovate 2026)

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Nek Chand's Rock Garden, illustrating the power of bricolage as a creative process
Statues in Nek Chand’s Rock Garden (photo by the author)

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. Though most of it confirmed what I already know, I learned a lot.

The gist of my talk was that generative AI challenges us to transform both how we teach and what we teach. I have spoken quite a bit about the “how” in the past – essentially it is to double down on the tacit, the relational, and the social, to care about and to empower learners, to focus on what it means to be a human in whatever fields we are trying to teach. The stuff we should already have been doing.

The “what” is new. GenAIs are pretty good at creating stuff, and that’s a problem because it is very, very tempting to get them to think for us (hence cognitive Santa Claus machines: we delegate the thinking to them so that we don’t have to). We now have access to most human knowledge, at a (mostly) expert level, with little skill needed to elicit any of it. These things are like search engines that actually give us what we are searching for, in detail, and then do whatever it was that we were planning to do with the search results on our behalf. If our descendants are not to be less than us (and I really want more for my own grandchildren), we now have to figure out what to do with that. If the answer is to turn in an essay or perform an assignment that any AI could do at least as well, then the world will end with a whimper. Our jobs are to take that, problematize it, and use it to create more than any of us (human or machine) could have created alone. Luckily we already have a model for that: bricolage, or tinkering.

Bricolage has got a bad rap in the past, often compared unfavourably with engineering (notably by Levi Strauss, who defined it and saw it as primitive) but, as Papert and Turkle wrote many years ago, it is a very legitimate way of engaging with the concrete, a highly creative activity in its own right, and it can be a very powerful approach to design. The photo at the top of this post shows just a handful of the thousands of stunning artworks created by Nek Chand and his team, all of it built from the waste products of the industrial city of Chandigarh – pieces of wire, chunks of porcelain, sacks of concrete, and other found objects. I have visited twice and cried at the beauty of it both times.

I have written of bricolage before, e.g. here and here (nicely reported on and more clearly expressed by Stefanie Panke), as a means of researching things that don’t (yet) exist, and I intend to write more. It seems to me, though, that this is one of the key skills that we should be developing for ourselves and for our students, not just for research but as a process and product of learning. It is the natural evolution of the steady progress from high-resolution to low-resolution cognition that has driven human progress for millennia. In the past we built on and with what other humans had already done: it is and has always been what makes us smart that we can, through technologies (including language and art), share parts of our cognition: we think with our creations. The more we create, the more we can create. Now we have machines that are themselves bricoleurs par excellence, capable of producing any parts or pieces we can imagine, at vast scale, and quite a few we cannot. This is different. If we take advantage of it, we can continue the technology-fuelled exponential growth that is a hallmark of our species (and, to be perfectly clear, art, writing, poetry, architecture, music, and all the humanities are among the most significant of those technologies). If we don’t, we face not just the model collapse of genAIs but, ultimately, of our own cognition. This is not about replicating what we can already do. It’s about being able to do what we cannot yet imagine. This seems like a good mission for education to me.

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