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AI technologies, such as smart speakers or streaming recommendations, are becoming part of everyday life for children and teenagers. It’s therefore increasingly important to help young people understand not just how these systems work, but how to question them.
When AI systems generate outputs such as a recommendation, these outputs are often accompanied by an explanation. In our latest research seminar, Professor Dr. Dan Verständig (Goethe University Frankfurt, Center for Critical Computational Studies) spoke about how explanations are shaped by the people who write them, and only become meaningful when someone else makes sense of them. Dan introduced us to Social Explainable AI (Social XAI) and Critical Computational Literacy (CCL), and asked us to consider not just what an AI system explains, but for whom, why, and who benefits.
Dan opened with a deceptively simple question: why do we need explanations in the first place? His answer was that explanations provide orientation. They reduce uncertainty, and in doing so, they make our shared social world liveable. Explanations have evolved from Socratic dialogue, to the printed book, to the classroom, to today’s digital interfaces, and now to AI-generated explanations. But explanations have never been neutral conveyors of information. Dan illustrated this with a simple demonstration: he showed the seminar participants an aerial photo of a beach and asked what they noticed.
The aerial photo Dan shared during his seminar. What do you notice about this picture?
Seminar participants pointed to the coastline, the sandy beach, and a tiny figure in the frame. Dan then revealed that the photo was taken by him, using a drone at Montara State Beach in California on a specific afternoon in April 2024. He asked how a climate researcher, a surfer, or an artist might each describe the very same image differently.
This demonstration highlighted that an explanation is always for someone, for some purpose, in some context. That framing carries straight through into how we should think about AI explanations too. An explanation that satisfies a data scientist debugging a model is not the same as one that satisfies a patient asking why an algorithm flagged their scan, or a citizen asking why they were denied a loan. In other words, explanations are never technical.
Drawing on Rohlfing and Lim’s 2026 work, Dan described Social XAI as an approach that puts interaction, rather than output, at the centre of explainability. Dan illustrated this with a simple but effective diagram: an AI explanation starts as a system output, is filtered through a person’s interpretation, and only then becomes meaning through construction. A one-size-fits-all output, however technically accurate, isn’t yet an explanation until someone has made sense of it in their own terms.
To explore this further, Dan’s research group has developed co-construction workshops, bringing people together to interrogate AI explanations collaboratively rather than receive them passively. In these workshops, participants are asked questions such as: “What counts as evidence? What is missing? What are the alternatives? Who benefits from this? Do we agree?”
To answer these questions, participants do not examine an AI model to find out how it generates a decision. Instead, participants scrutinise its outputs in the same way that they would critically evaluate a politician’s promise or a newspaper’s headline.
Critical Computational Literacy (CCL) is a framework that describes what people need to engage with AI explanations critically. Dan presented CCL as consisting of four interlocking dimensions:
Taken together, these dimensions push back against a narrow, purely technical notion of ‘AI literacy’ as knowing how a model works. Instead, CCL treats literacy as something biographical and value-laden. Dan explained that individuals bring their own history and stance to any encounter with computational systems, and genuine literacy means being able to interrogate these systems’ explanations rather than simply accept or operate them.
Although Dan’s research took place with adult participants, Social XAI and Critical Computational Literacy are ideas that could also be used in the K-12 classroom. For example, if students interact with AI explanations through smart speakers or streaming recommendations in their everyday lives, this presents an opportunity to teach them how to engage critically with AI outputs. Students might explore why a smart speaker suggested a particular recipe, or investigate the explanation for why a streaming service recommended a particular show.
Explainability is also a key part of our own Experience AI resources. When training a model, students write their own model cards to document who built a model, what training data was used, how accurate the model’s predictions were, and any known limitations. In this activity, explainability is traceable, and students use their analytical skills to create transparent, fair, and accountable model cards.
Social XAI suggests an extension to this activity. Students could ask what matters about the explanation they have written and why this is important. Through this critique, students can consider who will read these model cards and how the cards might be interpreted. In this way, AI explanations become more than a technical output, and become artefacts whose meaning is co-constructed by the author and the reader.
This seminar was part of our ongoing series on teaching about AI in the arts, humanities, and sciences. You can watch the full recording of Dan’s talk, including the Q&A discussion, here:
Our research seminar series continues to explore how AI is taught across the curriculum. In our next seminar on Tuesday, 6 October at 19:00–20:30 BST, we welcome Eleni Petraki and Damith Herath (University of Canberra) who will present an engineering and robotics curriculum aimed at equipping future engineers with the diverse skills demanded by a growing workforce. To take part in the seminar, click the button below to register. We hope to see you there.
The schedule of our upcoming seminars is available online. You can catch up on past seminars on the blog and on the previous seminars and recordings page.
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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.
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Artificial intelligence (AI) is already part of young people’s everyday lives, from the content recommended to them on social media to the generative AI tools they increasingly encounter. But AI technologies are also being used to tackle challenges in the wider world, from forecasting floods to monitoring our environment.
That’s why we believe AI literacy shouldn’t sit within a single subject. Young people need opportunities to explore how AI works, where it is used, and the questions it raises across the curriculum.
To address this, we created a new collection of free themed Experience AI resources, designed to make it easier for educators to bring AI literacy into the subjects and topics they already teach. The resources include those co-developed by the Raspberry Pi Foundation and Google DeepMind, alongside those developed independently by the Raspberry Pi Foundation, including two units created with support from our partner, Digital Moment.
When we began developing these resources, we initially explored creating materials specifically for individual curriculum subjects. But through conversations with educators and our partners around the world, we learnt that a more flexible approach could be much more useful.
Curricula differ between countries, schools, and age groups, and AI technologies rarely fit neatly within traditional subject boundaries. The same technology can raise scientific, geographical, social, creative, and ethical questions.
So rather than assigning each resource to a particular subject, our new resources are organised around broad themes, including the environment, critical thinking, and ethics.
The resources include:
Designed for learners aged 8 to 16, the resources can be used in different subjects and adapted by educators to suit their own classroom context.
That flexibility is important. We want educators to be able to introduce meaningful AI literacy without feeling that they need to become AI specialists or find space for an entirely new subject in an already busy curriculum.
The need for this approach is increasingly recognised by subject experts.
Dr Becky Kitchen, Head of Professional Development at the Geographical Association, highlighted the importance of giving young people opportunities to engage critically with AI technologies as part of their wider learning:
“Pupils are increasingly coming into contact with AI and so it’s vital that they are taught how to harness its power in an effective and critical way. Developing resources across the curriculum to embed this knowledge, understanding, and use is critical.”
After reviewing the new resources, she added:
“These resources are outstanding. They develop pupils’ geographical knowledge while simultaneously exploring the role that AI can have in a meaningful and concrete way.”
The connections extend well beyond geography.
Professor Geoff Cox, Professor of Art and Computational Culture at London South Bank University, emphasised the role that arts and humanities subjects can play in developing a richer understanding of AI technologies:
“If AI literacy is left solely to STEM subjects or computer science, we lose the opportunity to ask deeper human, cultural, and ethical questions — questions that art is uniquely equipped to explore.”
He also highlighted the value of combining different ways of learning:
“The resources move between visual analysis, practical activity, and critical reflection, recognising that only in combination can AI literacy be developed effectively.”
These perspectives reflect an important principle behind the collection. AI literacy isn’t simply about knowing how a technology works. It is also about being able to question it, understand its applications and limitations, and consider its impact on people, communities, and the world.
Before launching the resources more widely, earlier this year we collaborated with our partner, Digital Moment, to give educators in Canada the opportunity to test some of them with their students. Their experiences helped us understand not only how the resources worked in practice, but also where they could fit naturally into existing teaching.
As Indra Kubicek, CEO of Digital Moment, explains:
“Educators play a critical role in helping students understand and think critically about the use of AI and its implications across a wide range of subjects. Building the next generation of creators, builders, and innovators who will shape the future of AI starts with AI education in the classroom.”
One of the educators who took part was Grade 8 teacher Sandra Theobald, who tested our AI and social media activity. Before the lesson, she expected her students, who were already regular social media users, to be familiar with many of the ideas.
While they did have a good background knowledge, the activity prompted them to make new connections between their own behaviour, the data they generate, and the content they are shown as a result of the algorithms presented to them.
Students were able to explore the ideas themselves, with Sandra taking more of a supporting role. She found that they came away feeling more confident about understanding how their interactions with social media affect what they see, and keen to share what they had discovered with their families.
For educators who may feel unsure about introducing AI tools in their classroom, Sandra’s advice is to simply give it a go, rather than waiting until feeling like an expert:
“My advice is to start small and seek out trusted resources and experts in AI education. Partner with another educator so you can explore and learn together. There is so much information available that it can feel overwhelming, so take it one step at a time. Learn alongside your students and build your confidence as you go.”
Canadian educator Colin McKenzie also used the AI and social media unit with his Grade 7 and 8 classes.
Using Somekone, a closed social media simulation for the classroom, his students created accounts and explored how their behaviour affected the content the system recommended.
The experience felt familiar enough to capture their attention, but gave them an opportunity they wouldn’t normally have when using a real social media platform: to examine what was happening behind the scenes.
Students reflected on their interests and choices and then used data on their own behaviour to understand how that behaviour affected what they were shown. Colin found that students were eager to return to the activity each day to discover what would happen next.
Crucially, he also found it straightforward to incorporate the activity into his existing teaching rather than having to significantly change his plans.
