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CAREERMASH · TEN-MINUTE LESSON SHEET · SUBJECTS ALLIED TO MEDICINE · MEASURED AUGUST 2026

Who does AI come for first in Subjects Allied to Medicine?

Objectives (Gatsby 2 & 4 aligned)

  • Students can explain what an AI exposure score measures (the share of a job's tasks AI is already used for; a measurement of now, not a prediction).
  • Students can name one career in Subjects Allied to Medicine that measures low and one that measures high, and say why the difference exists.
  • Students can describe one “moat”: something structural that protects work from software.

The activity (8-10 minutes)

  1. Project whatcareer.net/en/yellow/class and pick Subjects Allied to Medicine.
  2. Each round: two careers, hands-up vote on which one AI is doing more of, arrow key to record the room's call, space to flip.
  3. After eight rounds, print the lesson record (one click on the final screen).

Discussion prompts, from this subject's live cards

  • Cardiac Nurse measures 6 and Public health analysts measures 55, in the same subject. What is different about the day-to-day tasks?
  • Of the Subjects Allied to Medicine careers shown, 76 have a structural moat. Which moat would you rather stand behind: hands-on work, legal accountability, or people wanting a real person - and why?
  • A high score is a measurement of today, not a prediction of disappearance. What is one job where AI does much of the work and humans still matter more than ever?

Sources on every card: whatcareer.net/en/yellow · exposure blends Anthropic and OpenAI research (2026) with our own UK moat, robotics and science reviews; door grades from OpenAI, "The AI Jobs Transition Framework" (2026, CC BY 4.0). This sheet regenerates from live data; reprint each term rather than filing it.