Connexion
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.
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’:
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.
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.
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.
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.
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.
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.
The post Encouraging learners to think first, prompt second: Using large language models to learn appeared first on Raspberry Pi Foundation.
As computing technologies continue to rapidly evolve in today’s digital world, computing education is becoming increasingly essential. Arto Hellas and Juho Leinonen, researchers at Aalto University in Finland, are exploring how innovative teaching methods can equip students with the computing skills they need to stay ahead. In particular, they are looking at how generative AI tools can enhance university-level computing education.
In our monthly seminar in September, Arto and Juho presented their research on using AI tools to provide personalised learning experiences and automated feedback to help requests, as well as their findings on teaching students how to write effective prompts for generative AI systems. While their research focuses primarily on undergraduate students — given that they teach such students — many of their findings have potential relevance for primary and secondary (K-12) computing education.
Generative AI consists of algorithms that can generate new content, such as text, code, and images, based on the input received. Ever since large language models (LLMs) such as ChatGPT and Copilot became widely available, there has been a great deal of attention on how to use this technology in computing education.
Arto and Juho described generative AI as one of the fastest-moving topics they had ever worked on, and explained that they were trying to see past the hype and find meaningful uses of LLMs in their computing courses. They presented three studies in which they used generative AI tools with students in ways that aimed to improve the learning experience.
An important strand of computing education research investigates how to engage students by personalising programming problems based on their interests. The first study in Arto and Juho’s research took place within an online programming course for adult students. It involved developing a tool that used GPT-4 (the latest version of ChatGPT available at that time) to generate exercises with personalised aspects. Students could select a theme (e.g. sports, music, video games), a topic (e.g. a specific word or name), and a difficulty level for each exercise.
Arto, Juho, and their students evaluated the personalised exercises that were generated. Arto and Juho used a rubric to evaluate the quality of the exercises and found that they were clear and had the themes and topics that had been requested. Students’ feedback indicated that they found the personalised exercises engaging and useful, and preferred these over randomly generated exercises.
Arto and Juho also evaluated the personalisation and found that exercises were often only shallowly personalised, however. In shallow personalisations, the personalised content was added in only one sentence, whereas in deep personalisations, the personalised content was present throughout the whole problem statement. It should be noted that in the examples taken from the seminar below, the terms ‘shallow’ and ‘deep’ were not being used to make a judgement on the worthiness of the topic itself, but were rather describing whether the personalisation was somewhat tokenistic or more meaningful within the exercise.
The findings suggest that this personalised approach may be particularly effective on large university courses, where instructors might struggle to give one-on-one attention to every student. The findings further suggest that generative AI tools can be used to personalise educational content and help ensure that students remain engaged.
How might all this translate to K-12 settings? Learners in primary and secondary schools often have a wide range of prior knowledge, lived experiences, and abilities. Personalised programming tasks could help diverse groups of learners engage with computing, and give educators a deeper understanding of the themes and topics that are interesting for learners.
Another key aspect of Alto and Juho’s work is exploring how LLMs can be used to generate responses to students’ requests for help. They conducted a study using an online platform containing programming exercises for students. Every time a student struggled with a particular exercise, they could submit a help request, which went into a queue for a teacher to review, comment on, and return to the student.
The study aimed to investigate whether an LLM could effectively respond to these help requests and reduce the teachers’ workloads. An important principle was that the LLM should guide the student towards the correct answer rather than provide it.
The study used GPT-3.5, which was the newest version at the time. The results found that the LLM was able to analyse and detect logical and syntactical errors in code, but concerningly, the responses from the LLM also addressed some non-existent problems! This is an example of hallucination, where the LLM outputs something false that does not reflect the real data that was inputted into it.
The finding that LLMs often generated both helpful and unhelpful problem-solving strategies suggests that this is not a technology to rely on in the classroom just yet. Arto and Juho intend to track the effectiveness of LLMs as newer versions are released, and explained that GPT-4 seems to detect errors more accurately, but there is no systematic analysis of this yet.
In primary and secondary computing classes, young learners often face similar challenges to those encountered by university students — for example, the struggle to write error-free code and debug programs. LLMs seemingly have a lot of potential to support young learners in overcoming such challenges, while also being valuable educational tools for teachers without strong computing backgrounds. Instant feedback is critical for young learners who are still developing their computational thinking skills — LLMs can provide such feedback, and could be especially useful for teachers who may lack the resources to give individualised attention to every learner. Again though, further research into LLM-based feedback systems is needed before they can be implemented en-masse in classroom settings in the future.
