For learning, the useful habit is to make an initial attempt before asking an AI chatbot for help—then judge the response and decide what to do with it. A polished answer can finish an assignment while bypassing the thinking the task is meant to develop. The goal is not to ban large language models (LLMs), but to help learners use them deliberately and stay in control of their learning.
What “think first, prompt second” means
It is a sequence for using an LLM as a learning aid rather than treating it as an answer machine: understand the task, try some of the thinking yourself, ask for help suited to your goal, and assess whether the response is useful. Afterward, consider whether the exchange helped you learn or simply let you avoid the effort.
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The Raspberry Pi Foundation describes this approach in a five-lesson Experience AI unit for learners aged 13–16, developed with Google DeepMind. Its 28 September 2026 article presents the lessons as a way to understand LLMs, evaluate their outputs, and use them to support learning—not as a blanket prohibition on AI. Read the Raspberry Pi Foundation’s overview of the unit.
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Choose the kind of help that fits the learning goal
An LLM may provide a direct answer, offer a hint, or ask a question that pushes the learner to think more deeply. The right choice depends on whether the goal is simply to complete a task or to understand and practise a skill.
#1 Best Overall
| Kind of assistance | What it does | Best fit |
|---|---|---|
| Telling | Supplies the answer or solution. | Checking a result or resolving a point when the learner already understands the reasoning. It is a poor substitute for practice when the task is intended to build that reasoning. |
| Guiding | Offers hints or support without doing all the work. | Getting unstuck while continuing to solve the problem yourself. |
| Challenging | Asks questions that invite explanation, reconsideration, or deeper thought. | Testing understanding, exploring alternatives, or extending an idea. |
LLMs often default to telling. Learners can ask explicitly for a hint or for questions instead of a complete solution. For example: “Give me one hint at a time, and don’t reveal the answer unless I ask.” Or: “Ask me questions that help me check my reasoning; don’t solve it for me.” These requests are practical applications of the unit’s guiding and challenging approaches, not a guarantee that a model will follow them perfectly.
Evaluate the response instead of accepting it
An LLM can sound confident and produce fluent text while still being inaccurate. Treat its output as something to examine, not as proof. Compare claims with reliable sources, check any reasoning or calculations, and ask whether the response actually addresses the task. If you cannot explain the answer in your own words, you may not yet understand it.
Consider, too, where an answer may come from. Training data can reflect uneven source quality and may overrepresent some languages, cultures, and perspectives while leaving others less visible. An answer that sounds neutral is not necessarily complete or representative. For topics where perspective matters, look for whose experience is included and whose may be missing.
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After using an LLM, ask what changed: Can you now explain the idea, solve a similar problem, or improve your own draft? Or did the tool produce work you could not reproduce or defend? The distinction matters because cognitive offloading—the transfer of thinking to a tool—can save effort, but may also displace the practice a learning task is designed to provide.
- Likely supporting learning: the response gives a useful hint, helps you identify a gap, or prompts you to explain and refine your reasoning.
- Likely replacing learning: the tool completes the central thinking, and you submit or rely on its output without being able to evaluate or explain it.
This is a decision to make in context, not a rule that every AI use is helpful or harmful. The key question is: when AI technology does the work, what happens to the learning?
A practical routine for learners and teachers
- Clarify the task. Identify what you are meant to learn or practise, not only what you need to hand in.
- Make an initial attempt. Write down what you know, try a first step, or identify the part you cannot yet solve.
- Ask for a fitting kind of help. Request a hint to keep practising, a challenging question to test understanding, or a direct answer only when that serves the goal.
- Check the output. Look for inaccuracies, unsupported claims, gaps, and perspectives that may be missing.
- Put the help to work yourself. Revise your attempt, explain the idea in your own words, or solve a related example without copying the response.
- Reflect. Decide whether the interaction developed your understanding or did the thinking for you.
The unit’s prompting strategies are described as platform-agnostic and avoid dependence on acronyms. That makes the underlying habit—state what kind of help you need, then evaluate what you get—more transferable across tools and languages than memorising a platform-specific formula.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the reported teen-use figures do—and do not—show
The Raspberry Pi Foundation’s 28 September 2026 article reports that more than half of teens in the US and UK use AI tools for homework, and that one in 10 says they do most or all of their homework with chatbots. It attributes these figures to recent reports including Pew Research, 2026. The article does not identify the underlying study details, sample, field dates, country-by-country breakdown, or definitions, so these should be read as figures reported by the Foundation, not as independently verified prevalence estimates.
The same article attributes “I use it every day” to a 17-year-old in a Pew Research 2026 study, but the underlying report is not identified in enough detail to assess the quotation’s context. It also mentions a Harvard GSE 2024 study in connection with a teenager’s account of using AI to cheat on schoolwork; that example is not evidence of how common cheating is.
Best Value
These caveats do not change the practical classroom question: how can learners use tools they encounter without surrendering the thought and practice that help them learn?
Where teachers can start
Teachers looking for a structured resource can explore the Experience AI programme and its five-lesson unit for ages 13–16. The approach offers a way to discuss AI use through concrete choices—answer, hint, or challenge—along with accuracy, representation, and reflection, rather than reducing the conversation to permission or prohibition.
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