How is AI literacy taught?
AI literacy means understanding how systems learn patterns from data, their limits and risks, and responsible use.
Core approach and implementation process
Learners understand that a generative system is a model producing likely outputs, not a person who knows truth. Training data, bias, hallucination, sourcing and privacy are examined through examples. The same prompt can yield different results and outputs require independent verification. Prediction, classification and generation are distinguished.
Classroom practice, monitoring and cautions
Task rules state allowed use, how learners show their own contribution and how assistance is acknowledged. Personal or confidential data are not entered into open systems. AI can support comparison, critique and drafting rather than replace thinking. Access inequality and assessment design are part of ethical use.