We Designed the Habitat
I have been reading Owen Jones’s Force of Nature [1], a book about natural selection which has the unfortunate side effect of making almost everything look like natural selection. This is probably why evolutionary biologists should not be allowed near university strategic planning.
Jones’s argument is not the tired one that evolution happened a very long time ago and eventually produced us, universities and the committee meeting. His point is that selection is happening all the time. More importantly, what we do changes the selection pressures acting in the world. This turns out to be an awkward thought to have while watching education respond to LLMs.
The Sin of Assumed Invariance
Jones uses commercial fishing to make the problem clear. Fishing industries preferentially catch large fish. Yet they have often behaved as though fish populations will remain more or less the same: remove this year’s large fish and nature will kindly manufacture another standard batch for next year.
Jones calls this the Sin of Assumed Invariance. The problem is that the fishing is itself changing the population. If being large makes you more likely to end up on a plate, being smaller becomes rather a good reproductive strategy. Jones describes evidence that intense size-selective fishing can alter growth, maturation and reproduction surprisingly quickly. The intervention changes the thing being intervened upon.
Education appears to have adopted the Sin of Assumed Invariance as an implementation strategy. We introduce AI detectors, declarations of use, supervised assessment, oral defences, locked browsers and diagrams explaining precisely when ChatGPT may be consulted. Then we ask whether the intervention worked. Somewhere inside this model sits a strangely motionless student. The policy changes. The assessment changes. The LLM changes. The student, apparently, waits. Unfortunately, students learn. So do teachers.
Introduce an AI detector and you have not simply detected AI use. You have made undetectable AI use more valuable. Introduce an AI declaration and you have not simply produced transparency. You have created a new genre: the acceptable account of how AI was used. Invent an “AI-proof assessment” and there will shortly be students discovering how a LLM can help them complete it.
This is not evidence that students have suddenly become morally defective. It is evidence that environments have consequences. Jones gives us a much better question than: Did our intervention work? He suggests that we ask instead: What did our intervention select for?
That question should probably be printed above the door of every university AI working group. Unfortunately, there is usually already something above the door. Future Ready, perhaps. Or Responsible AI. Possibly Transforming Learning for an AI-Enabled Future, if the sign was commissioned by consultants and the available wall space was generous.
These phrases perform an important institutional function. They imply that somewhere there is a future, that it has already been inspected, and that readiness consists largely of arriving there in the correct attire.
Jones suggests a less reassuring possibility. Every intervention alters the environment in which subsequent behaviour occurs. The policy does not merely regulate practice; it becomes part of the conditions to which practice adapts. The assessment rule, detector, declaration form and approved-use matrix all enter the habitat and begin exerting selection pressures of their own.
So the awkward question for the working group is not simply whether its policy encourages “responsible AI use.” It is whether the policy makes responsible use more viable than irresponsible use, or merely makes responsible-looking use more viable. Responsible-looking use sharpens the Jones point: institutional interventions can select for the appearance of the desired behaviour rather than the behaviour itself.
Future Ready is much neater. It also fits on a lanyard.
We built the fitness function
Jones becomes even more useful when he turns to evolutionary computation. The basic trick is wonderfully simple. Generate lots of possible solutions. Introduce variation. Test them. Let better-performing solutions contribute to the next generation. Repeat.
But there is a rather important detail. Someone has to specify what counts as “better.” Evolutionary computation therefore requires what Jones calls a fitness function: a specification of the problem and the characteristics against which candidate solutions will be judged.
Education already has these. We call them rubrics. Suppose an assignment rewards coherent prose, clear structure, plausible argument, appropriate referencing and something identifiable as critical thinking. Then a technology arrives that can produce coherent prose, clear structure, plausible argument and references while sounding sufficiently thoughtful to survive moderate exposure to a marking rubric.
Education responds with astonishment. This is rather like constructing a bird feeder and expressing outrage when birds turn up. We designed the habitat. Then we complained about the wildlife. The problem is not necessarily that the fitness function is bad. It is that LLMs expose a distinction we had previously been able to ignore. The declared fitness function might be: develop historical understanding. The operational fitness function might be: produce 2,000 words containing the characteristics that cause a marker to award 73%.
Before LLMs, these could be treated as sufficiently close cousins. Producing the essay generally required enough reading, thinking and writing that the artefact provided some evidence of what had happened inside the student. Not perfect evidence. Education has always survived on proxies. Otherwise assessment would require opening students and inspecting the learning directly, which would create paperwork and sometimes a lot of blood.
LLMs disturb the proxy. They produce some of the valued characteristics of the artefact without necessarily producing the educational process we thought the artefact represented. The machine has not destroyed assessment. It has done something considerably ruder. It has revealed the fitness function.
