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).
I was listening to Guy Raz’s How I Built This podcast, his interview with Jensen Huang. The conversation turned to jobs and AI. Huang used radiology, not teaching, as his example, and it stuck with me.
Experts declared that radiology would be the first profession AI eliminated. The logic made sense at the time: early AI was built for computer vision, computer vision could read scans better than any human, so radiologists were done. This prediction turned out to be right and wrong. Every radiologist now uses AI to read scans, faster and with fewer errors, which lets them see more patients. AI did not replace radiologists; it made them more productive. The job changed and persisted.
Huang pointed to the distinction between a job’s purpose and its tasks. The distinction seems obvious in hindsight, yet I had not framed it this way before. 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, the higher-order task, and it requires clinical judgment, context, and information no model has. AI took over the lower-order task, and human radiologists (in the loop) took on more of the higher-order work. Faster scanning let hospitals order more scans and see more patients, which increased demand for radiologists.
Huang located the harm in the prediction itself. When experts declared radiology obsolete, young people who might have entered the field chose something else instead, and the field ended up short on radiologists. The narrative did what the technology could not. A shrinking pool of radiologists could also give AI tools a larger share of the field than they would have earned otherwise, turning the narrative into 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 take over the lower-order tasks of teaching: writing lessons, building assessments, giving feedback. The purpose of teaching is learning, and the work that serves it is building experiences, guiding students through complexities, and asking the question that shifts perspective and builds motivation to learn. Offloading the production tasks should push higher-order work to the center.
Education will have abundant AI tools, and, hopefully, teachers with more time to spend on that higher-order work.
AI will change teaching, as every technology integration does. The profession will persist, and it will demand 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, in 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.