The Radiology Lesson
I drive to Statesboro for work. It is an hour-long drive, enough to listen to podcasts but also to reflect on the changes in education, in higher ed and in K-12 (especially as a parent: “what will my son do in the future?” comes to mind a lot).
Today I was listening to Guy Raz’s How I Built This podcast, his interview with Jensen Huang. The conversation turned to jobs and AI, close to what we have been writing about recently. Huang did not talk about teachers, but he brought up an example about radiologists that stuck with me.
He told about how experts declared that radiology would be the first profession AI eliminated. The logic they proposed made sense: early AI was built for computer vision, computer vision could read scans better than any human, so radiologists were done. This prediction was right and wrong. While every radiologist now uses AI to read scans, they do it faster and make fewer errors. This way they can see more patients, and the demand for radiologists has actually increased. AI did not replace radiologists; it made them more productive. The job changed, but it did not disappear.
Huang, while making this case, pointed to the distinction between a job’s purpose and its tasks. This distinction, of course, is very commonsense when you think about it, but the thing is, I had not thought of it quite this way. In the radiology example, reading scans is a lower-order task: routine, pattern-based, the kind of work AI handles well. Diagnosing disease is the purpose, or the higher-order task. That requires clinical judgment, context, and information no model has. AI took over the lower-order task, and humans (in-the-loop) started doing more of the, important, higher-order work. This (not sure if correct) can make scanning (cheaper and) faster, hospitals ordered more scans, saw more patients, and needed to hire more radiologists, not fewer.
Huang, importantly, added another point. The harm, he argued, was not the technology but the prediction. When experts declared radiology obsolete, young people who might have entered the field chose something else instead. The result: the world does not have enough radiologists. The narrative did what the technology could not. And, additionally, with the fewer radiologists, who knows, maybe the AI tools will take over the field more than they would have otherwise. The narrative could have created a self-fulfilling prophecy.
(A grain of salt here: Huang is the CEO of Nvidia. He has a clear interest in AI optimism. But I think the point stands.)
I believe the same thing applies to teaching.
GenAI will replace the lower-order activities in teaching: writing lessons, building assessments, giving feedback (all the lower-level tasks of these activities). AI can do a lot of that. But for teaching these are not the point. The purpose is learning, and the work that serves it is building experiences, guiding students through complexities, asking the question that shifts perspective, and more importantly leading to interest and motivation to learn. Offloading the production tasks should make higher-order work more central, not less.
So, the future will be that we will not be short on AI tools for education, but we will, hopefully have teachers who will have more time to spend on higher-order work.
AI will change teaching. It already is, like every technology integration (hopefully) does. The profession will not disappear, but it will require more of the skills that are hardest to replace. The question is whether we say that clearly enough to keep people coming in.
Further reading: Hamilton, Rosenberg & Akcaoglu (2016) examined the SAMR model as a framework for thinking about levels of technology integration in teaching — TechTrends, 60(5). Koehler, Mishra, Akcaoglu & Rosenberg (2013) addressed TPACK — the knowledge teachers need to integrate technology effectively — in Bharati & Mishra (Eds.), ICT Integrated Teacher Education Models.