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ALL SHEETSRUN THE GAME

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 (a Careermash index of how exposed a job's tasks are to AI today; an estimate, not a direct measurement of use, and 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 an estimate of today's exposure, 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 · AI exposure today blends Anthropic and OpenAI research (2026), matched to each job by keyword or subject area, with our own UK moat, robotics and science reviews; entry difficulty is a Careermash rule of thumb, not a forecast. This sheet regenerates from live data; reprint each term rather than filing it.