Finally, Arto and Juho presented a study where they introduced the idea of ‘Prompt Problems’: programming exercises where students learn how to write effective prompts for AI code generators using a tool called Promptly. In a Prompt Problem exercise, students are presented with a visual representation of a problem that illustrates how input values will be transformed to an output. Their task is to devise a prompt (input) that will guide an LLM to generate the code (output) required to solve the problem. Prompt-generated code is evaluated automatically by the Promptly tool, helping students to refine the prompt until it produces code that solves the problem.
Feedback from students suggested that using Prompt Problems was a good way for them to gain experience of using new programming concepts and develop their computational thinking skills. However, students were frustrated that bugs in the code had to be fixed by amending the prompt — it was not possible to edit the code directly.
How these findings relate to K-12 computing education is still to be explored, but they indicate that Prompt Problems with text-based programming languages could be valuable exercises for older pupils with a solid grasp of foundational programming concepts.
At the end of the presentation, Arto and Juho summarised their work and hypothesised that as society develops more and more AI tools, computing classrooms may lose some of their community aspects. They posed a very important question for all attendees to consider: “How can we foster an active community of learners in the generative AI era?”
In our breakout groups and the subsequent whole-group discussion, we began to think about the role of community. Some points raised highlighted the importance of working together to accurately identify and define problems, and sharing ideas about which prompts would work best to accurately solve the problems.
As AI technology continues to evolve, its role in education will likely expand. There was general agreement in the question and answer session that keeping a sense of community at the heart of computing classrooms will be important.
The Raspberry Pi Computing Education Research Centre and Faculty of Education at the University of Cambridge have recently published a teacher guide on the use of generative AI tools in education. The guide provides practical guidance for educators who are considering using generative AI tools in their teaching.
In our current seminar series, we are exploring how to teach programming with and without AI technology. Join us at our next seminar on Tuesday, 12 November at 17:00–18:30 GMT to hear Nicholas Gardella (University of Virginia) discuss the effects of using tools like GitHub Copilot on the motivation, workload, emotion, and self-efficacy of novice programmers. To sign up and take part in the seminar, click the button below — we’ll then send you information about joining. We hope to see you there.
The schedule of our upcoming seminars is online. You can catch up on past seminars on our previous seminars and recordings page.
The post Using generative AI to teach computing: Insights from research appeared first on Raspberry Pi Foundation.
At the Raspberry Pi Foundation, we believe everyone belongs in computer science, and that it is a much more varied field than is commonly assumed. One of the ways we want to promote inclusivity and highlight the variety of skills and interests needed in computer science is through our ‘I belong’ campaign. We do this because the tech sector lacks diversity. Similarly, in schools, there is underrepresentation of students in computing along the axes of gender, ethnicity, and economic situation. (See how researchers describe data from England, and data from the USA.)
The ‘I belong’ campaign is part of our work on Isaac Computer Science, our free online learning platform for GCSE and A level students (ages 14 to 18) and their teachers, funded by the Department for Education. The campaign celebrates young computer scientists and how they came to love the subject, what their career journey has been so far, and what their thoughts are about inclusivity and belonging in their chosen field.
These people are role models who demonstrate that everyone belongs in computer science, and that everyone can bring their interests and skills to bear in the field. In this way, we want to show young people that they can do much more with computing than they might think, and to inspire them to consider how computing could be part of their own life and career path.
Salome is studying Computer Science with Digital Technology Solutions at the University of Leeds and doing a degree apprenticeship with PricewaterhouseCoopers (PwC).
“I was quite lucky, as growing up I saw a lot about women in STEM which inspired me to take this path. I think to improve the online community, we need to keep challenging stereotypes and getting more and more people to join, thereby improving the diversity. This way, a larger number of people can have role models and identify themselves with someone currently there.”
“Another thing is the assumption that computer science is just coding and not a wide and diverse field. I still have to explain to my friends what computer science involves and can become, and then they will say, ‘Wow, that’s really interesting, I didn’t know that.'”
Devyani is a third-year degree apprentice at Cisco.
“It was at A level where I developed my programming skills, and it was more practical rather than theoretical. I managed to complete a programming project where I utilised PHP, JavaScript, and phpMyAdmin (which is a database). It was after this that I started looking around and applying for degree apprenticeships. I thought that university wasn’t for me, because I wanted a more practical and hands-on approach, as I learn better that way.”
