Talking to Kids About AI
\n \t
\n
<\/BR>
\n
\n \t
\n
<\/BR><\/p>\n
<\/a>I\u2019ve had the I have a few rules of thumb I follow when preparing any talk, for any audience. I need to be very clear in my own mind about what information I intend to impart, and what new things the audience should know after they leave, because this shapes everything about what information I\u2019m going to share. I also want to present my material at an appropriate level of complexity for the audience\u2019s preexisting knowledge \u2014 not talking down, but also not way over their heads.<\/p>\n In my day to day life, I\u2019m not necessarily up to speed on what kids already know (or think they know) about AI. I want to make my explanations appropriate to the level of the audience, but in this case I have somewhat limited insight about where they\u2019re coming from already. I have been surprised in some cases that the kids were actually quite aware of things like competition in AI between companies and across international boundaries. A useful exercise when deciding how to frame the content is coming up with metaphors that use concepts or technologies the audience is already very familiar with. Thinking about this also gives you an access point to where the audience is coming from. Beyond that, be prepared to pivot and adjust your presentation approach, if you determine that you\u2019re not hitting the right level. I like to ask kids a little bit about what they think of AI and what they know at the start, so I can start to get that clarity before I\u2019m too far along.<\/p>\n With kids in particular, I\u2019ve got a number of themes I want to cover in my presentations. Regular readers will know\u00a0I\u2019m a big advocate for laypersons being taught what LLMs and other AI models are trained to do<\/a>, and what their training data is, because that is vital for us to set realistic expectations for what the models\u2019 results will be. I think it\u2019s easy for anyone, kids included, to be taken in by the anthropomorphic nature of LLM tone, voice, and even \u201cpersonality\u201d and to lose track of the limitations in reality of what these tools can do.<\/p>\n It\u2019s a challenge to make it simple enough to be age-appropriate, but once you tell them about how training works, and how an LLM learns from seeing examples of written material, or a diffusion model learns from text-image pairs, they can interpolate their own intuition about what the results of that might be. As AI agents become more complex, and the underlying mechanisms get tougher to separate out, it\u2019s important for users to know about the building blocks that lead to this capability.<\/p>\n For myself, I start with explaining training as a general concept, avoiding as much technical jargon as possible. When talking to kids, a little anthropomorphizing language can help make things seem a little less mysterious. For example, \u201cwe give computers lots of information and ask them to learn the patterns inside.\u201d Next, I\u2019ll describe examples of patterns like those in language or image pixels, because \u201cpatterns\u201d by itself is too general and vague. Then, \u201cthose patterns it learns are written down using math, and then that math is what is inside a \u2018model\u2019. Now, when we give new information to the model, it sends us a response that is based on the patterns it learned.\u201d From there, I give another end to end example, and walk through the process of a simplified training (usually a time series model because it\u2019s pretty easy to visualize). After this, I\u2019ll go into more detail about different types of model, and explain what\u2019s different about neural networks and language models, to the degree that\u2019s appropriate for the audience.<\/p>\n I also want to cover ethical issues related to AI. I think kids who are in later elementary or middle grades and up are perfectly capable of understanding the\u00a0environmental<\/a>\u00a0and\u00a0social impacts<\/a>\u00a0that AI can have. Many kids today seem to me to be quite advanced in their understanding of global climate change and the environmental crisis, so talking about how much power, water, and rare mineral usage is required to run LLMs isn\u2019t unreasonable. It\u2019s just important to make your explanations relatable and age appropriate. As I mentioned earlier, use examples that are relatable and connect to the lived experiences of your audience.<\/p>\n Here\u2019s an example of going from kid experience to the environmental impact of AI.