Then it gets jagged [2]
At this point the evolutionary story can become far too neat. The environment changes. People adapt. Everyone acquires AI literacy. There is a webinar. Problem solved.
But real people are inconveniently jagged. A student can be superb at getting useful responses from a LLM and terrible at recognising nonsense in those responses. Another can possess excellent disciplinary judgement but use ChatGPT as though it were Google with more adjectives. A teacher can understand their students and subject extraordinarily well while having almost no idea what current LLMs can do. Another can prompt magnificently while remaining slightly uncertain what any of it is for. Calling all this “AI literacy” gives the comforting impression of a ladder. Jaggedness gives us something more like a mountain range designed by a committee that disagreed about gravity.
People occupy different adaptive positions along multiple dimensions at once. That means there is no single route from novice to expert. More importantly, moving is difficult.
The trouble with leaving somewhere that works
Jones describes another problem in evolutionary computation. Candidate solutions can become too similar and converge on a local sub-optimal solution. His less impressive but much more useful translation is: stuck in a rut. The phrase is unfair to educational practice because ruts are often extremely efficient.
Consider the essay. The teacher knows how to set it. Students know roughly what one is. The LMS knows where to put it. The rubric knows how to judge it. The moderation process recognises it. The accreditation documents contain reassuring boxes into which it fits. Nobody has to explain to Quality Assurance why students are constructing imaginary conversations between Napoleon and a chatbot.
This arrangement may not represent the finest educational possibility available to humanity. But it works. And that matters. When people are told that LLMs require them to move to a new adaptive position, we frequently forget that the journey may first make them less competent.
A teacher who has refined an assessment for ten years replaces it with something experimental. Workload rises. Predictability falls. Students become confused. Moderators become interested. We can assume that nobody wants moderators to become interested.
So “resistance to change” may sometimes be resistance. But sometimes it is perfectly sensible behaviour by someone well adapted to their current environment who can see that getting somewhere supposedly better requires crossing a stretch of territory in which things get worse.
Jaggedness makes that crossing different for everyone. For one teacher, having a LLM challenge students’ explanations is an obvious next experiment. For another, opening ChatGPT is the experiment. For one student, a LLM expands what can be questioned. For another, fluent machine prose closes questioning down because it looks so thoroughly like an answer.
There isn't one transition to AI-enabled education. There are thousands of little transitions, made from different starting points, with different risks, capacities and adjacent possibilities. The arrow in the implementation diagram is therefore lying. It knows this. It simply has excellent graphic design.
Variation may be the thing we need
Jones’s discussion of evolutionary computation contains one final but useful twist. When candidate solutions become too similar, evolutionary algorithms can deliberately increase variation. Diversity helps the system escape the local rut and explore more of the possible solution space. This seems almost exactly opposite to our institutional instinct.
Faced with LLMs, education wants convergence and certainty. Approved practice. Approved tools. Approved prompts. Approved assessment designs. Frameworks explaining how to comply with other frameworks.
Some standardisation is clearly necessary. There are real questions about privacy, equity, intellectual responsibility and assessment validity. But if we do not yet know what good educational practice with LLMs looks like, and I don't think we do, then prematurely selecting one approved destination may be precisely the wrong thing to do.
We need variation. We need to encourage small experiments. We need to applaud cheap failures. We need different teachers trying different things from different starting positions. We should reward students noticing what helps them think and what merely helps them produce. Not because whatever emerges will necessarily be wonderful. Evolution has produced both the human brain and the blobfish. Selection is not a synonym for progress.
Which brings us back to the most important Jones question which is not How do we get education to adapt to AI? But is What is our educational environment selecting for?
If our assessments reward the production of answers, LLMs will flourish there. If our AI policies reward concealment, concealment will improve. If our institutions reward standardisation, safe practices will outcompete interesting ones. And if moving to new practices imposes all the risk on individual teachers while the institution retains all the benefits, staying in the rut may remain an exceptionally fit behaviour.
LLMs may therefore be exposing something more interesting than the weaknesses of students or the limitations of assessment. They may be exposing the selection pressures of education itself.
We built and maintain the habitat. We specified the fitness functions. We rewarded some behaviours and made others expensive. Then a peculiar new creature appeared that was exceptionally well suited to parts of the environment we had constructed.
Perhaps the interesting task is not to domesticate it. Perhaps it is to ask why the habitat looks like this in the first place. Sadly, that question does not fit neatly into the implementation framework. But be reassured, a working group will no doubt be established.
Notes
[1] Jones, O. D. (2026). Force of nature : understanding evolution's deepest logic--and putting it to use (First edition.). W.W. Norton & Company.
[2] I have written a number of posts about the jaggedness of AI savviness in teachers and students and the problems that flow from that. This is one of them.
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