“At the moment, I’m currently doing a product owner role, which is where I hope to graduate into. It’s a mix between both a business role and a technical role. I have to stay up to speed with the current technologies we are using and developing for our clients and customers, but also I have to understand business needs and ensure that the team is able to develop and deliver on time to meet those needs.”
Omar is a Mexican palaeontologist who uses computer science to study dinosaur bones.
“I try to bring aspects that are very well developed in computer science and apply them in palaeontology. For instance, when digitising the vertebrae, I use a lot of information theory. I also use a lot of data science and integrity to make sure that what we have captured is comparable with what other people have found.”
“What drove me to computers was the fact you are always learning. That’s what keeps me interested in science: that I can keep growing, learn from others, and I can teach people. That’s the other thing that makes me feel like I belong, which is when I am able to communicate the things I know to someone else and I can see the face of the other person when they start to grasp a theory.”
Tasnima is a computer science graduate from Queen Mary University of London, and is currently working as a software engineer at Credit Suisse.
“During the pandemic, one of the good things to come out of it is that I could work from home, and that means working with people all over the world, bringing together every race, religion, gender, etc. Even though we are all very different, the one thing we all have in common is that we’re passionate about technology and computer science. Another thing is being able to work in technology in the real world. It has allowed me to work in an environment that is highly collaborative. I always feel like you’re involved from the get-go.”
“I think we need to also break the image that computer science is all about coding. I’ve had friends that have stayed away from any tech jobs because they think that they don’t want to code, but there’s so many other roles within technology and jobs that actually require no coding whatsoever.”
Aleena is a software engineer who works at a health tech startup in London and is also studying for a master’s degree in AI ethics at the University of Cambridge.
“I do quite a lot of different things as an engineer. It’s not just coding, which is part of it but it is a relatively small percentage, compared to a lot of other things. […] There’s a lot of collaborative time and I would say a quarter or third of the week is me by myself writing code. The other time is spent collaborating and working with other people and making sure that we’re all aligned on what we are working on.”
“I think it’s actually a very diverse field of tech to work in, once you actually end up in the industry. When studying STEM subjects at a college or university level it is often not very diverse. The industry is the opposite. A lot of people come from self-taught or bootcamp backgrounds, there’s a lot of ways to get into tech and software engineering, and I really like that aspect of it. Computer science isn’t the only way to go about it.”
Alice is a final-year undergraduate student of Computer Science with Artificial Intelligence at the University of Brighton. She is also the winner of the Global Challenges COVID-19 Research Scholarship offered by Santander Universities.
“[W]e need to advertise computer science as more than just a room full of computers, and to advertise computer sciences as highly collaborative. It’s very creative. If you’re on a team of developers, there’s a lot of communication involved.”
“There’s something about computer science that I think is so special: the fact that it is a skill anybody can learn, regardless of who they are. With the right idea, anybody can build anything.”
Help us spread the message that everyone belongs in computer science: share this blog with schools, teachers, STEM clubs, parents, and young people you want to inspire.
Whether you’re studying or teaching computer science GCSE or A levels in the UK (or thinking about doing so!), or you’re a teacher or student in another part of the world, Isaac Computer Science is here to help you achieve your computer science goals. Our high-quality learning platform is free to use and open to all. As a student, you can register to keep track of your progress. As a teacher, you can sign up to guide your students’ learning.
And for younger learners, we have lots of fun project guides to try out coding and creating with digital technologies.
The post I belong in computer science appeared first on Raspberry Pi.
Technology should be for everyone, but it has to be built by everyone to be for everyone. At Raspberry Pi, we work to empower everyone to become a tech creator and shape our collective digital future, and we hope that our work will help to increase the tech sector’s diversity.
Today, part of our team is attending WeAreTheCity’s WeAreTechWomen conference to spread the word about our free programming courses and encourage more women to share their digital skills with the next generation.
I asked Carrie Anne Philbin, our Director of Educator Support, and Vanessa Vallely OBE, Managing Director at WeAreTheCity, about their thoughts on how we can make the tech sector more diverse, and what part role models, education, and professional development play in this.
Vanessa, WeAreTheCity helps organisations foster a strong female workforce, and provides opportunities for women to network and develop their skills. Why do you think it’s important for women and people from minority backgrounds to support each other in the professional world?
Vanessa Vallely: I believe it is important for everyone to support each other. It is important that we work as a collective and collaborate, as at the end of the day we are all trying to achieve the same goal. 17% women in tech [in the UK] is not enough.
