This is a collection of talks, interviews, papers, and posts on the subject of generative AI, going back to 2015. I think the one I like most is my presentation for TAMK, that covers most of my concerns and a few of my hopes about genAI in relatively digestible format. Enjoy!
The collective ochlotecture of large language models: slides from my talk at CI.edu, 2024
A brief discussion of the intersection between collective intelligence and artificial intelligence, and how that impacts educators and education.
The Manifesto for Teaching and Learning in a Time of Generative AI: A Critical Collective Stance to Better Navigate the Future
I’m 7th of 47 authors on this paper that provides a fairly comprehensive overview of issues relating to generative AI and education, and offers a set of recommendations for each of them. I think it will be quite a significant paper inasmuch as it provides framing for what is soon to come and sets a number of agendas for future research.
Slides from my ICEEL ’24 Keynote: “No Teacher Left Behind: Surviving Transformation”
In this talk I gave only a summary view of generative AI, spending most of the talk focusing on how teachers should respond. As it happens, that is largely to do what they always should have done – to care, to cultivate passion, to celebrate community, to be engaged, and so on.
How AI Teaches Its Children: slides and reflections from my keynote for AISUMMIT-2024
My hopes and fears for generative AI in a 30-minute talk. Discusses ways to rethink education in the light of the risks posed by generative AI, not for obvious reasons like cheating or loss of teaching jobs, but because of how it affects what and how we learn. Two risks: the loss of skills (compensated for by the increased adjacent possibles) and how we learn ways of being through our interactions with LLMs (a much stickier and more profound problem). The title alludes to How Gertrude Teaches Her Children, by Pestalozzi, and, in particular, Pestalozzi’s erroneous belief that we can scientifically establish teaching methods that, if applied, will inevitably lead to effective learning.
Sets, nets and groups revisited
Rethinking the nature of groups and collectives in online learning, with some observations about potential roles for generative AI.
Proctored exams have fallen to generative AI
An example of the use of generative AI in a proctored, in-person exam. This particular attempt failed because of the student’s behaviour, that I reckon to be a failure in technique, not in the tech used to cheat. Others will be doing this more successfully.
Interview with David Webster
An hour of thought-provoking chat with Dave Webster about generative AI
▶ I got air: interview with Terry Greene
Interview with me about my book, How Education Works, with the tail end of the conversation about generative AI
Educational ends and means: McNamara’s Fallacy and the coming robot apocalypse (presentation for TAMK)
Includes slides and video. Talking about the problems that proxy measurements of learning that work for human-based learning really really don’t work well when AIs are involved.
Stories that matter and stories that don’t: some thoughts on appropriate teaching roles for generative AIs
Do we really want AIs telling stories to our kids, and what do they learn as a result? Some thoughts.
Presentation – Generative AIs in Learning & Teaching: the Case Against
A Lunch n Learn session for Athabasca University, with a brief to present a negative view of generative AI.
Published in Digital – The Human Nature of Generative AIs and the Technological Nature of Humanity: Implications for Education
The most rigorous paper I have written so far about my fears and hopes for generative AI, published in Digital. It’s not perfect, but it does have references!
Recording and slides from my ESET 2023 keynote: Artificial humanity and human artificiality
A rabble rousing discussion of the risks of generative AIs
10 minute chats on Generative AI – a great series, now including an interview with me
Three short commentaries around three talking points, ably guided by Tim Fawns.
Cognitive prostheses and the future of the human race
Doom, gloom, and the slow fizzling of human intelligence.
The artificial curriculum
Thoughts on the hidden and tacit curriculum, and the abject failure of generative AI to meet the non-explicit aims of education.
CHATGPT DOESN’T ALWAYS GET IT RIGHT, BUT THE FUTURE OF AI IS ‘SO SCARY AND SO EXCITING,’ SAYS AU PROF
A report on an interview with me, not exactly representing my views in all their complexity, but a good news story from early in the ChatGPT pandemic.
View of Speculative Futures on ChatGPT and Generative Artificial Intelligence (AI): A Collective Reflection from the Educational Landscape
Great paper on AI possibilities – I am one of 36 co-authors. This one is being cited at a phenomenal rate that I have never experienced for any previous paper.
Two stories about learning to be human from a machine
Comparing how ChatGPT and I might go about writing the same story about being taught by generative AI.
Loab is showing us the unimaginable future of artificial intelligence – ABC News
Scary, spooky, wonderful. Pre-ChatGPT.
So, this is a thing…
Pre-ChatGPT discussion of what happens when both students and teachers turn out to be AIs.
Can GPT-3 write an academic paper on itself, with minimal human input?
Pre-ChatGPT post raising an alarm bell or two about where, just a few months later, we actually found ourselves.
A modest proposal for improving exam invigilation
Some thoughts on using robots as remote proctors. Possibly a little satirical in intent.
At last, a serious use for AI: Brickit
Reporting on a wonderful app for suggesting Lego models to build, based on available bricks identified by a discriminative AI.
Smart learning environments, and not so smart learning environments: a systems view | Smart Learning Environments | Full Text
2018 paper making the point that smartness is a feature of the entire technological assembly, not its parts. AIs are only parts.
AIs can pass SATs. So, what does this tell us about SATs?
Brief post from 2015 noting that generative AIs had, at that point, already reached the point they could pass a SAT with an average mark. We’ve had a few years to fix this problem and yet we have made very little progress doing so.