Design AI systems with attention to how data sources, selection and information flow influence behaviour and outputs.

What you learn at this level

• Learners identify examples of data that might be used to train AI systems.

• Learners are tasked with designing an AI tool that can sort recyclable materials. In small groups, they decide what data they could collect on the internet to train the AI tool. With support from a teacher, they discuss which aspects of the data would be most important to train an effective and accurate AI tool.

What you learn at this level

• Learners compare the importance of data selection and collection methods in cases when AI is used to make decisions that affect others.

• Learners compare different methods for organising a set of animals, such as grouping them based on physical characteristics. They discuss what happens when new animals are introduced to the set that do not fit into the existing groups.

What you learn at this level

• Learners evaluate how specific properties of a dataset (e.g. size, features, biases) affect the performance and impact of an AI model.

• Learners conduct a class survey to choose a class pet and represent the results using a bar chart. They compare these results with their individual preferences, as well as data from a grade-level survey and a school-wide survey. Learners discuss how increasing the size and diversity of the dataset changes the outcomes of the survey and consider how an AI system might make different recommendations based on its training data.