1.3 Learning About vs Teaching with AI
If your remember only one sentence from this page: AI is a satnav. It saves the driver who already has a map in her head and quietly stops the driver who hasn't from ever building one
The satnav problem
To drive a London taxi, you must first pass a knowledge test: 320 set routes across the 113 square miles within six miles of Charing Cross, for which Transport for London allows up to two years before the examinations even begin.
Examined drivers also use a satnav, and nobody thinks less of them for it. The knowledge test did not become worthless when the device arrived. The satnav became the thing that makes the drive safe. A driver with the city in her head uses the satnav for what she cannot know: the accident on the bridge or tonight's traffic. And she notices when it routes her down a street that has been dug up since April.
Now put a newcomer in the same taxi with the same satnav. She arrives at every address. For a while, she is indistinguishable from the experienced driver. But following the satnav’s instructions, she is building nothing. After a year of turning left when instructed, she still cannot cross the city without the device, and she cannot tell when it is wrong.
That is a decade of research on AI use in one image, and it holds without the image too. The effect of an AI tool depends far more on what the user already knows than on the tool itself. This is what researchers call a schema, the organized prior knowledge of a person. Where it is in place, handing work over to a machine frees you up. Researchers call this beneficial cognitive offloading. However, when knowledge needs to be developed, the same process can prevent it from forming. This is called detrimental cognitive offloading.
A study with around a thousand secondary students shows this happening in a mathematics classroom. One group practiced with an unrestricted chatbot, another without. While they had it, the chatbot group solved 48 % more practice problems! Then came an exam with the chatbot taken away. Now, the same students who solved practice problems so well scored 17% worse than the group that had never used the AI chatbot. A third group used an AI version built to withhold answers and ask questions instead. That group lost nothing.
That third group is the whole design question. A satnav that names the destination and helps you find the route teaches you the city. A satnav that says turn left in 200 meters does not, however good its data. Researchers call the difference guardrails, and the design decides whether you help your students learn the city's map or simply find the destination.
Please remember: You have the knowledge. Your students, in the subject you teach, do not yet. That asymmetry is the whole chapter.
Two floors, not two topics
In academic literature, these two areas are considered distinct fields. You could think of them as two floors of a building, with most teachers located on the ground floor and no staircase leading to the first floor.
When asked to rate their knowledge of AI, teachers in Germany gave themselves an average rating of 3.4 out of 5. However, they rated their ability to apply this knowledge in the classroom much lower, at 2.6 out of 5. Knowing and doing had become disconnected. A trial across five European countries then tested whether training could close that gap. In the trial, 736 teachers in France, Ireland, Italy, Luxembourg and Slovenia were given a course and support. The results showed that their knowledge and ability to judge what a tool could do increased. However, their teaching did not change. They tried things and went back to what they had been doing before.
This is the knowledge–action gap. Courses produce knowledge, but changing practice needs accompaniment, trialing and colleagues to think with. Transfer has to be designed, not hoped for.
What teachers are actually doing
The most robust finding across countries is quietly surprising: teachers use AI intensively for themselves and hardly at all with their students. In Germany, 52 % use AI at least several times a month. Only 29 % use it in lessons with a class in front of them. So the public debate about "AI in the classroom" is largely a debate about something that is not yet happening at scale.
What is happening is workload relief, and here the news is good. In a British trial, 259 teachers across 68 schools were given AI support for planning science lessons for 11- to 13-year-olds. Planning time fell from 81 to 56 minutes a week. When independent assessors reviewed the resulting materials without knowing which was which, they found no drop in quality. Teachers' own estimates of the time they save across all subjects are even higher, at around six hours a week for regular users.
And one pattern is worth pausing on. In a large American survey, teachers in schools with a clear written AI policy reported saving about a quarter more time than teachers in schools without one. That is the teachers' own account rather than a measurement, and a survey cannot show which way the arrow runs. Well-organized schools may simply do both things well. But it is the opposite of what most people expect, and it is worth noting that nobody has found the reverse: wherever this has been examined, clear rules and heavy use go together. -> 1.5 Compliant use of AI
Why the tool is not the point
If you wanted a ranking of the best AI tools, the research will disappoint you and then, on second reading, set you free. When researchers gather many separate studies and ask what separates the classrooms where AI helped a great deal from those where it barely helped at all, the answer is never the brand of software. It is what the students were asked to do with it.
There are three comparisons that really drive the point home.
Pedagogy beats technology, and by a wide margin. This is what happens every time somebody sorts the studies by what the students were actually doing. Two limits should be considered equally.
Good feedback is not the same as feedback that works. In the study where AI feedback was rated highest in quality by expert reviewers, students' revisions came out worst. And where students receive both kinds, they act on their teacher's comments more often than the machine's. What makes feedback work is not its polish but whether it is taken up and uptake runs on the relationship behind it.