Fourteen Seconds Before the Worksheet
Large Language Models arrived in education. This was potentially significant. Here was a peculiar new object capable of producing language, arguing, role-playing, translating, explaining, inventing examples, changing perspectives, making unexpected connections, confidently fabricating things and, when challenged, apologising in a manner suggesting absolutely no emotional consequences whatsoever.
Education regarded it carefully. For about fourteen seconds. Then somebody said:
“Can it make a worksheet?”
And that was more or less that.
The domestication programme [1]
Education has considerable experience with new technologies. Whenever something strange arrives, there is a short period during which it might conceivably change what we do. This is dangerous. Fortunately, institutions have developed powerful antibodies. The unfamiliar object is surrounded by committees, frameworks, learning outcomes, risk registers and people asking whether it integrates with the LMS.
Eventually it is rendered harmless by being made to perform an existing activity slightly faster. The computer became a typing machine. The internet became somewhere to put readings. The learning management system became a filing cabinet that sends email. And the Large Language Model, an object apparently assembled from a substantial portion of recorded human language, became a machine for making lesson plans.
Domestication complete. Nobody was hurt.
This is an astonishing achievement. It should not be underestimated. We have taken something genuinely odd, a machine with which one can conduct a rapid, recursive conversation about almost anything, and taught it to produce: lesson plans, quizzes, PowerPoint slides, rubrics, model answers, avatars that “teach”, differentiated worksheets, feedback comments, and cheerful little exit tickets asking students what they learned today.
This is rather like discovering a visiting extraterrestrial civilisation and immediately asking whether its spacecraft can laminate. The answer may well be yes. That doesn't make it the interesting question. Yet there is something wonderfully reassuring about the whole process.
If you type: Create a Year 9 lesson for photosynthesis.
Seconds later there will appear a lesson plan, a worksheet, an infographic, three learning outcomes, several activities, six multiple-choice questions and, if the machine has not been adequately supervised, a rubric. This looks enormously productive. There is certainly a lot of it. Quantity has always enjoyed a somewhat undeserved reputation in education.
We have been here before
There is a small historical joke buried in all this. In the early days of educational computing, some teachers wrote software to teach particular concepts. This was no simple matter.
Suppose you wanted to write a program to teach fractions. Before the computer would do anything useful, someone had to decide what a fraction was, what students commonly misunderstood about fractions, which examples mattered, which sequence might help, what counted as an error and what should happen next. This involved an irritating amount of thinking and, an awkward thing sometimes happened.
The person who learned most from the educational software was the person who wrote it. Writing the program forced its author to take the concept apart, examine it, find its difficult edges and put it back together. The student later encountered the finished software and clicked NEXT.
This was not quite the intended distribution of learning. Still, there was something interesting going on. The difficult work of making the teaching material was itself intellectually productive. The problem was solved. The machine can do it.
Cognitive offloading, now with clip art
Ask a LLM: Give me five analogies for entropy suitable for a ten-year-old.
The machine disappears briefly into whatever passes for thought in these circumstances and returns with five analogies. Three are faintly dreadful. One involves a bedroom. There is nearly always a bedroom. One is actually quite good. The good one is copied into PowerPoint. Yay! Success!
But something curious has happened. Someone, or something, has done the wandering. Possible representations have been generated. Comparisons have been made. Explanations have been bent, tested and discarded. The edges of the concept have been bumped into. The human receives analogy number four.
This is called cognitive offloading, which makes the transfer of intellectual activity sound reassuringly like placing luggage in an overhead compartment. Clearly, effort has been saved. Nobody had to spend twenty minutes producing five not-quite-right analogies.
This is generally regarded as a benefit because educational systems have spent decades establishing the important principle that teachers have far too much to do. But there is a small possibility that the five not-quite-right analogies were where some of the learning was hiding. We may have automated the troublesome part that required us to think and retained the PowerPoint slide. This would be mildly funny if it were not such a familiar move.
The photocopier acquires opinions
The odd thing about LLMs is not that they make educational materials. Humans have been making educational materials for centuries. Some civilisations are believed to have collapsed beneath the weight of them. The odd thing is the interaction.