“I think this is a powerful and easy resource for teachers to use in the classroom. It is easy to set up and navigate, and it ties in well with curriculum areas around online usage, especially with how prominent AI is becoming in our educational lives.”
For Colin, the activity provided a practical way to connect AI literacy with what his students had already learnt about online safety and evaluating information. It also helped students recognise that the content they encounter online isn’t simply appearing by chance: their own actions can influence the content recommended to them by AI-driven systems.
The experience was valuable enough that Colin plans to make the unit part of his teaching each year.
Feedback like this has reinforced why flexibility is central to our approach.
A lesson about flood forecasting might support learning in geography or science. Exploring AI-generated content could lead to discussions in art, media literacy, humanities, or computing. An activity investigating social media algorithms can connect AI literacy with online safety, critical thinking, and digital citizenship.
Most importantly, these things provide opportunities for students to encounter AI literacy in different contexts and recognise that understanding AI is relevant far beyond the computing classroom.
AI technologies will continue to change. The contexts in which young people encounter them will change too.
Our aim isn’t to prepare students for one particular AI tool or moment in time. It’s to help them develop the knowledge and critical thinking skills they need to understand all kinds of AI technologies, question them, and make informed decisions about them.
Educators shouldn’t need to be computer science teachers, or AI experts, to do that.
By creating flexible resources that connect AI with subjects and issues young people are already exploring, we hope to make it easier for more educators to bring AI literacy into their classrooms.
Explore the new themed Experience AI resources and discover a starting point for your learners.
We’d like to say a big thank you to our global partner, Digital Moment, for their support in trialling these new resources with educators and learners in Canada.
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Oracle’s proposed investment in 2 gigawatts of renewable energy projects for New Mexico comes as local opposition could delay development of the Project Jupiter data center that Oracle is building for OpenAI.
The two tech companies are developing the $165 billion Project Jupiter data center in Santa Teresa, New Mexico, as part of the broader Stargate AI infrastructure project announced by President Donald Trump in 2025. But Project Jupiter faces local protests and court battles over concerns about its environmental impacts—and the latest Oracle announcement on September 8 seeking proposals for renewable energy projects does not change the fact that the data center will be powered by fuel cells that consume natural gas.
“Like all matching programs, this would be synthetic in the sense that 2 GW of renewables wouldn't directly power the data center,” wrote Michael Thomas, CEO of the Cleanview data platform that tracks renewable energy and data center projects, in a LinkedIn post.
Experience AI equips young people with a meaningful understanding of artificial intelligence (AI) and machine learning by giving educators the knowledge and confidence to teach these topics in ways that suit their classrooms.
Whether you’re introducing AI to learners for the first time, helping them deepen their understanding, exploring generative AI with them, or integrating AI literacy across the curriculum, Experience AI offers you all the resources you need for free.
Since we started publishing Experience AI resources in 2023, they have been downloaded over a million times in 195 countries, and we have worked with partner organisations in more than 40 countries to train educators to teach AI literacy. Thanks to partners, we have learned a lot about how teachers around the world use the resources in their classrooms, and this has given us direction for what new resources to develop.
Many teachers looking for AI literacy resources are not computing specialists and have very busy timetables. Under pressure to deliver more content without more instructional time, what educators need are resources they can integrate into what they already teach. To support them, we’re now offering an updated suite of Experience AI resources that make AI literacy more accessible, flexible, and relevant.
The resources include those co-developed by the Raspberry Pi Foundation and Google DeepMind, alongside those developed independently by the Raspberry Pi Foundation.
The new Discovering AI resources, aimed at learners aged 8–12 and learners aged 13–16, are single lessons for introducing the fundamental ideas behind AI. They support educators with learners who have little or no prior knowledge of AI, and include engaging, age-appropriate activities.
From there, our updated Foundations of AI units let teachers support their learners to develop a deeper understanding of how AI systems work, how they’re trained, and how they can be applied to real-world problems.
And a new and growing collection of thematic resources supports teachers and learners to explore AI through cross-subject topics such as creativity, the environment, and critical thinking.
Experience AI now fully reflects our belief that AI literacy needs to be cross-curricular. Application of AI technologies isn’t limited to neat domains or subjects, so learners’ opportunities to understand them shouldn’t be either.
Education systems vary significantly between countries, and in most national curricula, AI literacy is not yet clearly defined. Nevertheless, teachers are both under pressure to deliver this new topic area in their limited classroom time, and eager to rise to the challenge to support their learners.
So we asked ourselves: how can we help educators to teach AI literacy in any subject without additional lesson time, when we cannot create specific resources for every subject in every education system?
Our answer came from educators themselves. Through our network of global partners, we learned that teachers were not waiting for us to tell them in what subjects the Experience AI resources belonged. They were already adapting them to teach AI literacy in all sorts of contexts. For example, we saw that some educators adapted the resource on AI and ecosystems, which we had developed for Biology classrooms, for their Geography classrooms, where it supported similar learning goals.
This insight into teachers’ classroom practice prompted us to change our approach.
Now, rather than designing resources for a single subject, we design and organise them in themes that fit across subjects, such as environment, creativity, ethics, and critical thinking. So a resource for exploring the environmental impact of AI data centres could be used in Geography, Physics, Citizenship, or Business Studies classrooms. An activity about AI-generated media could prompt discussions in Digital Literacy, Art, or Computing lessons. The same material supports different curricular goals, depending on how teachers choose to use it.
A key advantage of this new thematic approach is flexibility. Our thematic resources support teachers to:
Like all Experience AI materials, the new resources:
While Experience AI will continue to focus on core AI literacy concepts — how AI systems work, how they are used, and how to think critically about them — the resources we offer will increasingly be:
In this way, we aim to make teaching AI literacy practical and achievable for every educator, regardless of their subject specialism or previous experience of using or teaching about AI.
We’ve tested the new thematic resources with our Experience AI partner, Digital Moment in Canada, who also co-created our Social Media and Flood Forecasting units. Educators’s feedback shows that they value the added flexibility and find the new materials easier to bring into their teaching.
With our new approach, we’re able to offer a more flexible Experience AI programme that supports a wider range of educators, however they want to bring AI literacy into their classrooms.
If you use the resources in your classrooms, please tell us what you think. We’ll continue refining and expanding the Experience AI programme and resources in response to feedback from educators and partners around the world.
Get in touch and share your stories of using Experience AI in the classroom via our email: advocacy@experience-ai.org
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Young people see and use artificial intelligence (AI) systems frequently, and hear a lot about how they are applied across many areas of life. That makes them understandably eager to find out more about how AI works. For some, this interest extends to wanting to experiment with the technology to investigate their own questions and build something themselves. How can schools support learners who want to take their interest further and gain recognition for the knowledge and skills they develop along the way?
For learners in the UK, there is currently no established qualification that develops technical AI skills. This can make it difficult to create space in the curriculum for dedicated AI learning, especially for learners from age 14, for whom preparation for high-stakes exams is prioritised over non-examined learning.
We have been working over the past year to develop a research-informed curriculum framework for data science, which lays out many of the foundational skills and knowledge needed to understand and develop AI models. Alongside this, we have been making the case in England for a Level 3 qualification in data science and AI. However, establishing a new qualification can take years.
In the meantime, we are starting to support students in England who choose an established route for extending their skills and getting that effort recognised: the Extended Project Qualification (EPQ).
The EPQ is a well-established qualification for 16- to 19-year-olds offered in England, Northern Ireland, and Wales by several exam boards (including AQA, Pearson Edexcel, OCR, Eduqas/WJEC, and City & Guilds). It is completed by around 1 in 10 learners at this stage of education.
Learners pursuing an EPQ undertake an extended, self-directed investigation or project. They choose an area of interest where they want to develop their own project and take responsibility for planning, completing, and evaluating the project work.
Its self-directed nature makes the EPQ particularly well suited to learners who want to explore emerging areas, such as artificial intelligence and data science. For students fascinated by AI, an EPQ offers an opportunity to do more than simply learn about the technology. With the right foundations and support, they can investigate a question of their own, build a machine learning model, and use that work to gain a recognised qualification.
We are now working to offer those foundations through a new introductory ‘Data Science and AI’ course that will enable learners to build a machine learning model as their EPQ artefact.
Creating a machine learning model involves much more than choosing a statistical technique and using easily available training data. It involves understanding the problem being addressed, deciding what data to use, exploring and preparing that data, building and evaluating a model, and interpreting the model’s outputs.
Learners who want to do this work for their EPQ need not only technical knowledge and skills, but also skills for critical thinking, research, decision-making, evaluation, and reflection — skills that are valuable for any further study and future career.
To help learners develop these foundations, we are creating an introductory ‘Data Science and AI’ course, based on our curriculum framework for data science and designed to be completed before learners begin their AI-focused EPQ.
The course will guide students through a structured investigation based on a recognised data science lifecycle. By working through it, students will:
The course will consist of 10 units, each involving 2 to 3 hours of independent study for learners. We are designing the activities with a no-code approach to allow students to focus on understanding data science concepts and processes.
Importantly, the work learners produce during this course won’t be the EPQ investigation itself. Working through ‘Data Science and AI’ will give learners the knowledge and skills they need to undertake an AI-focused project with greater independence and confidence.
After they have completed the course, when they begin their EPQ project pursuing a question that interests them, students can apply their new skills and knowledge to make decisions about their AI investigation and document their progress.
We are looking forward to supporting learners in England to turn their interest in one of today’s most significant technologies into the opportunity to develop independent research and project skills through a recognised qualification.
