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Computers & Digital Literacy · Grade 8

AI Literacy, Bias and Human Judgment

Learners check a fictional plant-disease app's predictions against an expert and discover that a high confidence score is not the same as being right. They count false positives and false negatives in a school spam filter and weigh which error costs more, examine how people over-trust automated suggestions, and design human review with real appeals. Finally they compare error rates across groups and decide when to rely on, check or reject an AI suggestion based on the stakes.

  • 4 lessons
  • About 105 minutes
  • AI judgment check: 10 questions
  • Ways to answer: choose an answer, highlight a text, match pairs, type a number, put in order and sort into groups
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What learners will discover

  • Explain that a confidence score is a model's own estimate, not a guarantee, and find confidently wrong predictions in a results table.
  • Identify false positives and false negatives in plain language, count them in a small table, and compare what each kind of error costs in a situation.
  • Recognise automation bias and design meaningful human review and appeals for an automated suggestion system.
  • Explain how error rates can differ across groups, and decide whether to rely on, check or reject an AI suggestion based on the stakes.

Lessons

  1. Confidence is not correctness 6 steps4 practiceabout 24 min
  2. False positives and false negatives 7 steps5 practiceabout 26 min
  3. Automation bias and real human oversight 6 steps4 practiceabout 26 min
  4. Who bears the errors? Groups and stakes 7 steps4 practiceabout 29 min
  5. AI judgment check 10 questions at the end

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