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CAREERMASH · TEN-MINUTE LESSON SHEET · LANGUAGES AND AREA STUDIES · MEASURED AUGUST 2026

Who does AI come for first in Languages and Area Studies?

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 Languages and Area Studies 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 Languages and Area Studies.
  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

  • Teachers of English as a Foreign Language measures 18 and Language Policy Analyst measures 60, in the same subject. What is different about the day-to-day tasks?
  • Of the Languages and Area Studies careers shown, 2 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 from Anthropic labour market research (2026); 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.