AI literacy activities for PE lessons

• An algorithm is a set of steps or rules. Machine learning is a way of finding patterns from training data.
• If the examples are limited, or the rules miss something important, the output may be unhelpful.
• The safest habit is human oversight: a person checks the output and remains responsible for the decision.

• Algorithm: a set of step-by-step instructions, like rules that tell a player what to do next.
• Artificial intelligence: a computer system that uses patterns in examples to produce an answer or prediction.
• Training data: the examples used to help a machine learning system find patterns, like practice passes used to teach a system what to recognise.
• Human oversight: people checking an AI system's output before trusting or acting on it.

• UNESCO: Pupils created or tested rules for a basketball-related AI system and applied criteria about accuracy, fairness and human responsibility when judging its output.
• OECD AILit: Pupils explained how rules and examples shape AI outputs and designed a human-controlled use of AI advice.
• PISA 2029: Pupils identified how missing camera information could produce an unfair recommendation and proposed responsible action before it affected team selection.
• EU AI Act: Pupils examined human oversight and accuracy checks for an AI system proposed for a team-selection decision.

A short selection. A full guide maps every activity to the frameworks you choose for your region.

• AI is becoming a "digital coach", from apps that track dribbling speed to automated cameras that follow the ball. These systems are only as good as the data they were trained on.
• Technology isn't magic: it's a system of rules and patterns.
• When an AI fails to recognise a tackle as a foul, it's often because that specific movement wasn't in its training data.

• Computer vision: how a computer "sees" and understands what is happening in a video or photo.
• Training data: thousands of examples given to an AI so it can learn what a good pass or a foul looks like.
• Bias: when an AI makes mistakes because its training data didn't include enough different types of people or movements.
• Algorithm: a set of step-by-step rules an AI follows to make a decision, like "if the ball crosses the line, then signal a goal".

• UNESCO: Students identified how computer vision patterns are used to track physical movement in a sports context.
• OECD AILit: Students explored the governance of AI in sports by role-playing the limitations of algorithmic decision-making.
• PISA 2029: Students debated the ethical implications of automated refereeing, focusing on fairness and accountability.
• Anthropic AI Fluency (4D): Students demonstrated discernment by identifying when AI pose-tracking failed during high-speed movement.

A short selection. A full guide maps every activity to the frameworks you choose for your region.