These are the slides that I used for my talk with a delightful group of educational leadership students from TAMK University of Applied Sciences in Tampere, Finland at (for me) a somewhat ungodly hour Wednesday night/Thursday morning after a long day. If you were in attendance, sorry for any bleariness on my part. If not, or if you just want to re-live the moment, here is the video of the session (thanks Mark!)
The brief that I was given was to talk about what generative AI means for education and, if you have been following any of my reflections on this topic then you’ll already have a pretty good idea of what kinds of issues I raised about that. My real agenda, though, was not so much to talk about generative AI as to reflect on the nature and roles of education and educational systems because, like all technologies, the technology that matters in any given situation is the enacted whole rather than any of its assembled parts. My concerns about uses of generative AI in education are not due to inherent issues with generative AIs (plentiful though those may be) but to inherent issues with educational systems that come to the fore when you mash the two together at a grand scale.
The crux of this argument is that, as long as we think of the central purposes of education as being the attainment of measurable learning outcomes or the achievement of credentials, especially if the focus is on training people for a hypothetical workplace, the long-term societal effects of inserting generative AIs into the teaching process are likely to be dystopian. That’s where Robert McNamara comes into the picture. The McNamara Fallacy is what happens when you pick an aspect of a system to measure, usually because it is easy, and then you use that measure to define success, choosing to ignore or to treat as irrelevant anything that cannot be measured. It gets its name from Robert McNamara, US Secretary of Defense during the Vietnam war, who famously measured who was winning by body count, which is probably among the main reasons that the US lost the war.
My concern is that measurable learning outcomes (and still less the credentials that signify having achieved them) are not the ends that matter most. They are, more, means to achieve far more complex, situated, personal and social ends that lead to happy, safe, productive societies and richer lives for those within them. While it does play an important role in developing skills and knowledge, education is thus more fundamentally concerned with developing values, attitudes, ways of thinking, ways of seeing, ways of relating to others, ways of understanding and knowing what matters to ourselves and others, and finding how we fit into the social, cultural, technological, and physical worlds that we inhabit. These critical social, cultural, technological, and personal roles have always been implicit in our educational systems but, at least in in-person institutions, it seldom needs to be made explicit because it is inherent in the structures and processes that have evolved over many centuries to meet this need. This is why naive attempts to simply replicate the in-person learning experience online usually fail: they replicate the intentional teaching activities but neglect to cater for the vast amounts of learning that occur simply due to being in a space with other people, and all that emerges as a result of that. It is for much the same reasons that simply inserting generative AI into existing educational structures and systems is so dangerous.
If we choose to measure the success or failure of an educational system by the extent to which learners achieve explicit learning outcomes and credentials, then the case for using generative AIs to teach is extremely compelling. Already, they are far more knowledgeable, far more patient, far more objective, far better able to adapt their teaching to support individual student learning, and far, far cheaper than human teachers. They will get better. Much better. As long as we focus only on the easily measurable outcomes and the extrinsic targets, simple economics combined with their measurably greater effectiveness means that generative AIs will increasingly replace teachers in the majority of teaching roles. That would not be so bad – as Arthur C. Clarke observed, any teacher that can be replaced by a machine should be – were it not for all the other more important roles that education plays, and that it will continue to play, except that now we will be learning those ways of being human from things that are not human and that, in more or less subtle ways, do not behave like humans. If this occurs at scale – as it is bound to do – the consequences for future generations may not be great. And, for the most part, the AIs will be better able to achieve those learning outcomes themselves – what is distinctive about them is that they are, like us, tool users, not simply tools – so why bother teaching fallible, inconsistent, unreliable humans to achieve them? In fact, why bother with humans at all? There are, almost certainly, already large numbers of instances in which at least part of the teaching process is generated by an AI and where generative AIs are used by students to create work that is assessed by AIs.
It doesn’t have to be this way. We can choose to recognize the more important roles of our educational systems and redesign them accordingly, as many educational thinkers have been recommending for considerably more than a century. I provide a few thoughts on that in the last few slides that are far from revolutionary but that’s really the point: we don’t need much novel thinking about how to accommodate generative AI into our existing systems. We just need to make those systems work the way we have known they should work for a very long time.