<\/p>\n \u201cSo you all have chromebooks to use for homework, right? Do you ever find that when you sit with your laptop on your lap and do work for a long time that the back gets warm? Maybe if you have a lot of files open at once, or watch a lot of videos? So that heating up is the same thing that happens in big computers called servers that run when an LLM is trained or is used, like when you go on chatGPT\u2019s website.<\/em><\/p>\n The data centers that keep chatGPT going are full of servers that are all running simultaneously, and all getting pretty darn hot, which isn\u2019t good for the machinery. So, sometimes these data centers use cool water plus some chemicals together piped through tubes that go right over all the servers, and these help cool off the machines and keep them running. However, this means that a ton of water is being used, mixed with chemicals, and heated up as it goes through these systems, and it can mean that that water isn\u2019t available for people to use for other things like farms or drinking water.<\/em><\/p>\n Other times, these data centers use big air conditioners, which take a lot of electricity to run, which means there may not be enough electricity for our houses or for businesses. Electricity is also sometimes made by burning coal in power plants, which puts out exhaust into the air and increases pollution too. \u201d<\/em><\/p>\n<\/blockquote>\n This brings the kid\u2019s experience into the conversation, and gives them a tangible way to relate to the concept. You can do similar kinds of discussion around copyright ethics and stealing content, using artists and creators familiar to the Children<\/a>, without having to get deep in the weeds of IP law. Deepfakes, both sexual and otherwise, are certainly a topic lots of kids know about too, and it\u2019s important that children are aware of the risks those present to individuals and the community as they use AI.<\/p>\n It can be scary, especially for younger kids, when they start to grasp some of the unethical applications of AI or global challenges it creates, and realize how powerful some of this stuff can be. I\u2019ve had kids ask \u201chow can we fix it if someone teaches AI to do bad things?\u201d, for example. I wish I had better answers for that, because I had to essentially say \u201cAI already sometimes has the information to do bad things, but there are also lots of people working hard to make AI more safe and prevent it from sharing any bad information or instructions on how to do bad things.\u201d<\/p>\n The anthropomorphizing of AI problem is true for adults and kids both \u2013 we tend to trust a friendly, confident voice when it tells us things. A large part of the problem is that the LLM voice telling us things is frequently friendly, confident, and wrong. The concept of media literacy has been an important topic in pedagogy for years now, and\u00a0expanding this to LLMs is a natural progression<\/a>. Just like students (and adults) need to learn to be critical consumers of information generated by other people or corporations, we need to be critical and thoughtful consumers of computer-generated content.<\/p>\n I think this goes along with understanding the tech, too. When I explain that\u00a0an LLM\u2019s job is to learn and replicate human language, at the simplest level by selecting the probable next word in a series based on what came before<\/a>, it makes sense when I go on to say that the LLM can\u2019t understand the idea of \u201ctruth\u201d. Truth isn\u2019t part of the training process, and at the same time truth is a really hard concept even for people to figure out. The LLM might get the facts right frequently, but the blind spots and potential mistakes are going to show up from time to time, by the nature of probability. As a result, kids who use it need to be very conscious of the fallibility of the tool.<\/p>\n This lesson actually has value beyond just the use of AI, however, because what we\u2019re teaching is about dealing with uncertainty, ambiguity, and mistakes. As\u00a0Bearman and Ajjawi (2023)<\/a>\u00a0note, \u201cpedagogy for an AI-mediated world involves learning to work with opaque, partial and ambiguous situations, which reflect the entangled relationships between people and technologies.\u201d I really like this framing, because it comes back around to something I think about a lot \u2014 that LLMs are created by humans and reflect back interpretations of human-generated content. When kids learn how models come to exist, that models are fallible, and that their output originates from human-generated input, they\u2019re getting familiar with the blurry nature of how technology works today in our society more broadly. (In fact, I highly recommend the article in full for anyone who\u2019s thinking about how to teach kids about AI themselves.)