Computing

Identifying computers in everyday life — not just laptops but phones, tablets, smart speakers, traffic lights, washing machines; what makes something a computer

Spotting AI in daily life: face unlock on a phone, video recommendations, spelling auto-correct, automatic doors that detect people; technology that seems to 'know' things

What happens when you talk to Alexa, Siri, or Google Assistant; they listen, try to understand, look up answers; sometimes they get it wrong; they are tools, not alive

Sorting objects into 'smart' (can sense and respond) and 'not smart' (just sits there); a toaster vs a smart speaker; introduction to the idea that some machines can sense and respond to the world

Step-by-step instructions for everyday tasks (making a sandwich, brushing teeth); that if instructions are wrong or missing, things go wrong; computers follow instructions exactly

What a robot really is — not the sci-fi version; robots in factories, robot vacuum cleaners, robot arms in surgery; that robots follow instructions given by people

What data is: information that computers use — numbers, words, pictures, sounds; everything a computer knows comes from data that people give it

Humans are great at spotting patterns; computers can learn to spot patterns too, but they need lots of examples; sorting and classification activities as the basis of machine learning

How machine learning works at a conceptual level: show the computer many examples, it finds patterns, then it makes predictions about new things; hands-on experience with Teachable Machine or similar tool

Machines make mistakes; they only know what they've been shown; bad training data leads to bad results; AI is not magic — just maths on data; showing edge cases and failures

Comparing human and machine capabilities: creativity, empathy, common sense vs speed, memory, repetition; the Turing Test (simplified); what makes humans unique

How recommendation systems work: YouTube, Netflix, and shop websites track what you click and find patterns; filter bubbles; the difference between helpful suggestions and manipulation

How computer game characters 'decide' what to do; simple rule-based AI vs learning AI; NPCs, difficulty adjustment; AI as the opponent in chess or board games

What AI might do in 10 years; what we want it to do and what we're worried about; children as future designers and decision-makers about AI; hopeful, empowered framing

AI needs huge amounts of energy and water to train; data centres and their environmental cost; but AI can also help — predicting weather, monitoring deforestation, optimising energy; trade-offs

What data AI systems collect about you; who has it and why it matters; cookies, tracking, smart speakers always listening; your data is valuable

Whether AI should make important decisions about people: jobs, loans, justice; who is responsible when AI makes unfair decisions; introduction to algorithmic fairness

How AI is changing the world of work: some jobs disappear, new ones are created, many change; jobs AI can't do (yet); what skills matter in an AI world

Design thinking applied to AI ethics: if you were designing an AI system, what rules would you give it? Who should it help? What should it not be allowed to do?

If training data is biased, AI will be biased; examples: facial recognition working better for some skin tones, translation assuming gender; where bias comes from and whether we can fix it

Deepfakes, AI-generated images and text; how to spot them and why they matter; the importance of checking sources; not everything online is real