“We want more women in tech, and we want them to represent all aspects of society.” – Vanessa Vallely OBE
We cannot be what we cannot see, therefore asking women who are already working in tech to stand up and own their role model status is a great start.
What can individuals do to address the lack of diversity in the tech sector?
Carrie Anne Philbin: Firstly, let’s recognise that we need the tech sector to be more representative of the population of the world. It’s problematic to have a small subsection of society be the controllers of a growing digital world.
Then, we need to be the change we want to see in the industry. Let’s try different avenues and then let’s be open about our challenges and successes.
VV: I believe every woman in the tech sector is a role model to future generations. There are a number of things individuals can do, for example go back to their schools and tell their tech stories, or contribute/write blogs. This doesn’t just raise their profile, it puts their story out there for others to aspire to. I think this is really important, especially if the individual is from a background where role models are less visible. There are lots of different organisations and networks that facilitate individuals getting involved in their school or early career initiatives which has made it easier to get involved and give back.
CAP: As a woman in the computing field, I think it is important that I hold the door open for other women coming through in my wake, and that I highlight where I can, great work by others.
Ever since I realised that my skills and knowledge in computing were useful and allowed me to be creative in a whole new way, I’ve championed computer science as a subject that everyone should experience. Once you’ve created your first computer program or built your first network, you’ll never want to stop.
Carrie Anne, how does your coding session at WATC’s WeAreTechWomen conference today tie into this?
CAP: At the Raspberry Pi Foundation, I spend a lot of time thinking about how to teach computing well, and about how young people can have great learning experiences so they can become the makers and creators of tomorrow.
“Technology is not a mystery, nor is it hard to learn. I want to dispel this myth for everyone regardless of gender, ethnicity, or economic status.” – Carrie Anne Philbin
During my session at WeAreTechWomen, I hope to support attendees to write their first creative python program, based on a project I wrote for Code Club to create a virtual pet. It is my hope that the session will be the spark of inspiration that gets more women and men from diverse backgrounds excited about being creators of technology.
You’ve built a career in tech education as a teacher, YouTuber, and Director at Raspberry Pi. How can beginners get comfortable creating with tech?
CAP: There isn’t anything magical about technology, and once you know this, you can start to explore with confidence, much like our ancestors when they learned that the earth was round and not flat.
“Phrases like ‘I’m not good with technology’ or ‘It’s all too complicated for me’ are reassuring to say in a society where the accepted view is that maths and science are hard, and where this view is reinforced by our media. But it is OK to be a beginner, it is OK to learn something new, and it is OK to play, explore, fail, and succeed on the journey.” – Carrie Anne Philbin
However you like to learn, be it on your own or with others, there is a way that suits you! I’ve always been quite project-minded: I have ideas about things I want to make, and then go and see if I can. This is how I stumbled across the Raspberry Pi in 2012 — it seemed like an accessible and cheap way to make my automation dreams come true. It also wasn’t too bad at randomly generating poems.
Aside from teacher-led instruction or independent exploration, another way is to learn with others in a relaxed and informal setting. If you’re a young person, then clubs like Code Club and CoderDojo are perfect. If you’re an adult, then attending a Raspberry Jam or conferences like WeAreTechWomen can provide a supportive environment.
“By being kinder to ourselves and seeing ourselves as life-long learners, it is easier to overcome insecurity and build confidence.” – Carrie Anne Philbin
A great way to approach new learning is at your own pace, and thanks to technology, we have access to online training courses with great videos, exercises, and discussion — many of these are completely free and let you connect with a community of learners as well.
How do you think educating the next generation about computing will change the makeup of the tech sector?
CAP: We’re in an exciting phase for computing education. The world has woken up to the importance of equipping our young people with the knowledge and skills for an ever increasing digital landscape. This means computer science is gaining more prominence in school curricula and giving all children the opportunity to discover the subject.
“Education can be democratising, and I expect to see the makeup of the tech sector reflect this movement in the next five to twenty years.” – Carrie Anne Philbin
Unlike physics or music, computing is still a relatively young field, so we need to do more research into what is encouraging and what isn’t, particularly when we work with young people in schools or clubs.
We’re still learning how to teach computing, and particularly programming, well to encourage greater diversity, so it’s great to see such a vast Gender Balance in Computing research project underway as part of the National Centre for Computing Education here in England. It’s not too late for schools in England to get involved in this project either…
The post “17% women in tech is not enough” appeared first on Raspberry Pi.
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