It’s one thing to track AI infrastructure trends from a desk stacked with research notes, livestreams, and release logs. It’s another to watch your own children – and I have five – slip so easily into ChatGPT, Midjourney, and whatever new tool lands in their feeds that you realize: fluency has already arrived.

I’m also a heavy user of generative AI in my own work, and it’s clear adoption has crossed a threshold. In the mid-2010s, “AI literacy” meant recognizing what a narrow system could and couldn’t do – an image classifier, a toy robot, a chatbot that knew only a handful of intents. Today’s generative models are multimodal, persistent, context-sensitive, and frighteningly good at outputs that read, sound, and look like ours. They don’t just answer prompts; they tilt opportunities and influence decisions.

Generative and other types of AI is no longer something you “learn to use.” It’s the environment we all live in. It’s woven into search ranking, gameplay, homework feedback loops – even those shouted “Alexa, what is…?” questions across the kitchen. And while we’re all hurtling toward AI-augmented cognition at a fast pace, the challenge of learning how to best make use of this technology literacy is being rewritten. Now, it’s about communication, about asking the right questions of the outputs, spotting when information retrieval went sideways, and collaborating with systems that update their knowledge in real time, under the hood, without asking permission or updating the user.

Pause on that. These systems are continuously rewriting themselves as we use them. That’s the world our kids are growing up in. And they should be the architects of that, right?

That means that along with learning how to read and write, youth will need to learn how to have a critical understanding and use of AI, as another crucial literacy. And of course, AI Literacy isn't a brand-new idea. Its foundations were laid over the past decade. But the scale, scope, and urgency of AI literacy have been so dramatically amplified by genAI that, for most people, it feels like a completely new concept.

Which is why we’re launching the AI Literacy Series. Not to water down AI for children, but to reimagine how we – as builders, parents, and interpreters – prepare the next generation to live, think, and create inside an always-on model ecosystem. And how we should build for them. It’s also about how to be an awesome parent with or without prior knowledge about AI and machine learning. 

My partner in this is Stefania Druga, who was building AI literacy long before Attention Is All You Need hit arXiv. In 2012 she founded Hackidemia, an NGO that brought coding and robotics to kids in 73 countries, training over 400 local mentors. By 2016 she was at the MIT Media Lab Scratch team, where she launched Cognimates – a platform where kids could train image classifiers, script chatbots, and program robots to respond to the physical world. It was a playground that showed AI isn’t magic – it’s data, code, and design choices you can touch, tweak, and sometimes break.

There are two ways to approach this series: you can watch or listen to our conversation as-is, old school – as if you are joining us live. Or you can read it here, as a curated reflection on what it means to raise AI-literate kids in a world that’s still figuring out AI itself. Because how we talk to our children about AI will shape how they talk back to it.

Also, please check the Resources section, there is plenty of awesome material.

First of all, What is AI Literacy?

Everyone’s calling for “AI literacy” – in hiring, in education – yet there’s no shared definition, and much of what passes for literacy is already outdated. In 2021, it was enough to know how to trigger a skill on Alexa or train a Teachable Machine model. In 2025, with LLMs embedded in Office, Google Search, iOS, and classroom tools, the baseline is far higher.

In one of her first papers, Stefania defined AI literacy as the ability to read and write with AI. Now, it became the ability to read, write and create with AI. It breaks down into three linked competencies:

But what about cheating?

When there is creation – there is often a challenge of plagiarism. Post-ChatGPT, plagiarism anxiety is front and centre in schools. A recent conversation still plays in my head: a professor of economics, arms flung wide in frustration – “I can’t openly allow my students to use ChatGPT because I don’t know how to control it. But I also can’t stop them. What am I supposed to do?” Druga’s work with Harvard’s Project Zero at the Graduate School of Education offers a brilliant and practical solution. Instead of imposing rigid bans, they co-designed a framework with teens to make the process of using AI transparent and intentional. And it’s a simple as ‘post it’ sticky notes.

It’s called Graidients, it is simple but effective:

Druga has taken this a step further by creating a digital tool that translates the final, agreed-upon "board" into a custom system prompt. When the student uses an LLM for their assignment, this prompt instructs the AI to adhere to the established rules, holding both the student and the AI accountable. This approach brilliantly reframes the "cheating" problem as an opportunity for metacognition and ethical reasoning.

From P-Doom to Practical Empowerment

We also talked about the discourse around AI that is often shadowed by "P-Doom" (Probability of Doom) – the fear of existential risks from superintelligence. While these are important long-term considerations, Druga argues that focusing on them can be paralyzing for young people. When confronted with abstract, massive problems like algorithmic bias or AI-driven surveillance, the natural response is helplessness.

Her solution is to bring these problems down to a tangible, human scale.

Do we all really need to know about AI?

Sometimes parents say something like this, "what if my child wants to be a veterinarian, or a singer, or a farmer. Why do they need AI literacy?"

Druga argues that we are witnessing a fundamental shift in the nature of work. The 20th century prized hyper-specialization. The AI-driven 21st century, however, may favor the generalist. As AI models become incredibly proficient at specialized tasks (like radiology or legal discovery), the premium on human value will shift to skills that AI struggles with:

AI literacy becomes the meta-skill that enables this adaptability. It empowers the veterinarian to use AI for faster diagnostics, the musician to co-create novel melodies with a generative model, and the farmer can use AI to monitor crops and livestock, optimize water and fertilizer use, predict harvest times, control pests precisely, and forecast market prices for better profits. It’s not about replacing their passion – it’s about enriching, augmenting it.

Bill Gates recently advised new graduates to “embrace AI tools” but not expect stability in career paths – the coming decades may involve multiple career shifts and continuous upskilling. This underscores a reality: learning how to learn (often with AI’s help) will be the most important skill of all. In families, this might translate to encouraging kids to follow their curiosity and learn by doing. If your child is interested in cars, maybe they’ll explore how electric vehicles use AI for self-driving; if they love art, perhaps they’ll try an AI image generator to enhance their creativity (while also discussing the ethical issues around AI art). The specific knowledge might evolve, but an attitude of lifelong learning and adaptability will serve them in any field.

What can you do now, at home? 7 activities

The Takeaway: We’re All Learning Together

AI literacy is not (necessarily) about turning every child into an ML engineer. It’s about turning them into AI engineers! Just kidding (but maybe not). It’s about:

The most comforting message from this conversation between me and Stefania is that we're all learning together. Experts, engineers, builders, entrepreneurs, researchers, artists, and parents are all navigating this rapid technological shift in real time.

We can’t have all the answers but we can keep exploring, cultivating curiosity, critical thinking, and co-creation. That is the point and the opportunity. Treat AI as a subject to read, a medium to write with, and a system your kids can reshape.

And that’s the spirit of the AI Literacy Series.

If you try any of the activities, send us your boards, sketches, and home policies. We will fold the best into a living playbook as the series unfolds.

Resources and further reading

Play with AI and ML