{"id":31019,"date":"2026-09-02T19:36:38","date_gmt":"2026-09-02T14:06:38","guid":{"rendered":"https:\/\/www.kaspersky.co.in\/blog\/?p=31019"},"modified":"2026-09-02T19:36:38","modified_gmt":"2026-09-02T14:06:38","slug":"teaching-kids-to-use-ai-in-school","status":"publish","type":"post","link":"https:\/\/www.kaspersky.co.in\/blog\/teaching-kids-to-use-ai-in-school\/31019\/","title":{"rendered":"AI for schoolkids: 25 helpful prompts + usage tips"},"content":{"rendered":"

Artificial intelligence has gone from a technological novelty to a tool so ordinary that kids in elementary school are already using it. Trying to ban children from using AI at this point makes about as much sense as it once did to ban search engines or Wikipedia.<\/p>\n

Today we\u2019re breaking down how chatbots actually work, and how to teach your kid to use AI as a study aid rather than a substitute for thinking for themselves<\/p>\n

Explain to your child (and yourself) how AI works<\/h2>\n

Before explaining anything about artificial intelligence to your child, it helps to understand how it actually works and where its limits are.<\/p>\n

Most of the popular chatbots that kids (as well as adults) use are what\u2019s known as large language models, or LLMs. To build such a model, researchers train it to spot patterns across huge amounts of text. The model breaks text down into small units called tokens<\/em> (each token can be a sequence of words, a punctuation mark, a single word or character, or a whole sentence), then predicts which token is most likely to come next.<\/p>\n

\"An<\/a>

This example shows how the GPT-3.5 and GPT-4 models split messages into tokens. Source<\/a><\/p><\/div>\n

The same thing happens when a chatbot converses with a person. A language model doesn\u2019t actually reason on its own. It\u2019s calculating which words and phrases are most likely to follow one another in order to produce a response that fits your query. That means chatbots will sometimes give incomplete or flat-out wrong answers, no matter how often they\u2019re updated or retrained. A vivid example: just two years ago, popular chatbots confidently assured<\/a> users that the word \u201cstrawberry\u201d has two letters \u201cr\u201d instead of three. Mistakes like these are commonly known as hallucinations.<\/p>\n

Many chatbots can also search the internet for information. In that case, the AI pulls in data from an outside source, but it still uses its own internal algorithms to put the answer together. And search results themselves can include outdated posts, advertising content, tabloids, and other questionable sources.<\/p>\n

How to help your child use AI wisely<\/h2>\n

Talk to them about how AI can get things wrong<\/h3>\n

We\u2019ve already seen that artificial intelligence isn\u2019t immune to errors. Unfortunately, hallucinations aren\u2019t the only issue. Even when a chatbot is working from accurate information, it can still present it in a misleading way by leaving something out, putting the emphasis in the wrong place, or drawing a questionable conclusion. Sometimes the result depends on factors a user would never think to consider when asking a question.<\/p>\n

One surprising factor is simply what language you\u2019re using. Most of the data that major models are trained on is in English, so these systems tend to perform better in English. The same AI model might answer a question correctly in English but get it wrong when asked the exact same thing in, say, Serbian. This gap shows up most in fields such as science, medicine, technology, and law, where there\u2019s simply far more high-quality English-language material for the model to draw on.<\/p>\n

Another quirk of chatbots is that they get worse as you feed them more information. Studies show<\/a> that the more text and data a model has to work with, the harder it is for it to keep track of details, connect information from different parts of a conversation, and answer questions that require real depth of understanding. Therefore, uploading an entire book to a chatbot and asking detailed questions about it is a surefire way to trigger hallucinations.<\/p>\n

It\u2019s also worth questioning not just what the AI tells you, but the way you ask your questions in the first place. Chatbots often try to please the user, and will shape their answer to match the user\u2019s apparent point of view. For example, the same chatbot might give opposite answers to \u201cWhy is the Earth overpopulated?\u201d and \u201cWhy isn\u2019t the Earth overpopulated?\u201d, each time building its response around the assumption baked into the question. One recent study found<\/a> that even the most advanced language models failed to challenge false premises in 26\u201342% of cases.<\/p>\n

Let AI help with learning, not do the learning<\/h3>\n

Many a former student knows that copying others\u2019 work is an art unto itself \u2014 one that takes exceptional skill and at least some engagement with the material (sometimes even a fair amount). These days, unfortunately, even that skill is at risk of disappearing: the temptation to copy an answer that took only a few seconds to generate is simply too strong. But chatbots are far from straight-A students themselves.<\/p>\n

In one study, researchers had three popular chatbots sit a chemistry exam, and compared their scores to those of real students from 2009\u20132023. The students came out ahead<\/a>. Chatbots will sometimes land on the correct answer, but arrive at it via a chain of flawed reasoning<\/a>, so their answers could just as easily be wrong.<\/p>\n

So it\u2019s worth explaining to your child that having AI write essays from scratch, or solve equations without then double-checking them, is a dead end. On top of that, in a real job they\u2019ll be judged on actual skills and knowledge, and chatbots won\u2019t help them fake the expertise. What AI can genuinely help with, without getting in the way of learning, is explaining difficult topics and working through mistakes. Here are some examples:<\/p>\n

If a topic is hard to make sense of<\/h4>\n