<\/p>\n As I\u2019ve written about before<\/a>, the profusion of deepfake\/\u201dAI slop\u201d video and image content online creates a lot of difficult questions. This is another area where I think giving kids information is important, because it\u2019s easy to absorb misinformation or outright lies through convincing visual content. This content is also one step away from the actual creation process for most kids, as a lot of this material is being shared widely on social media, and is unlikely to be labeled. Talking to kids about what tell-tale signs help to detect AI generated material can help, as well as general critical media literacy skills like \u201cif it\u2019s too good to be true, it\u2019s probably fake\u201d and \u201cdouble check things you hear in this kind of post\u201d.<\/p>\n However much we explain the ethical issues and the risks that the LLM will be wrong, these AI tools are incredibly useful and seductive, so it\u2019s understandable that some kids will resort to using them to cheat on homework and in school. I\u2019d like to say that we need to just reason with them, and explain that learning the skills to do the homework is the point, and if they don\u2019t learn it they\u2019ll be missing capabilities they need for future grades and later life\u2026 but we all know that kids are very rarely that logical. Their brains are still developing, and this sort of thing is hard even for adults to reason about at times.<\/p>\n There are two approaches you might take, essentially: find ways to make schoolwork harder or impossible to cheat on, or incorporate AI into the classroom under the assumption that kids are going to have it at their disposal in the future. Now, monitored work in a classroom setting can give kids a chance to learn some skills they need to have without digital mediation. However, as I mentioned earlier, media literacy really has to include LLMs now, and I think supervised use of LLMs by an informed instructor can have plenty of pedagogical value. In addition, it\u2019s really impossible to \u201cAI-proof\u201d homework that\u2019s done outside of direct instructor supervision, and we should recognize that. I don\u2019t want to make it sound like this is easy, however \u2014 see below in the\u00a0Further Reading<\/strong>\u00a0section for a number of scholarly articles on the broad challenges of teaching AI literacy in the classroom. Teachers have a very challenging task to try not only to keep up on the technology themselves and evolve their pedagogy to fit the times, but also to try and give their students the information they need to use AI responsibly.<\/p>\n In the end, the question is what exactly we ought to be recommending kids do and not do in a world that contains AI, in the classroom and beyond. I\u2019m rarely an advocate for banning or prohibition of ideas, and I think the example of science-based, age-appropriate comprehensive sex Education<\/a> presents a good lesson. If children are not given accurate information about their bodies and sexuality, they don\u2019t have the knowledge necessary to make informed, responsible decisions in that area.\u00a0We learned this when abstinence-only sex ed made teen pregnancy rates<\/a>\u00a0go through the roof in the early 2000\u2019s.<\/a>\u00a0Adults will not be present to enforce mandates when kids are making the difficult decisions about what to do in challenging circumstances, so we need to make sure the kids are equipped with the information required to make those decisions responsibly themselves, and this includes ethical guidance but also factual information.<\/p>\n One last thing that I think is important to mention is that adults should be modeling responsible behavior with AI too. If teachers, parents, and other adults in kids\u2019 lives are not critically literate about AI, then they aren\u2019t going to be able to teach kids to be critical and thoughtful consumers of this technology either.<\/p>\n A\u00a0recent New York Times story<\/a>\u00a0about how teachers use AI made me a little frustrated. The article doesn\u2019t reflect a great understanding of AI, conflating it with some basic statistics (a teacher analyzing student data to help personalize his teaching to their levels is both not AI and not new or problematic), but it does start a conversation about how adults in kids\u2019 lives are using AI tools, and it mentions the need for those adults to model transparent and critical uses of it. (It also briefly grazes the issue of for-profit industry pushing AI into the classroom, which seems like a problem deserving more time \u2014 maybe I\u2019ll write about that down the road.)