The ‘Data Science and AI’ course will be available to selected schools in England from September 2026, where students and teachers will test the materials and we will learn from their feedback to improve the course materials. We plan to make the course freely available in 2027.
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We’re pleased to share that we are expanding our Experience AI programme to Australia and Aotearoa New Zealand to train 5000 educators who can reach 150,000 students by 2028, thanks to generous funding of $1.2 million from Google.org.
Working with local education organisations, we will support young people to develop a foundational understanding of AI technologies, their social and ethical implications, and the role that AI can play in their lives.
AI systems are a common part of everyday life and influence how we access information, and how we work and solve problems. We believe that young people need more than the ability to use AI tools: they need the knowledge, skills, and confidence to understand how AI technology works, to think critically about its impact, and to create AI-based solutions of their own.
Experience AI is our free educational programme co-developed with Google DeepMind that helps teachers and students learn about artificial intelligence. Through the Experience AI training, lessons, classroom resources, and hands-on activities, teachers introduce young people to how AI systems work, how they can be used, and what their impacts may be.
We bring AI literacy to young people across the world with Experience AI by building trusted partnerships with local organisations that lead sustainable delivery of the programme in ways that suit their contexts. Through this global network of Experience AI partners, we have trained over 50,000 educators who can reach an estimated 4.8m young people. Today, Experience AI resources are used in over 195 countries and available in 22 languages. In recognition of its global impact, Experience AI was named a laureate of the 2025 UNESCO King Hamad Bin Isa Al-Khalifa Prize for the Use of ICT in Education.
In Australia, the first partner we are working with is the Computer Science Education Research Group (CSER), based at Adelaide University. Professor Katrina Falkner, the university’s Pro-Vice-Chancellor, Learning and Teaching, says about the partnership:
“We are thrilled to partner with the Raspberry Pi Foundation to bring the Experience AI programme to Australian schools. It is so important that teachers are provided opportunities to understand AI so they can help students develop the self-regulated learning skills needed to thrive in a world where AI is increasingly part of everyday learning and work. Educators can play a critical role in ensuring that a human lens of critical thinking, ethical judgement, creativity, and meaningful human connection remain at the heart of AI education and adoption, preparing students for careers where effective collaboration with AI tools will be essential.”
In Aotearoa New Zealand, we are working with Tōnui Collab Charitable Trust, a Maōri-led organisation dedicated to creating innovative STEM learning opportunities.
Shanon O’Connor, Director of Tōnui Collab, says about the partnership (1):
“We are partnering with the Raspberry Pi Foundation to provide this kaupapa to educators in Aotearoa, adapting and contextualising their global Experience AI program to make it meaningful and relevant in Aotearoa.
This kaupapa isn’t about learning to code; it’s not a kaupapa designed solely for the ‘tech enthusiasts’, it’s about fostering digital equity, ensuring rangatahi have the tools and knowledge to thrive in a world increasingly shaped by technology. It’s our attempt to ensure the digital divide doesn’t become a digital chasm.
We’re also facilitating robust conversations about data bias and the impact this has on the ways we as Māori engage with AI-powered technologies, creating space for kōrero about tech tikanga and our collective responsibilities when using or engaging with AI-powered technologies.”
All young people need opportunities to develop the skills, knowledge, and confidence to navigate and shape a world where AI technologies are widely used. With support from Google.org and education partners across Aotearoa New Zealand, Australia, we will continue to expand access to high-quality AI education.
Find out more about Experience AI at experience-ai.org
(1) Shanon uses some Māori words that are common in Aotearoa New Zealand for both speakers and non-speakers of reo Māori:
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The development of AI and data science has transformed how we gain insights from data. In healthcare, AI tools are being used in the development of new treatments as researchers apply machine learning methods to datasets. However, applying AI in healthcare also brings risks, particularly when systems amplify existing biases in data or design.
In our fourth seminar in our current series on teaching about AI in the arts, humanities, and sciences, Kathy Jessen Eller (The Concord Consortium) introduced the Data Science, AI & You (DSAIY) programme, a high school curriculum that helps students critically evaluate the role of data and AI in healthcare.
Kathy began her seminar by arguing that for many students who use AI tools in their coursework, questions remain about whether they are critically evaluating the tools’ outputs. Students may or may not check an AI-generated answer against primary sources to see if the answer is accurate. There is also growing concern that students’ use of AI tools lets them offload cognitive work rather than engage in deeper thinking. This presents a challenge for educators: how do we help students use AI productively while still supporting them to develop the critical judgement needed to evaluate its outputs?
To tackle this challenge, Kathy and her colleagues have developed the Data Science, AI & You (DSAIY) programme (pronounced ‘Daisy’). DSAIY is a semester-long high school curriculum designed to introduce students to AI by actively engaging them in the machine learning process.
The programme introduces machine learning as the engine behind many AI tools, and introduces the concept of bias through real-world examples. Students use a variety of tools to collect and prepare data, train, test, and evaluate models. It culminates in an ‘AI-a-thon’ where young people work in cross-disciplinary teams alongside data scientists, clinicians, and their own teachers to gain real-world experience.
At the time of the seminar, 11 teachers had delivered the programme to over 800 students across a variety of settings in Rhode Island, USA. Teachers are heavily supported with four days of professional development and ongoing technical assistance throughout implementation. The students that took part had a wide variety of prior experience, including many with no prior background in computer science or statistics. Female participation is notably high; one teacher even remarked that the course saw more girls enrolled than any of his other computer science classes.
In DSAIY, students experience the full machine learning pipeline from data collection and data preparation, to modeling and deployment. Using Python, they train and test simple machine learning models on authentic healthcare data. The aim of the programme is to move students from basic graphing to evaluating complex models, transitioning them from merely plotting data to deeply reasoning about it.
The programme makes use of CODAP (the Common Online Data Analysis Platform), a free, web-based tool developed by The Concord Consortium. CODAP provides an interactive, highly visual environment that lowers the barrier to entry. Students can visualise large datasets and click into individual data points, allowing them to see individual cases within a larger dataset.
The curriculum uses real-world examples from healthcare to introduce concepts of bias and fairness. For example, students learn about pulse oximeters, which estimate blood oxygen levels. However, as these use red and infrared light, readings can vary depending on skin pigmentation, which can lead to inaccurate readings.
Students also collect their own blood oxygen data and plot it using CODAP to observe variability. They consider the accuracy of their measurements and grapple with the ethics of removing outliers from a dataset. This led to students asking critical questions about the makeup of their datasets, the context in which data are collected, and the implications of how data are used in healthcare.
Through the DSAIY programme, Kathy reported that students developed stronger data reasoning skills, gained a deeper awareness of inherent AI biases and risks, and built confidence in public speaking and collaborating with others. Students were also highly engaged and appreciated the focus on real-world healthcare applications and their social implications.
Kathy concluded the seminar by arguing that AI literacy must start with data literacy. When students learn to examine, question, and reason about the data behind AI technologies, they develop the critical thinking skills needed to engage with outputs from real-world systems or everyday technologies like ChatGPT. This can then help them evaluate both the trustworthiness of these tools and their role in important decision-making processes.
You can watch the seminar here:
If you are interested in learning more about Kathy’s work, you can read about the DSAIY programme here or you can read the paper here. You can also learn about CODAP, the data visualisation tool featured in this seminar here.
In our current seminar series, we’re exploring how AI is taught across the curriculum. In our next seminar on Tuesday 14 July at 17:00–18:30 BST, we welcome Dan Verständig (Goethe University Frankfurt) who will explore the connection between Social explainable AI (Social XAI) and Critical Computational Literacy (CCL). To take part in the seminar, click the button below to register. We hope to see you there.
The schedule of our upcoming seminars is available online. You can catch up on past seminars on our blog and on the previous seminars and recordings page.
The post AI literacy begins with data literacy: An example from healthcare appeared first on Raspberry Pi Foundation.
Today’s data-driven tools can make many aspects of our personal lives less time-consuming, because they present us with options and even make decisions for us. By relying on predictive text features, we write messages more quickly and outsource our word choices. By using music and film recommendations, we outsource our personal taste. And by using AI chatbots that produce confident answers to every single one of our questions, we outsource our thinking.
Of course, I use and enjoy all of these products. But because I grew up before data-driven tools existed, I also know the accidental delights of exploring a city without a smartphone map, the joy of thoughtfully choosing a gift for a friend, and the satisfaction of comparing insurance quotes and understanding their details. These non-AI-assisted acts exercise my critical thinking skills — something that is harder to do in a world where AI products promise so much convenience.
Critical thinking is even more vital now in the age of AI. The brand-new issue of Hello World — and our new podcast mini series — offers research, advice, and practical resources for teaching young people, and ourselves, to think critically.
In issue 30 we share articles from educators who have already been thinking deeply about the role of critical thinking in the age of AI. They discuss a range of questions such as:
• What do educators bring to the table when teaching with digital technologies?
• Why AI professional learning should build teachers’ critical thinking, not just their confidence in using tools
• Whose knowledge is shaping AI?
Our feature articles also include:
• Managing cognitive load for deeper thinking
• AI systems in assessment
• Promoting human decision-making
From the team at the Computer Science Teachers Association (CSTA) in the USA we have an article about their newly rewritten CSTA K–12 Standards, a research-backed framework designed to prepare students for a future that seems to be arriving very fast on some days. As their article says:
“AI can generate answers instantly, but understanding and evaluating answers still
requires human judgement. In a world moving at supersonic speed, CS education needs to find a new balance. Students must learn to think critically so they can direct AI rather than being directed by it.” – Amanda O’Mara, Smita Kolhatkar, and Tiffany Jones in Hello World issue 30
Developing critical thinking skills is important for young people, regardless of the discipline you teach. In the age of AI, computing education is uniquely situated to cultivate this mindset, encouraging students to engage more thoughtfully with the AI tools they use daily.
