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.