You can say: “No, that's not what I mean” or, “Try another explanation,” or, “Assume the opposite,” or, “Give me an example where this breaks down,” or, “Argue against yourself,” or, “Make the case from a perspective I dislike,” or, “That's nonsense.” And the machine will always comply. It does not sigh. It does not glance at the clock. It does not say that this was covered in last week's meeting. This is a genuinely peculiar new capacity. So naturally, we have given it a template for a Year 8 worksheet.
Perhaps the output isn't the interesting object
One consequence of domestication is that attention settles on the thing produced. The lesson plan, the infographic, the worksheet or the model answer. The finished thing looks educational, so we assume that is where the educational value must be.
But perhaps the interesting object is not the output. Perhaps it is the traffic: the questions, the rejected answers, the sudden change of direction, the moment someone notices that a plausible explanation is wrong, the comparison between three different representations, the argument about what counts as a good example or, the realisation that the question itself is badly worded.
When the machine says something unexpected. The human thinks, effectively, “Hang on.” That begins to look less like content production and more like intellectual activity. Which is inconvenient, because intellectual activity is much harder to upload to the LMS.
The slightly embarrassing possibility
Students already have access to versions of these machines, often better versions and some students may be more LLM skilled than the teacher [2]. This creates a curious arrangement.
The institution may use a LLM privately to produce teaching materials. The teacher may use a LLM privately to produce teaching materials. The student may use a LLM privately to produce the assignment. Everybody then meets in the classroom and pretends the interesting thing is the document. This is a magnificent piece of theatre. The machines converse with everyone backstage while the humans exchange PDFs out front.
Perhaps there is another possibility. Rather than always hiding the interaction and presenting its polished remains, we could sometimes make the interaction itself available for inspection. Not because everyone should learn a set of approved prompting tricks. That would simply be domestication with an advanced settings menu. But because there may be something worth seeing in how different people work with a machine that answers too easily.
You can ask things like: What got accepted? What got challenged? What was ignored? (Ouch) Who noticed the hidden assumption? Who decided that using the machine at all is making matters worse? These are not really LLM skills. They are ways of working with claims, explanations, possibilities and uncertainty. They merely become unusually visible when another participant in the conversation can produce a confident answer in two seconds.
A small problem with responsible use
Educational organisations are understandably concerned that students should use AI responsibly. The usual response is to write a policy. This is rather like responding to the invention of the bicycle by issuing a document on responsible balance. The policy may be necessary. It is unlikely to teach balance.
If working with systems like these becomes part of ordinary intellectual life, then students probably need encounters with actual practices rather than simply instructions about permitted outcomes. They need to see people getting it wrong. They need to see seductive answers rejected. They need to see uncertainty survive contact with fluent prose. They need to see that sometimes the best use of a LLM is to continue the conversation. And sometimes they need to see that the best use is to close the window and go for a walk.
And to think the unthinkable, there may even be no single correct way of working with the machine. This will be disappointing for anyone currently preparing a framework. Ooops!
A successful domestication
The deepest domestication of LLMs may therefore have little to do with banning them or permitting them. It happens when we decide, almost without noticing, what kind of thing they are. Maybe they are seen as a resource generator or productivity aid or a tutor, or cheating device. Maybe a feedback machine.
Once the label sticks, the possibilities begin to shrink. The strange object becomes familiar. The familiar object becomes manageable. The manageable object gets a procurement category. And eventually somebody produces a two-page guide called Five Ways to Use Generative AI Effectively in Your Classroom.
At which point the alien spacecraft has successfully been fitted with a laminator.
But perhaps we should leave the machine strange for a little longer. Not because it is magical. It isn't. Not because it will transform education. Things have been promising to transform education for quite some time and education remains impressively difficult to transform. But because there is something almost heroic about encountering a genuinely unfamiliar technology and resisting, for slightly longer than fourteen seconds, the urge to make it do what we were already doing.
The worksheet can wait.
It has waited before.
Notes
[1] A less playful account can be found in this short paper: Bigum, C. (2023, June). Teacher librarians, orthodox and heterodox: making sense of and playing in a world increasingly run by machines. Access, 37(2), 31-35. https://drive.google.com/file/d/1XJSD294lvIjAAwc6rXkwXK8iA8zx-NJX/view?usp=sharing
[2] I have a post that points to the problem of the uneven or jagged nature of LLM savviness among students and their teachers.