Also in issue 30:
• Flatgames
• Predictive classroom systems
• A physics meets technology project
And much, much more.
Let us know which articles you found most helpful for your teaching or which resources you tried out by sending us a message or tagging us on social media.
Thank you to Oracle for sponsoring this issue of Hello World.
The post Hello World #30 out now: Critical thinking in the age of AI appeared first on Raspberry Pi Foundation.
A fake recruitment phishing campaign impersonates major brands and uses trusted HR platforms to steal Google account credentials.
The post Fake Job Offers Impersonate Netflix, OpenAI, and FIFA to Steal Google Credentials appeared first on TechRepublic.
The Neuron Academy launched on July 7 with self-paced AI courses for professionals who want to use ChatGPT, Claude, and Gemini more confidently.
The post The Neuron Launches AI Training Platform for Everyday Professionals appeared first on TechRepublic.
The best applicant tracking systems for 2026 include Rippling for all-in-one HR, Pinpoint for scalable hiring, and Freshteam by Freshworks for easy candidate management.
The post 10 Best Applicant Tracking Systems in 2026 appeared first on TechRepublic.
Researchers disclosed usbliter8, a SecureROM exploit affecting older Apple devices that can bypass boot protections with physical access.
The post New Apple Exploit Exposes Millions of iPhones Worldwide, No Software Fix Available appeared first on TechRepublic.
L'été astronomique commence officiellement en juin 2026 dans l'hémisphère nord. Les nuits sont plus courtes à l'approche du solstice, mais les spectacles astronomiques à voir même à l'œil nu sont bien au programme.
At the Raspberry Pi Foundation, we believe that alongside learning to code, a crucial part of computing education is building AI literacy skills. Amidst the rapid pace of development and the growing impact of AI tools, it is increasingly important for educators everywhere to feel equipped to address the topic of AI with their learners, to help young people understand their world, be responsible users of AI technologies, and prepare to become the future creators of these technologies.
We work at the leading edge of AI education, combining research and industry expertise with practical classroom experience to define what AI means for computing education, and how to best support teachers and learners to understand these technologies.
Whether you are a teacher, a Code Club mentor, or a parent, we have a wide range of free resources to help you teach your young people about AI, and learn more about it yourself.
We offer a variety of teaching materials to help you bring AI into your setting. You do not need to be a professional educator or have a background in computer science to use these resources, and we provide everything you need to guide your learners with confidence.
Experience AI is our free AI literacy programme, developed in collaboration with Google DeepMind. Through ready-to-use classroom resources, including lesson plans, presentations, and hands-on activities, the programme helps educators all over the world teach their learners about how AI works, as well as its wider social and ethical implications. You and your learners will investigate AI tools, explore real-world uses of AI, and engage with critical issues such as bias, fairness, and transparency, helping learners to understand AI and use it responsibly. The lessons currently available are designed for learners aged 11–14, and we are releasing resources for other age groups this year.
To help bring Experience AI to more educators and learners around the world, we work with a global network of partner organisations, who help us provide tailored and translated resources and offer localised, high-quality training and support for educators in their regions. Experience AI resources are currently available in 19 languages, and have already been downloaded in more than 180 countries. In recognition of its impact, in 2025 Experience AI was named a laureate of the UNESCO King Hamad Bin Isa Al-Khalifa Prize for the Use of ICT in Education.
“[Experience AI] has definitely changed my outlook on AI. I went from knowing nothing about it to understanding how it works, why it acts in certain ways, and how to actually create my own AI models and what data I would need for that. I would 100% recommend others who don’t know much about AI to try it out.” – Student, Arthur Mellows Village College, UK
If you are looking to introduce school-aged young people to AI with short, beginner-friendly coding and digital making projects, take a look at our collection of Code Club projects about AI and machine learning. These projects are a great way to spark curiosity and investigate how AI and machine learning works.
In the projects, learners get hands-on with a range of AI tools and platforms, and explore different applications of AI, such as image recognition, voice recognition, and (for learners aged 13 and over) generative AI. For example, in Doodle detector, learners use Machine Learning for Kids with Scratch to create a machine learning application that can identify what they have drawn.
The projects feature clear step-by-step instructions, and many include video tutorials, to help learners work at their own pace and in a style that suits them. We also provide mentor guidance to help you prepare.
For resources to support older learners to build their understanding of AI, head to Ada Computer Science. Ada Computer Science is our free online platform for computer science students and teachers, developed in partnership with the University of Cambridge. It provides comprehensive resources for learners aged 14–19 across the breadth of computer science, including detailed learning materials about AI and machine learning. The materials include helpful definitions, clear explanations, and carefully designed self-marking questions to support young people’s learning.
To help you build your knowledge around AI technologies and grow your confidence to teach your young people about this important topic, we offer a range of learning resources and professional development opportunities, all for free. They are open to everyone, so we invite you to dive into any that interest you.
For flexible, self-paced learning options, take a look at the following free online courses, which cover a range of topics within AI:
You might also be interested in Hello World, our free magazine and podcast for educators teaching computing and AI. Each magazine is packed with resources, discussions, news, and ideas, and you can subscribe to receive each issue as soon as it is released. The next issue, coming in July, will focus on critical thinking in the age of AI. You can download our previous issues to explore articles about a variety of topics within AI too. What’s more, you can continue your learning with the Hello World podcast, which accompanies the magazine and features discussions with educators and researchers from around the world. Visit the podcast page to discover previous episodes exploring vibe coding and programming education, AI education around the world, and more.
To learn about research-informed teaching strategies to help you as you explore AI with your learners, you can read these short Pedagogy Quick Reads:
For more insights from computing education research, you can join our monthly online research seminars. Our 2026 research seminar series focuses on teaching about AI across the curriculum, delving into research on teaching and learning about AI from disciplines beyond computer science, including the arts, sciences, and humanities. Our next seminar takes place on 16 June, and you can catch up on previous seminars from our current series here. You can also explore our archive of recordings from previous seminar series, with themes including teaching about AI and data science, teaching programming (with or without AI), and more.
All of these resources and learning opportunities are available to anyone interested in educating young people about AI, anywhere in the world. We hope they help you gain understanding, confidence, and inspiration to guide your young learners to engage with AI safely and responsibly, and to learn valuable skills that will help them navigate their world.
“Let the students explore, advance, and grow. And who knows — maybe one of our students will go on to become a mentor or leader in this field someday.” – Ana Judith Zavaleta, computer science teacher, Mexico, speaking about Experience AI
If you are in the UK, you can also use these resources to get involved with the first-ever AI Awareness Day, a new nationwide campaign designed to build AI literacy across UK schools, which is taking place on 4 June.
The post Support your young people with our AI literacy resources appeared first on Raspberry Pi Foundation.
Europe, un des satellites naturels de Jupiter, possèderait un océan immense caché sous sa surface glacée. Cette particularité fait espérer l'existence de formes de vie extraterrestre, mais les preuves pourraient ne pas être si solides que cela.
NASA released a much-anticipated contract solicitation for a Mars-orbiting spacecraft late last week, kicking off what is sure to be a hotly contested and potentially controversial procurement.
At issue is $700 million, already appropriated by Congress, to build a spacecraft, launch it to Mars, and once there to serve as a vehicle to relay communications between the red planet and Earth. But the stakes may be even bigger than this, including the possible resurrection of the recently canceled Mars Sample Return mission.
As part of the new solicitation, NASA says it will conduct the acquisition "as a full and open competition." But will it? That's the question that several people involved with this procurement process are asking. And it could turn messy, quickly.
We are working with partner organisations around the world to support teachers in building confidence with AI in the classroom through our Experience AI programme. In this guest post, Catarina Marques from our partner TUMO Portugal shares what the organisation is learning from delivering training to educators.
Whenever we run Experience AI training sessions, we keep coming back to the same thing: teachers are not lacking interest in AI, what they are lacking is time. Time to explore the technology and tools, time to talk about them with colleagues, and time to work out what they really mean for their classrooms.
And that matters, because AI is not something schools can just put off until later. It is already here. Students are hearing about it, using it, and forming opinions about it. Teachers are being asked to respond to it now, often while still trying to make sense of it themselves.
The Experience AI teacher training is about more than educators to a set of resources. It is about helping them feel truly ready to take the Experience AI resources into the classroom and use them effectively with their students.
What we see again and again is that teachers need space to stop and think. AI is moving quickly, and schools do not always have the time or support to keep pace. New tools are developed all the time. Expectations keep shifting. There is a lot of noise, and not always much room to pause and ask: what is actually useful here? What do we need to understand better?
In our experience, that is where real learning starts: not in rushing through information, but in discussing it, debating it, and testing ideas together.
One of the most valuable parts of these training sessions is the part where teachers start talking to each other.
They bring real questions into the room. Which AI tools can actually help with their work? How should they think about ethics? How do they talk about AI safety with students? How do they respond to something that may feel both useful and worrying at the same time?
There is often confusion, and sometimes there is resistance too. That makes sense; this is still new territory for many schools. But there is also a real appetite to learn, especially because support in this area can still feel limited.
That is why listening is such an important part of our training. Teachers need space to reflect, compare experiences, and hear how others are approaching the same challenges. Very often, understanding grows through that process.
