Paper: Cognitive Santa Claus Machines and the Tacit Curriculum

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.

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

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 negaiveAs 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-positiveNotwithstanding 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.

So, this is a thing…

Students are now using AIs to write essays and assignments for credit, and they are (probably) getting away with it. This particular instance may be fake, but the tools are widely available and it would be bizarre were no one to be using them for this purpose. There are already far too many sites providing stuff like product reviews and news stories (re)written by AIs, and AIs are already being used for academic paper writing. In fact, systems for doing so, like CopyMatic or ArticleGenerator, are now a commodity item. So the next step will be that we will develop AIs to identify the work of other AIs (in fact, that is already a thing, e.g. here and here), and so it will go on, and on, and on.

This kind of thing will usually evade plagiarism checkers with ease, and may frequently fool human markers. For those of us working in educational institutions, I predict that traditionalists will demand that we double down on proctored exams, in a vain attempt to defend a system that is already broken beyond repair. There are better ways to deal with this: getting to know students, making each learning journey (and outputs) unique and personal, offering support for motivated students rather than trying to ‘motivate’ them, and so on. But that is not enough.

I am rather dreading the time when an artificial student takes one of my courses. The systems are probably too slow, quirky, and expensive right now for real-time deep fakes driven by plausible GANs to fool me, at least for synchronous learning, but I think it could already convincingly be done for asynchronous learning, with relatively little supervision.  I think my solution might be to respond with an artificial teacher, into which there has been copious research for some decades, and of which there are many existing examples.

To a significant extent, we already have artificial students, and artificial teachers teaching them. How ridiculous is that? How broken is the system that not only allows it but actively promotes it?

These tools are out there, getting better by the day, and it makes sense for all of us to be using them. As they become more and more ubiquitous, just as we accommodated pocket calculators in the teaching of math, so we will need to accommodate these tools in all aspects of our education. If an AI can produce a plausible new painting in any artist’s style (or essay, or book, or piece of music, or video) then what do humans need to learn, apart from how to get the most out of the machines? If an AI can write a better essay than me, why should I bother? If a machine can teach as well as me, why teach?

This is a wake-up call. Soon, if not already, most of the training data for the AIs will be generated by AIs. Unchecked, the result is going to be a set of ever-worse copies of copies, that become what the next generation consumes and learns from, in a vicious spiral that leaves us at best stagnant, at worst something akin to the Eloi in H.G. Wells’s Time Machine.  If we don’t want this to happen then it is time for educators to reclaim, to celebrate, and (perhaps a little) to reinvent our humanity. We need, more and more, to think of education as a process of learning to be, not of learning to do, except insofar as the doing contributes to our being. It’s about people, learning to be people, in the presence of and through interaction with other people. It’s about creativity, compassion, and meaning, not the achievement of outcomes a machine could replicate with ease. I think it should always have been this way.

Originally posted at: https://landing.athabascau.ca/bookmarks/view/15164121/so-this-is-a-thing