NEERAJ KANT
AI / CLASSROOM IDEAS
Create twelve cards describing imaginary objects. Give each object two visible features, such as shape and colour, and a category label. Decide a clear rule for the first version: for example, all blue objects belong to Group A and all red objects belong to Group B. Use invented data rather than information about real students.
Keep four additional cards aside for testing. Include a combination the class has not seen, such as a blue triangle when the example cards contain only circles and squares.
Show students the labelled example cards. Ask them to propose a rule and explain the evidence for it. Next, reveal the held-back cards without their labels. Students use their proposed rule to predict each group, then compare their predictions with your intended answers.
This is an analogy for supervised learning: a model learns relationships from labelled examples and is evaluated on other examples. Students reasoning about cards are not literally training a computer model, and this activity does not represent every type of AI.
Now remove most red objects from the example set, or introduce an incorrectly labelled card. Ask what evidence remains and where predictions become uncertain. Discuss why representative examples and accurate labels matter.
Do not describe a successful prediction as proof of human-like understanding. Recognising a pattern and explaining the wider world are different tasks.
For an extension, let another group design the examples and test cards. Ask learners to record one error and explain what they learned from it. The goal is to make evidence, testing, and uncertainty part of the conversation from the beginning.