Another thing we feel strongly about is that the training has to be engaging.
AI can feel intimidating. If the atmosphere is too heavy, it can be easy for people to step back from it. That is why the hands-on and playful side of Experience AI is so important. Team activities, discussion, and even a bit of healthy competition change the energy in the room. People get involved. They relax. They start exploring instead of worrying about getting everything right.
That matters for teachers, and it matters for students too. When teachers experience this kind of learning for themselves, it becomes easier for them to imagine creating it in their own classrooms. Play is not separate from the learning here — it is part of what makes it stick.
For us, this work feels urgent. Schools need the language, confidence, and literacy to engage with AI now, not in a few years’ time.
What teachers need most is not endless hype or more pressure. They need time to explore, time to discuss, time to understand, and time to build confidence. Experience AI has offered a way to begin that process.
If we want young people to engage critically and confidently with AI, we have to start by giving teachers the chance to do the same.
If you want to find out more about Experience AI, visit our website experience-ai.org
The post Beyond content: Helping teachers feel ready to teach AI appeared first on Raspberry Pi Foundation.
Artificial intelligence (AI) systems are often presented as objective. But plenty of evidence shows that AI systems can reflect and reinforce existing inequalities, from healthcare and education to scientific research itself.
In the first seminar of our new research seminar series on applied AI, Thema Monroe-White from George Mason University explored how we can better understand — and challenge — these patterns. Her talk focused on race-conscious algorithmic approaches to AI and data, and what they reveal about how knowledge is produced, represented, and used.
Drawing on two large-scale studies in her seminar, Thema showed that both scientific research and AI systems are shaped by human identities and social structures, and that recognising this is essential for educators, researchers, and anyone working with data.
A key idea running through Thema’s seminar was that data and algorithms are not neutral. They are shaped by the people, institutions, and systems that produce them.
Thema uses critical quantitative and intersectional approaches in her work to:
Thema and her collaborators have been conducting research in this area for more than a decade, developing techniques that systematically measure bias and its impact on society.
In a groundbreaking study published in 2022, just before the release of ChatGPT, Thema’s team used large-scale computational analysis of more than 5 million research articles to explore inequalities in scientific publishing. The data analysis approaches developed for this study were later used to explore bias in large language models (LLMs).
However, the 2022 study already demonstrated wide-reaching disparities in science and surfaced deep-rooted issues, showing that bias was already ingrained in the scientific data that was used to train LLM, and affecting topic choices, citation and institutional differences.
The results showed clear inequalities in the relationship between identity and topic choice. Authors from marginalised groups were more likely to study topics related to their communities and lived realities, including topics such as racial disparities and discrimination. Gendered patterns also appeared, with women publishing more frequently on more feminised topics, including families, literacy, learning, nursing, and pregnancy.
The study also found citation inequalities. Even among authors studying the same topic, authors from some groups were cited less often than others, with black and Latinx women the least likely to be cited. This shows that inequality is not only present in what gets studied, but also in whose work is recognised.
Institutional context mattered too. Researchers at mission-driven institutions were more likely to publish on topics connected to marginalised communities, while scholars at institutions seen as elite were more likely to publish on topics that aligned more closely with dominant groups and norms.
Taken together, the findings point to a simple but important idea: who we are shapes what knowledge gets produced. That matters because when some groups are underrepresented in research, the topics that affect their lives may also be understudied.
Having already developed their tool for name analysis for the previous study, Thema’s team was uniquely positioned to analyse the bias embedded in generative AI systems, specifically LLMs.
Thema’s most recent study examined how LLM–based tools represent people in everyday scenarios. The research team prompted the base models of LLM chatbots (such as Open AI’s ChatGPT, Anthropic’s Claude, Meta’s Llama, and Google’s PaLM or Gemini) to write short stories about students, workers, and relationships, generating 500,000 outputs across different domains. They then analysed how names associated with different racial and gender identities were portrayed.
One example Thema shared in the seminar described a student named “John” helping “Maria,” a student who had moved from Mexico and was struggling with Spanish. At first glance, this may seem like a small or even odd detail. But when oddities like this appear again and again across thousands of stories, they reveal systematic patterns.
The study found that characters with marginalised identities were more likely to be portrayed in subordinated roles in chatbot outputs. Characters with non-white-associated names were more often shown as needing help rather than offering it. Stereotypes were also reinforced, with some names repeatedly associated with struggling students, subordinate workers, or narrow professional roles. Some groups were omitted altogether, while white-associated names appeared more frequently and in more powerful positions.
Similar biases appeared across stories related to education, work, and relationships. Across all three topics, the most common pattern was one in which white characters were more likely to lead, rescue, or mentor, while non-white characters were more likely to be helped, corrected, or spoken for.
For educators, this is especially important because many AI tools are now being introduced into classroom settings as writing assistants, tutors, or sources of personalised feedback. When these tools reproduce biases and unequal assumptions, they can shape not only what students read, but also how students see themselves and one another.
Rather than rejecting computational methods altogether, Thema argued for using them more thoughtfully and responsibly.
One approach she highlighted is the Wells-Du Bois protocol, a framework designed to support bias mitigation, transparency, and more reflective use of data and models. It encourages researchers and practitioners to think carefully about inadequate or biased data, identity proxies, subpopulation differences, and the kinds of harms that can arise when AI systems are used without sufficient context.
Underlying this is a broader principle: when we do not know enough, we should say so. And when systems affect marginalised communities, those communities should not be an afterthought in how we build, evaluate, or use technology.
In her seminar, Thema emphasised the importance of thinking about how we respond to bias in AI tools in educational settings. Here are some starting points for meaningful discussions in your classroom:
If you would like to find out more about Thema’s work, watch the seminar recording:
You may also want to explore:
Our research seminars bring together educators and researchers to explore key questions in computing education.
Next in our series on applied AI, our Director of Research and Impact, Shuchi Grover, will talk about the role of K–12 education in developing competencies for the future of data and computing. Sign up now to join the seminar on 12 May, 17:00 BST:
The post AI is not neutral: What recent research says about bias, identity, and power appeared first on Raspberry Pi Foundation.
At a time when artificial intelligence (AI) systems and tools based on large language models (LLMs) are being rapidly introduced into industries and daily life, the basic definition of ‘thinking’ and the essential skills we teach the next generation are being called into question.
In this interview, Dr Shuchi Grover, a leading voice in computing education who has recently become our Director of Research and Impact, shares how her work in computational thinking is evolving.
Most people in the education and CS education world know me from my research in computational thinking and K–12 CS education over the last 15 years. What is less known, perhaps, is that I started my career as a software engineer after completing my undergraduate and graduate studies in CS. About 25 years ago, I made a concerted shift to education, completing a Masters in Education from Harvard University in 2003, and then after a gap earning a PhD in the learning sciences (with a focus on K–12 CS education) from Stanford University in 2014.
Over these last two and a half decades, I have trained my efforts on helping young learners and school-aged children develop 21st-century competencies in computer science, data science, AI, and cybersecurity; as well as on STEM and non-STEM learning experiences that integrate computational thinking, AI, CS, and data science. My research has also attended to promoting interest and a sense of belonging in CS among learners from historically underrepresented groups.
I recently joined the Raspberry Pi Foundation as Director of Research and Impact. I feel very fortunate, as this role builds on all the work I have done over the course of my professional life and also affords me an unparalleled opportunity on a global scale to continue this work I’ve been so passionate about in both formal and non-formal learning settings.
This question is deep and thorny, and I’m not sure we have a complete answer to it yet. I believe that thinking as a human endeavour continues to be valid and means what it always has meant: a cognitive process that involves making new connections and creating meaning. In the education literature, thinking is often equated to problem solving. So teaching students ‘thinking skills’ has meant teaching them logic and ways to solve problems — typically in the context of a domain. In the context of K–12 CS education, computational thinking essentially means computational problem solving.
What changes with LLMs is not the definition of thinking itself, but rather what thinking skills students need most urgently. For students, the idea of ‘critical thinking’ has become much more critical (no pun intended) in an era when LLM-based tools offer quick and easy ways to produce answers. Students need to be equipped with the skills to evaluate AI outputs, and to follow up in deliberate and mindful ways to ensure that the AI-generated answer they ultimately take away is factually accurate, unbiased (to the extent that it can be), and valid for their context. They should also have the ability to recognise when an output is not suitable for their purposes, and when they would be better off approaching a problem or project as they would have in the pre-LLM era. These kinds of metacognition and evaluation skills must be crucial elements of AI literacy training.
Over the past 5 to 10 years, the scope, pervasiveness, and complexity of computing applications have grown substantially. This growth has been propelled by developments in AI and machine learning (ML). Many of the ML methods that underpin these developments have been in existence for much longer, but two key ingredients were still needed: large quantities of data, and the requisite computational power to process those quantities of data efficiently. Around 10 years ago, these became a reality. Combining so-called ‘big data’ captured from the countless human activities on the World Wide Web with new, powerful graphics processing units (GPUs) enabled AI scientists to build powerful prediction, classification and, most recently, generative AI models. Thus these scientists ushered in a new paradigm of computing that is data-driven.
This has expanded the scope of what we need to teach students as part of CS education. In the context of AI and ML, you now have traditional programs that follow the algorithmic, deterministic paradigm of programming, but also ML applications that follow a data-driven, non-deterministic/probabilistic paradigm. CS curricula must help students develop an understanding of both. And data and data science are the crucial connective tissue between CS and AI/ML, so data literacy (which also captures elements of data agency and data equity) is critical to CS and AI learning experiences.
