Introduction
So, you want to teach your students about AI. But it’s hard to know how to teach them, or what to teach them, when it feels like the landscape is changing minute-by-minute, when there are continuous astounding breakthroughs* (*if you believe the tech news), and in general it seems like a full time job to keep up!
Well, teacher, friend, colleague – welcome home. Take a deep breath. This book is for you.
This book describes a values driven, ethical approach to AI. We want to emphasise the potential for AI to contribute to social and environmental progress, and human flourishing over machine/corporate flourishing. Sometimes this means embracing AI, sometimes it means rejecting it for some purposes.
The field of AI is larger than any single application. It’s more than Large Language Models and the chatbots they power. There are many specific applications of AI that solve real and pressing problems. There are also many specific applications of AI that cause real and present harms. How do we identify the harms? How do we recognise the solutions? How do we invest, as a society, in AI that assists human flourishing, rather than machine or business flourishing? What even IS AI??? And what isn’t? How can we use these systems responsibly, and figure out a path through all of the different tools and technologies?
We wanted to create something detailed enough to be meaningful, but general enough to still be useful in 1, 2 or 10 years time. We’ll acknowledge, whenever possible, when our statements are specific to this moment, as opposed to formalisms or foundational knowledge which should apply for any iteration of this technology.
By the way, the fact that the field is hard to parse as an non-expert, or to be frank as an expert – this is a feature, not a bug. The hype cycle, the marketing from inside the companies, the breathless coverage by the tech press from outside the companies – this is all part of a classic Gartner hype-cycle, where technology is introduced, marketing hype builds expectations to a frenzy, and then the technology turns out to fall disappointingly short of the hype. Eventually the cycle levels out to a place where the technology is useful and not over hyped. The only remarkable thing about the AI hype cycle is how deeply boring and predictable it all is.
About this resource
- We have designed this book so that you can use it in whatever way suits you. Dip into particular sections that you’re curious about, or read it from start to finish. Let us know what works for you!
- Technology changes over time, and it is entirely possible that parts of this book will become obsolete. What AI is capable of is changing all the time, but the ethical issues are omnipresent. We talk about technology as it stands today, but we don’t claim to predict what will be possible next year, or ten years from now.
- This book is a mixture of research, data, and our personal and professional opinions and perspectives. Our framing will often be different to the prevailing conversations about AI, but we feel this is a positive. It’s important that there are robust and critical conversations about new technology, and that you have the resources to share many different ideas and approaches with your students. This field is a wild mixture of genuine technical advancements and radically unrealistic marketing hype, and it can be incredibly difficult to untangle the two. This is our effort to give you references, ideas, and thinking tools. Some of these will be out of date quickly, some, especially the ethical considerations, are likely to have lasting value.
- Some sections have been deeply collaborative, and some much more written by Linda, and some by Laura. We stand behind the worth of the book, and we’re also human and will make mistakes, so please feel free to let us know, particularly if you can point us to robust references! We ask that you engage in the spirit of scholarly debate to improve the discourse.
- We are planning to release this book section by section, as a free, online resource. If you have comments, suggestions, or need more explanations for some things, please let us know.
People Friendly: Teachers’ Guide to AI © The Australian Data Science Education Institute 2025
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