dale lane

Generative AI is fast becoming an everyday tool across almost every field and industry. Teaching children about it should include an understanding of how it works, how to think critically about its risks and limitations, and how to use it effectively. In this post, I’ll share how I’ve been doing this.

My approach is to take students through six projects that give a practical and hands-on introduction to generative AI through Scratch. The projects illustrate how generative AI is used in the real world, build intuitions about how models generate text, show how settings and prompts shape the output, highlight the limits (and risks) of “confidently wrong” answers, and introduce some practical techniques to make AI systems more reliable.

As with the rest of what I’ve done with Machine Learning for Kids, my overall aim is AI literacy through making. Students aren’t just told what language models are – they go through a series of exercises to build, test, break, and improve generative AI systems in Scratch. That hands-on approach helps to make abstract ideas (like “context” or “hallucination”) more visible and memorable.

I’ve long described Scratch as a safe sandbox, and this makes it ideal to experiment with the sorts of generative AI concepts they will encounter in daily tools such as chatbots, writing assistants, translation apps, and search experiences.

Core themes

Across the six projects, students repeatedly encounter three core questions:

1. How does a model decide what to say next?
Students learn that language models generate text one word at a time, guided by patterns in data and the recent conversation (“context”).

2. Why do outputs vary, and how can we steer them?
Students discover how settings and prompting techniques can balance creativity vs reliability, and how “good prompting” is about being clear on the job you want done.

3. When should we not trust a model, and what do we do then?
Students experiment with hallucinations, outdated knowledge, semantic drift, and bias. They practise mitigations to these problems, such as retrieval (adding trusted information) and benchmarking (testing systematically).

All of this is intended to be a (much simplified!) mirror of real-world practice. Professional uses of generative AI combine generation (writing), grounding (bringing in trusted sources), instruction (prompting), and evaluation (testing and comparing). Children can be introduced to all of these aspects through hands-on experiences.

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