Ethical issues in the context of data and AI have become more heightened and pertinent: issues of data privacy, safety, bias, responsible and explainable AI, and most importantly, impacts of AI systems on society. Understanding of these issues — what we can call ‘sociotechnical literacy’ — needs to be much more central to CS education now.
Let me begin by saying that there is no AI without CS. So understanding CS is important and foundational even in this age of AI and LLMs. The rationale for teaching CS and coding to learners aged 5 to 18 has always been primarily about (a) preparing the next generation to understand, and thrive in, a world where countless aspects of day-to-day life are driven by computing, and (b) providing them with the tools and skills for problem solving and creative expression. That goal has not changed. Foundational coding skills are still important and relevant for learners.
However, there is the new reality we must contend with: it is now easy to produce accurate code using LLM-based tools. We need good research on what this means in terms of how we teach coding. There are many questions related to this issue for which we need empirical evidence: What are the foundational skills for programming effectively with AI tools? What CS topics, skills, and concepts must we emphasise or de-emphasise? Could teachers be supported by generative AI tools in teaching coding, and if so, how? Will use of AI tools result in poor learning for students? How might students leverage LLM tools in ways that don’t harm their foundational understanding of coding concepts, and at what age and stage? What kinds of LLM tools are safe and suitable, and what preparation must students have before they use them? What bigger, more sophisticated projects might students create with the help of an LLM tool? How might LLM tools aid student learning through formative feedback? Can LLM tools aid in metacognition by prompting reflection at the right moments in a project? These are just some of the many, many questions we need to answer to shape CS education over the coming years.
A version of this interview also appears in issue 29 of Hello World, available as a free download. Subscribe to the magazine to never miss an upcoming issue.
The post What does ‘thinking’ mean now? appeared first on Raspberry Pi Foundation.
AI is shaping the world young people are growing up in, and understanding how it works, as well as its benefits and risks, is now essential.
That’s the goal of Experience AI, our global education programme created in collaboration with Google DeepMind. Today, as we celebrate three years of the programme, we’re sharing our latest impact report, highlighting how the programme is helping educators and young people around the world build the knowledge and confidence to engage with AI critically and responsibly.
Since launching 3 years ago in April 2023, Experience AI has grown into a truly global initiative:
These numbers reflect the growing global demand for AI literacy teaching, and the power of partnerships to meet that need.
But the real impact is what happens in classrooms.
For John Pierce, a teacher at Mwingo Academy Primary School in Kenya, AI once felt out of reach. “I thought that AI was complicated — maybe a puzzle”
After taking part in Experience AI training, John’s perspective shifted. With structured lessons and ready-to-use resources, he now helps his students see AI as something they can understand and engage with.
“My learners are really enjoying the lessons… They keep asking, ‘Teacher, when are you having computer classes?’”
John’s experience reflects what we see across the programme: when teachers feel confident, students become curious, engaged, and motivated to learn more, often continuing those conversations beyond the classroom.
A core focus of Experience AI is supporting educators, many of whom are new to teaching AI concepts. Our evaluation shows this approach is working:
In Malaysia, educator Lee Siew Ling had previously struggled to explain AI concepts clearly. “Before this, I just shared simple examples… It was too hard to explain the concepts clearly to my students.”
With Experience AI resources, that changed. “The materials make it easier to teach AI… After using them, I became more confident to guide my students.”
By reducing preparation time and providing clear, structured lessons, the programme enables teachers to focus on what matters most: supporting their students’ learning.
The impact extends directly to learners. Across classrooms worldwide:
This is critical. AI literacy isn’t just about using technology, it’s about understanding how it works, questioning it, and recognising its societal impact.
At Wymondham College in the UK, Head of Computing, Kim Williams highlights the value of having trusted, research-informed resources: “Experience AI gave us a real structure to follow… It helps us deal with misconceptions and gives students the right messages.”
Through these lessons, students are not just learning about AI, they are developing the critical thinking skills they need to navigate a world shaped by it.
Experience AI’s reach is only possible through collaboration. Working with partners around the world, we localise content to make it relevant to different cultures and contexts, ensuring that AI education is not only accessible, but meaningful.
This work has also been recognised globally. In 2025, Experience AI was named a laureate of the UNESCO King Hamad Bin Isa Al-Khalifa Prize for the Use of ICT in Education, highlighting its strong ethical foundations and international impact.
We’re continuing to expand and evolve the programme, updating resources, developing new materials for different age groups, and growing our global partner network.
By the end of 2026, we expect to reach over 45,000 educators who can reach an estimated 4.4 million young people.
Because the challenge is clear: AI literacy should not be limited to a few. Every young person deserves the opportunity to understand and shape the technologies influencing their future.
Read the full Experience AI impact report to explore the data, stories, and insights behind this work. rpf.io/expai-impact
The post Experience AI: Reaching millions of young people with AI literacy appeared first on Raspberry Pi Foundation.
En avril 2026, la Lune devrait attirer tous les regards, alors que l'humanité revient à proximité du satellite naturel, après plus de 50 ans d'absence. Mais il y aura d'autres phénomènes astronomiques à ne pas manquer.
We’re excited to share that we are expanding our Experience AI programme across Latin America with the aim of training 24,000 educators and reaching 1.25 million students by 2028, thanks to generous funding of $4.6 million from Google.org.
Working with education partners across Argentina, Brazil, Chile, Colombia, Dominican Republic, El Salvador, Mexico, Peru, and Uruguay, we will help young people develop a foundational understanding of AI technologies, their social and ethical implications, and the role that AI can play in their lives.
AI systems are part of everyday life in how we find information, work, and solve problems. We think that young people need more than access to AI tools: they need the knowledge, skills, and confidence to understand and create their own AI tools.
Experience AI, developed in partnership with Google DeepMind, is a free educational programme that helps teachers and students learn about artificial intelligence (AI). It introduces young people to how AI systems work and how they are used in everyday contexts through lessons, classroom resources, and hands-on activities. The resources give young people opportunities to think critically about the role of AI in society.
Through a global network of Experience AI partners, we have so far reached an estimated 2.9m young people and trained 30,000 educators. The programme’s resources are used in more than 180 countries and are available in 19 languages. In recognition of its impact, Experience AI was named a laureate of the 2025 UNESCO King Hamad Bin Isa Al-Khalifa Prize for the Use of ICT in Education.
In Latin America, as in other parts of the Experience AI network, our focus will be on sustainable, locally led delivery through partner organisations. Using our established ‘train-the-trainer’ model, we will equip 24,000 educators with the skills and knowledge to use the Experience AI resources to confidently deliver AI literacy lessons.
Our aim is to create a lasting impact for teachers and classrooms across the region and ensure that high-quality AI education is accessible to young people in a wide range of settings.
Experience AI is designed not only to build technical understanding, but also to help young people think critically about AI and its impacts.
Through the programme, students across Latin America will develop a foundational understanding of how AI works, while exploring key topics including how data is used in AI systems, how to identify AI-generated misinformation, and how to use generative AI tools responsibly. This will help them to understand the opportunities and challenges of AI, and to make informed decisions about how they use these technologies.
As AI systems are being built into many aspects of today’s world, it’s essential that young people have the opportunity to understand, question, and build with these technologies.
With support from Google.org, we will expand access to high-quality AI education across Latin America through Experience AI, helping over a million young people develop the skills, knowledge, and confidence to navigate and shape a world where AI technologies are widely used.
You can find out more about Experience AI at experience-ai.org.
The post Bringing AI education to 1.25 million students across Latin America appeared first on Raspberry Pi Foundation.
Certaines planètes solitaires qui n'orbitent pas autour d'une étoile pourraient tout de même rassembler les conditions pour voir apparaître la vie. Leurs lunes seraient protégées par leur atmosphère, ce qui serait suffisant pour que de l'eau liquide y subsiste.
Teaching about AI concepts in schools is a tricky business as there are complicated ideas to be taught.
To teach complex concepts, in computer science, we often use an instructional approach called ‘unplugged’. We use the unplugged approach to teach computing concepts without a computer. Often unplugged activities include using an everyday analogy or a physical fun activity. For example, to teach about algorithms, students might learn how to make a jam sandwich where the recipe and following instructions accurately are similar to an algorithm and the steps within it used to write a program. The jam sandwich activity has now become a popular and key teaching experience for young students across the world, as it teaches a complex but fundamental idea in a simple and fun way.
At the January 2026 Raspberry Pi Foundation Research Seminar, Salomey Afua Addo, a researcher at the University of Cambridge, presented her work about how to teach about AI. She has specifically looked at this in the context of high school students in Ghana, where AI is now part of the mandatory curriculum. In Ghana, most schools do not have access to computers, therefore an unplugged approach to teach about AI is a good idea. Therefore, Salomey developed a set of unplugged activities to teach about a range of AI concepts.
Here, I focus on one of the activities that she presented — one that I think will become another ‘jam sandwich’ experience for students. So if you might teach about AI at some point, then read on.
Salomey has designed an unplugged role-play activity to teach about neural networks and how they are trained to solve a problem. She focused on finding a familiar problem context for Ghanaian teachers and their students, and selected farming and crop disease. Students are asked to figure out what features about a farm are relevant for detecting diseases on cocoa trees. To solve the problem, students are given data about the farms (see Table 1). Giving students data, rather than preconceived rules about the context is key to the learning activity. Neural networks are data-driven — they provide a way to model given data so that we can make predictions. Here the features of farms, and importantly whether disease is or is not found in their cocoa trees, is the data that is used to train a model. The model is used to make predictions, which can then be used to improve farming by reducing crop disease.
