Encouraging learners to think first, prompt second: Using large language models to learn

Imagine this scenario:

Alex, a teenaged learner, reads a homework assignment, ponders a little, and then opens an AI chatbot. A quick prompt produces a structured, well-written answer within seconds. Alex pastes the text into a document, tweaks a few words, and submits the work.

For many young people, this is becoming the norm.

Recent reports show that, in countries including the US and UK, over half of all teens use AI tools for homework, and 1 in 10 says they do most or all of their homework with chatbots. “I use it every day,” stated one 17-year-old student in a recent study (Pew Research, 2026), and went on to say how she relies on chatbots for everything from homework to life decisions. In another study, a teenager openly admitted to using AI to cheat on assignments, essays, and book reports (Harvard GSE, 2024).

This raises an important question: when AI technology does the work, what happens to the learning?

Researchers are increasingly concerned that such uncritical use of AI tools may lead to cognitive offloading, where learners outsource their thinking, and the weakening of higher-order thinking skills in young people, such as problem-solving, critical thinking and creativity.

To address these concerns, the Raspberry Pi Foundation and Google DeepMind have teamed up to create a new unit of research-informed lessons on large language models (LLMs) as part of Experience AI. Rather than focusing simply on how to use AI tools, the new unit helps learners understand how LLMs work, evaluate their outputs, and use them strategically to support their own learning.

The unit, designed for 13- to 16-year-old learners, supports a learning-focused approach, drawing on metacognition, critical thinking, feedback literacy, and learning-centered prompt engineering. The five lessons in it support young people’s cognitive development, strengthen their metacognitive skills, and bolster their ability to strategically use LLMs for their learning. They draw on extensive research within Google DeepMind’s Learning team on how to scaffold around AI use to preserve ‘productive struggle’ and learner agency.

Research-informed pedagogies

Feedback literacy: Developing learners’ judgement of AI-generated outputs

The new ‘Using LLMs strategically for learning’ unit is grounded in learning science research, including feedback literacy. Feedback literacy refers to the skill of actively interpreting, judging and using information when learning, instead of passively receiving information. 

In the unit, learners explore three types of information, also called ‘feedback types’:

• Telling: When someone is given the answer straight away
• Guiding: When someone is guided with hints
• Challenging: When someone is asked questions that make them think harder

Learners discover that deep learning requires a blend of all three feedback types. They also analyse LLM outputs and discover that LLMs default to providing the answer, usually in a ‘tell’ format. 

To help learners strategically use LLMs for their learning, they are provided with prompting strategies that elicit LLM outputs in ‘guide’ and ‘challenge’ formats. This helps learners engage more deeply with their own learning and develop higher-order thinking skills.

Prompting techniques for learning, not just answers

To help young people strategically use LLMs for their learning, the lessons include learning-centred, platform-agnostic prompting techniques. These include strategies borrowed from research papers on the most effective prompting frameworks. To make sure that the prompting strategies in the lessons remain effective over time and can be translated into different languages when we expand the resources, we did not use acronyms, unlike most popular prompting frameworks.

Building metacognition and future-ready skills

There are several industry reports highlighting the future-proof skills in the age of AI, such as problem solving, communication, critical thinking, collaboration and creativity skills. In the ‘Large language models’ unit, learners reflect on which skills they want to build and consider how their use of LLMs can support or hinder their development, helping them make responsible and intentional choices about using LLMs in ways that build their long-term capabilities rather than replace them.

There are also activities in the lesson resources that help learners understand the long-term impacts of cognitive offloading and uncritical LLM use on the development of their future skills. The resources encourage learners to stay actively engaged in their learning and be reflective of their LLM use when learning.

Uncovering LLM training data

Many young people (Pew Research, 2026) use LLMs as search engines, believing that LLMs produce outputs that are always accurate. To address that misconception, the lessons include activities that question where the training data comes from, and how accurate those sources of data are. 

For example, when young people discover that a portion of data that LLMs are trained on come from social media platforms like Reddit and Facebook, they are encouraged to question the quality and factuality of these sources through classroom-wide discussions. Through discussions and thinking exercises, learners are also encouraged to question whose voices, languages, cultures and perspectives are (and are not) represented in these AI models.

Looking ahead

As AI tools become more integrated in young people’s lives, many learners, like Alex, are tempted to turn to AI tools for quick answers, convenience, and support. 

The ‘Using LLMs strategically for learning’ unit equips young people with the skills to use LLMs effectively, critically and strategically, in ways that are most helpful to their learning. Through research-informed lessons, they explore how LLMs are created, why their outputs are not always accurate and how to evaluate AI-generated responses. 

Just as importantly, learners are encouraged to reflect on their own use of AI: when using an LLM supports their learning, when it does not, and how they can remain in control of their thinking, learning and skills development.

With these resources, young people like Alex are not discouraged from using LLMs, but encouraged to pause, rewrite their prompts in ways that are more helpful to their learning, evaluate the outputs they receive, and stay actively involved in their own thinking. 

As we prepare young people for a future we do not yet know, the most important skill we can teach them is how to keep thinking. Who does the thinking, gets the learning.

The unit is available on the Experience AI website.

Acknowledgements

We would like to thank all the educators across South Africa, Nigeria, Kenya, the United Kingdom, India and beyond who piloted these resources and shared their thoughtful feedback. Their contributions helped shape and strengthen the unit. We are also grateful to Google DeepMind for their continued support in the development of this unit.

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