<\/p>\n To counter one assertion of the piece, I wouldn\u2019t complain about teachers using LLMs to do a first pass at grading written material, as long as they are monitoring and validating the output. If the grading criteria are around grammar, spelling, and writing mechanics, an Llm<\/a> is probably suitable based on how it\u2019s trained. I wouldn\u2019t want to blindly trust an LLM on this without a human taking at least a quick look, but human language is in fact what it\u2019s designed to understand. The idea that \u201cthe student had to write it, so the teacher should have to grade it\u201d is silly, because the purpose of the exercise is for the student to learn. Teachers already know the writing mechanics, this is not a project that is meant to force teachers to learn something that is only achievable by manually grading. I think the NYT knows this, and that the framing was mostly for clickbait purposes, but it\u2019s worth saying clearly.<\/p>\n This point goes back, once again, to my earlier section about understanding the technology. If you confidently understand what the training process looks like, then you can decide whether that process would produce a tool that\u2019s capable of managing a task, or not. But automating grading has been part of schooling for decades at least \u2014 anyone who\u2019s filled out a scantron sheet knows that.<\/p>\n This technology\u2019s development is forcing some amount of adaptation in our education system, but we can\u2019t put that genie back in the bottle now. There are definitely some ways that AI can have positive effects on education (often cited examples are personalization and saving teachers time that can then be put towards direct student services), but as with most things I\u2019m an advocate for a realistic view. As I believe most educators are only too well aware, education can\u2019t just go on as it did before LLMs entered our lives.<\/p>\n Kids are smarter than we sometimes give them credit for, and I think they are capable of understanding a lot about what AI means in our world. My advice is to be transparent and forthright about the realities of the technology, including advantages and disadvantages it represents to us as individuals and to our broader society. How we use it ourselves will model to kids either positive or negative choices that they\u2019re going to notice, so being thoughtful about our actions as well as what we say is key.<\/p>\n For more of my work, visit\u00a0www.stephaniekirmer.com<\/a>.<\/p>\n If you\u2019d like to learn more about Skype a Scientist, visit\u00a0https:\/\/www.skypeascientist.com\/<\/a><\/p>\n https:\/\/www.nytimes.com\/2025\/04\/14\/us\/schools-ai-teachers-writing.html<\/a><\/p>\n https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC3194801<\/a><\/p>\n https:\/\/www.nyu.edu\/about\/news-publications\/news\/2022\/february\/federally-funded-sex-education-programs-linked-to-decline-in-tee.html<\/a><\/p>\n https:\/\/www.stephaniekirmer.com\/writing\/environmentalimplicationsoftheaiboom<\/a><\/p>\n https:\/\/www.stephaniekirmer.com\/writing\/seeingourreflectioninllms<\/a><\/p>\n https:\/\/www.stephaniekirmer.com\/writing\/machinelearningspublicperceptionproblem<\/a><\/p>\n https:\/\/www.stephaniekirmer.com\/writing\/whatdoesitmeanwhenmachinelearningmakesamistake<\/a><\/p>\n https:\/\/bera-journals.onlinelibrary.wiley.com\/doi\/full\/10.1111\/bjet.13337<\/a><\/p>\n https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2666920X21000357<\/a><\/p>\n https:\/\/www.stephaniekirmer.com\/writing\/theculturalimpactofaigeneratedcontentpart1<\/a><\/p>\n For anyone who\u2019s teaching these topics or would like a deeper dive, here are a few articles I found interesting as I was researching this.<\/p>\n https:\/\/bera-journals.onlinelibrary.wiley.com\/doi\/full\/10.1111\/bjet.13337<\/a><\/p>\n https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3408877.3432530<\/a>\u00a0\u2014 an early college level curriculum study<\/p>\n https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2666920X22000169<\/a>\u00a0\u2014 a preschool\/early elementary level curriculum study<\/p>\n https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3311890.3311904<\/a>\u00a0\u2014 analysis of SES and national variation in AI learning among young children<\/p>\n \n The post Talking to Kids About AI<\/a> appeared first on Towards Data Science<\/a>.<\/p>\n<\/div>\nPreparing to Explain Something<\/h2>\n
Understanding the Technology<\/h2>\n
AI Ethics and Externalities<\/h2>\n
\n
Unpacking the Idea of \u201cTruth\u201d<\/h2>\n
A side note on images and video<\/h3>\n
Cheating<\/h2>\n
Learning from the Example of Sex Ed<\/h2>\n
Modeling Responsibility<\/h2>\n
Conclusion<\/h2>\n
\n
\nFurther Reading<\/h2>\n
Additional Articles about Pedagogical Approaches to AI<\/h2>\n