Using farm data, students can learn how neural networks work, and they can do this through an unplugged role play — using ropes!
Here’s how Salomey’s classroom activity works. Sets of students act out the processes of training a neural network, including forward propagation, evaluation, and backpropagation. They take on the “roles’’ of some of the concepts of a neural network. One student acts as the supervisor, six students act as the input layer, two as the hidden layer, and one as the output layer.
Key concepts are simplified for students:
Simple numerical data about the features of the problem are given to the students, such as whether the “Temperature” is suitable (0=No, 1=Yes), if there are “Spots” on the plant (0=No, 1=Yes), if “Fertilizer’’ has been used, whether the “Leaf colour’’ is green or not (see Table 1). Importantly, each of the six features given are represented by the six “input layer” students. So each student can ‘process’ each feature as the data for a given farm is used to train the model. Cards are used to represent the data values passed between layers. And this is where the ropes come into play, as they are used to represent the connections between the nodes in the layers.
Written role-specific instructions are provided for the students to follow, for example, the Supervisor is given three steps to follow for the forward propagation stage, and the Input Layer students receive a different set of instructions and so on. The detail of the role play is shown in the instruction sheets (see Figure 1).
Using ropes to connect the nodes becomes most important at the reverse propagation stage. The clever part of this is that we can show an increase or decrease in the strength of connection by adding or removing ropes. For me, this is the ‘jam sandwich’ effect. This, I think, is probably the most significant learning point. Here, the number of ropes that connect the nodes in the layers are changed based on the strength of evidence that a particular feature is indicated, by the data, to be relevant to the output. In this case, whether “Temperature”, for example, has an implied effect on cocoa disease or not — based on the data, not on any preconceived rule. Simply put, if a farm did have disease then a rope is added, if a farm did not then a rope is removed. Or at a more abstracted level, if a particular neuron contributes towards the correct prediction, a rope is added, otherwise a rope is removed. In a real neural network, backpropagation involves complex maths, such as calculus that would not be accessible to students of this age. Therefore, the rope is an analogy that replaces something that would be impossible for these students to grasp if it was taught using the real-world implementation.
Problem to be solved in the unplugged activity: Identify features that are relevant for detecting diseases
At the end of the activity, features (temperature, leaf color, family farm, etc.) with many rope connections are considered to be relevant for crop disease detection on the farm, whereas features with fewer rope connections are considered to be irrelevant for crop disease detection. The more ropes attached to a particular feature, e.g., temperature, represent its higher relevance in identifying crop disease on the farm.
As part of the design of this activity, Salomey has simplified technical language so that throughout the role play students use everyday terms and she has chosen a context that is relatable for the students. For example, she uses the language of trust, and the new thickness of a rope connection, rather than using technical terms such as weight, loss function, and the error of the network.
Salomey also designed a follow-on activity that uses pen and paper. In this version of the activity, which she calls a board game, the students draw lines to connect the nodes in the layers. The thickness of the lines connecting the nodes represent the strength of the trust (see Figure 2).
Salomey also shared her evaluation of the resources. She conducted pre‑ and post‑intervention surveys with 39 teachers as part of the professional development on the AI teaching materials, and ten of those teachers implemented the unplugged activities in their classrooms. She reported that the teachers found the role-play activity was effective to demonstrate neural networks, that children worked independently to learn, and that some students who did not take part usually in class were engaged.
As well as sharing about her unplugged neural network activity, Salomey also talked about a set of AI stories that she has developed to teach about other aspects of AI applications. For example, the importance of fact-checking is demonstrated through a story about a young girl who fact-checked information she received from her friends about life in a city.
If you would like to find out more about Salomey’s work, you can find related materials on our seminar website.
Join us at our next seminar on Tuesday 17 March from 17:00 to 18:30 GMT to hear Rebecca Fiebrink (University of the Arts London) speak about teaching AI for creative practitioners. This will be the second seminar in our new series on how to teach about AI across disciplines. We hope to see you there!
To sign up and take part in our research seminars, click below:
You can also view the schedule of our upcoming seminars, and catch up on past seminars on our previous seminars page.
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Google introduces an AI Professional Certificate with six courses and a capstone, plus 20+ hands-on activities, and free access for small businesses.
The post Google’s New AI Certification Teaches Prompts, Research, and App Building appeared first on TechRepublic.
Mars 2026 commence, avec un nouveau programme astronomique bien rempli. Le mois du printemps sera marqué par une éclipse lunaire et des planètes visibles à l'œil nu. Voici les dates à retenir.
Measuring student understanding in computing education is not an easy task. As AI literacy becomes an important pillar in computing education, defining and accurately measuring students’ understanding of concepts and their skills is an even greater challenge.
In a recent seminar in our series on teaching about AI and data science, researcher Jesús Moreno-León (Universidad de Sevilla) talked about his work in developing assessment tools for computational thinking (CT) and AI literacy. Jesús is also co-founder of Programamos, a non-profit organisation that promotes the development of computational thinking, supporting teachers through training and sharing resources.
Jesús began by discussing the recent development of computer science assessment tools. Together with Gregorio Robles (Universidad Rey Juan Carlos), they created Dr Scratch, a web-based tool to assess the quality of Scratch projects and detect errors and bad programming habits (e.g. dead code). Projects are scored on the use of computational thinking concepts (e.g. parallelism, conditional logic) and the use of desirable programming practices (e.g. naming sprites, removing duplicate scripts) in order to give feedback to students and teachers to iteratively improve their Scratch projects.
Alongside measuring students’ programming skills, Jesús also shared work by Marcos Román-González (Universidad Nacional de Educación a Distancia) to develop the Computational Thinking test (CTt), a 28-item assessment tool designed to measure the computational thinking skills of students aged 10 to 16 years old. Two collaborators, María Zapata and Estafanía Martín (Universidad Rey Juan Carlos) further adapted these items to create the Beginners Computational Thinking test (or BCTt), an unplugged assessment suitable for younger learners aged 5 to 10 years old.
Jesús also described his more recent work at the Ministry of Education and Vocational Training in Spain to promote computer science at all educational levels. One initiative, La Escuela de Pensamiento Computacional e Inteligencia Artificial (or the School of Computational Thinking and Artificial Intelligence), supported Spanish teachers through training and resources to introduce CT and AI into the classroom. Over 400 teachers and 7000 teachers took part across Spain through unplugged activities and tools such as Machine Learning for Kids and LearningML, allowing students to classify text and images using machine learning. Older students created apps using the MIT App Inventor. When evaluating the design of the curriculum, they found they had strong instruments to measure the development of CT — such as the assessment tools described above — yet nothing to measure AI literacy.
The lack of valid AI literacy assessment tools led the team to develop the AI Knowledge Test (or AIKT), a 14-item survey consisting of multiple-choice questions designed to measure students’ understanding of AI. The instrument was inspired by previous work in the field and relevant research (e.g. the AI4K12 framework).
An example of one of these items is presented below. Can you solve it? The answer is at the bottom of this article.
Q1. Which of the following strategies would be most appropriate for teaching a computer to recognise photos of apples?
In a study on the impact of programming activities on computational thinking and AI literacy in Spanish schools, the authors tested these knowledge-based items with over 2000 students to assess the reliability (e.g. internal consistency), or a measure of the quality of a survey or test. They found one item (“As a user, the legal regulation that is approved regarding AI systems will affect my life”) did not correlate with the other items. This left a total of 13 items which were found to have sufficient internal consistency — meaning how well each item correlated with one another to measure an underlying construct (i.e. “AI knowledge”). They concluded that the assessment tool needed a higher ceiling and needed to address common misconceptions. The authors also learned that teachers needed free and open-source tools with low barriers for entry, such as not needing registration, and were suitable for classroom use, such as limiting data sent to the cloud.
With the rise of generative AI tools like ChatGPT or Google’s Gemini, Jesús and his colleagues felt their AI literacy assessment tool needed to focus on the capabilities of generative AI tools. They also felt they needed to take a broader view of AI and focus on additional dimensions, such as the social and ethical implications of AI tools. They are, therefore, currently revising their assessment items to align with several common frameworks, including the SEAME framework and AI Learning Priorities for All K–12 Students.
One of the revised items is presented below. Can you solve it? The answer is revealed below.
Q2. You have asked your students to design a decision tree to classify different fruits based on three characteristics: color, size, and shape. To check whether the following proposed solution is correct, you are going to test it. As what fruit does the decision tree classify a small, round, yellow apple?
Jesús concluded the seminar by describing his intentions to collaborate with others to test the revised AI literacy instrument with students in early 2026. We look forward to hearing about their results!
You can watch Jesús’s whole seminar here:
If you are interested to learn more about Jesús and his work, you can read about his development of the AI Knowledge Test (or AIKT) here and the Computational Thinking test (CTt) here or look at the original items here. You can also learn about the Beginners Computational Thinking test (BCTt) by watching a Raspberry Pi research seminar about it or reading about it here.
In our current seminar series, we’re exploring applied AI and how AI can be taught across the curriculum. In our next seminar in this series on 17 March at 17.00 UK time, we welcome Rebecca Fiebrink (University of the Arts London) who will explore the questions of how and why we might teach AI for creative practitioners, including children, students, and professionals.
To take part in the seminar, click the button below to register. We hope to see you there.
The schedule of our upcoming seminars is available online. You can catch up on past seminars on our blog and on the previous seminars and recordings page.
The post The challenges of measuring AI literacy appeared first on Raspberry Pi Foundation.
Learning Python often starts with the same building blocks: variables, functions, and loops. However, once young people have learnt these essential foundations, they may be eager to grow their skills and start using Python to explore data and create something meaningful to them.
Our free ‘More Python’ project path helps learners move beyond the basics and use data to create impactful projects of their own.
Python is the most widely used programming language in the world, not just because it’s accessible, but because it’s powerful. It is used to analyse data, build models, create data visualisations, and explore important questions.
For young learners, this means learning Python can become more than a coding exercise. It can be a way to investigate topics they care about, analyse and understand information, and tell powerful stories about real-world issues.
Working with data helps learners see how coding connects to the world around them — and builds confidence along the way.
In our day-to-day lives, data is everywhere: in sports results, maps, and scientific research, to name only a few examples. Learning how to work with data helps young people develop skills that go far beyond programming, including:
Data also underpins many of the AI systems people use today. For example, large language models, used to build tools such as ChatGPT, are trained on vast amounts of data. Therefore, understanding how data is collected, organised, and used is an important part of AI literacy.
In Python, structures like lists and dictionaries make it possible to organise, analyse, and explore data in creative ways. Using these tools to build projects can help abstract computing concepts start to feel more concrete and meaningful.
The ‘More Python’ project path supports learners through three stages: Explore, Design, and Invent. Each stage builds skills while giving learners more ownership over what they create.
In the Explore stage, young people learn new concepts and build confidence in using data and core Python structures, such as lists and dictionaries. Projects include:
These projects help learners develop new skills, while exploring how Python can be used to analyse and explain real-world information.
As learners progress to the Design stage, they start making creative choices about how their projects look and behave. In this stage, they:
Here, Python becomes a creative medium. As well as putting their new skills into practice, learners think about audience, interaction, and presentation to make their projects their own.
In the Invent stage, learners bring everything together. Using the skills they have built, they design and create a data visualisation on a topic they are passionate about. This final project gives learners the freedom to choose their data, shape their idea, and tell a story that matters to them.
By this point, learners are planning and creating their own projects, growing in confidence and independence.
If the young people you support have already learned the basics of Python, ‘More Python’ offers a clear and creative next step. The projects are designed to be accessible, and young people can work through them at their own pace, whether they are learning independently, at a Code Club, or in the classroom.
By working with data, getting creative, and making their own original projects, learners can build confidence and start to see what they can achieve with Python.
Alongside the ‘More Python’ project path, you can access hundreds of free coding projects on our Code Club Projects site. Find more projects to suit your learners’ interests, and support them to build their digital skills through creativity and making.
The post Levelling up with Python: Create with data appeared first on Raspberry Pi Foundation.
Where do great research ideas come from in computer science education? We might think of research breakthroughs as a single moment of genius, but in reality impactful research is often the result of many years of iterative development. In November’s research seminar, we heard from Karl-Emil Kjær Bilstrup, a researcher at the University of Copenhagen, about his work to develop ML-Machine. This work uses embodied learning principles and the BBC micro:bit to introduce learners to machine learning concepts. Findings from this research have been used to develop the micro:bit CreateAI resources, and in this blog, we will explain the research journey from initial small-scale work to educational resources used by many young learners around the world.
In Karl-Emil’s first research study, students used prompt cards to develop ideas for machine learning applications that could solve real-world problems, and to discuss the ethical dilemmas associated with their solutions. Students found it difficult to address these ethical dilemmas in their designs; for example, their ideas often featured a trade-off of user privacy. The findings from this research informed Karl-Emil’s next study, which moved from hypothetical scenarios to implementing machine learning in real-world settings.
The ‘Machine Learning Machine’ study made machine learning processes tangible for students through the use of two physical boxes, shown in the picture below. Students created drawings and fed them into the first box to train a model, and then tested the model by placing new drawings under a camera in the second box and having the model produce predictions of what the drawings showed. For example, students could draw pictures of the sun to represent daytime and the moon to represent nighttime to train a model to predict whether new drawings represented day or night. The machine was built for slow interaction, giving students time to think about the concepts and practices that they were developing. In a follow-up study, a new version of the Machine Learning Machine had been designed, which was controlled using a graphical user interface (GUI). This allowed users to “unbox” and influence parts of the machine learning process. For example, students could adjust the number of complete passes (called ‘epochs’) through the training data to improve the model’s accuracy.
The two studies with the Machine Learning Machines provided many useful findings for teaching about machine learning with K–12 (primary and secondary) learners. However, two constraints remained: firstly, there were limited opportunities for whole-class work because there was only one Machine Learning Machine, and secondly, learning experiences needed to be better connected to examples from students’ daily lives. As a result, the next iteration in Karl-Emil’s research involved using the micro:bit, which ensured access to a tangible device for every student, and a new graphical platform called ML-Machine that students could interact with.
The micro:bit is a small, programmable computing device that features sensors to gather data from the immediate environment. For example, the accelerometer is a motion sensor that can detect when the micro:bit is tilted from left to right, backwards and forwards, and up and down. Using the micro:bit with ML-Machine and some common household objects, students can create simple machine learning models that use data from the micro:bit’s accelerometer to detect whether the micro:bit is moving. This is a very different approach from rule-based programs on the micro:bit, where students might use programming constructs such as if statements to detect movement if the numerical reading from the accelerometer is above a certain value. Here, a machine learning model trained using a set of 20 examples is used to analyse live data readings and produce predictions about whether the micro:bit is moving.
In our seminar, Karl-Emil gave a live demonstration of the ML-Machine toolkit, so we highly recommend watching the recording to see how this toolkit brings machine learning concepts to life.
ML-Machine is the precursor to the micro:bit CreateAI resources, and the software is fully open-source. However, the innovation doesn’t stop there: Karl-Emil also explained that he is currently developing a new tool called math.ml-machine.org, where students can train a neural network and see a visualised k-nearest neighbour model to explore how a model makes predictions. The research journey is continuing, with new possibilities for educational opportunities to teach about machine learning.
The idea of embodied learning is interwoven throughout all of Karl-Emil’s research projects and is a cornerstone of all of his work. Embodied learning suggests that we learn more effectively when our whole body is involved in the learning process, not just our minds. For example, in the work described in this seminar, the Machine Learning Machines and the micro:bit were all tangible devices that students could touch and see.
Embodied learning is particularly important in activities that involve working with data-driven systems. In traditional programming activities, the flow of code can be traced transparently through a program. However, machine learning models are more opaque, and their outputs cannot be traced step by step. Students can benefit from using bodily movements and sensorimotor information to help understand machine learning concepts.
The ML-Machine toolkit was designed to support students to learn through embodied learning in three different ways:
Embodied learning helped students grasp concepts such as data quality. They could see how their bodily movements were being translated into digital data, and could spot when movements that appeared different to them were being classified as similar by the ML-Machine model. One case study participant described that the immediate feedback on screen made the concept of machine learning feel as if it were “coming to life as they [the students] manipulate something themselves and they’ve got control over it”.
Karl-Emil’s work shows how research ideas can be used in the classroom through a cycle of discovery, design, and reflection. From prompt cards exploring ethics to tangible machines and the micro:bit-based ML-Machine, his research shows how embodied learning can make complex ideas like machine learning not only understandable, but deeply engaging for young learners. The micro:bit CreateAI resources are a great example of how research findings can evolve into accessible, hands-on tools that empower educators and students alike. As this work continues to grow, it invites us to imagine new ways for learners to experience machine learning not as abstract theory, but as something they can see, feel, and shape with their own hands.
If you’d like to try out some of the ideas from this seminar, here are some useful resources:
Join us at our next seminar on Tuesday 17 March from 17:00 to 18:30 GMT to hear Rebecca Fiebrink (University of the Arts London speak about teaching AI for creative practitioners. This will be the second seminar in our new series on how to teach about AI across disciplines. We hope to see you there!
To sign up and take part in our research seminars, click below:
You can also view the schedule of our upcoming seminars, and catch up on past seminars on our previous seminars page.
The post Embodied machine learning: From research ideas to classroom activities appeared first on Raspberry Pi Foundation.
La frontière entre planète et étoile peut être fine pour certains astres qui flirtent avec les limites. Mais comment se forment ces structures qui défient les modèles scientifiques ? Le télescope spatial James Webb a tenté de répondre à cette question.
The online world that young people navigate today is different from the one we encountered just a few years ago: the search engines, social media platforms and digital tools they use to find information, interact with friends and complete schoolwork are now deeply embedded with AI technologies.
While the core aims of online safety education remain the same, the scope must now expand to include AI literacy: the ability to use, question and navigate AI tools so young people can make responsible choices online.
This is a shared challenge for anyone who supports young people as they navigate the online world: parents and carers, youth leaders and volunteers, and educators across all subjects. Many young people use these AI tools independently, often without guidance, so having open and useful conversations about trust, risk and responsibility matter just as much in the classroom as they do at dinner tables and Code Clubs.
At the Raspberry Pi Foundation, we have developed various AI literacy resources for educators, club leaders and parents to address this challenge in age-appropriate and practical ways. Our ‘AI safety’ resources, part of our Experience AI programme, are a set of free comprehensive teaching activities to support you in educating young people aged 11–14 in navigating key safety issues linked to AI, including privacy, misinformation, trust and responsibility. Delivered through videos, unplugged activities and discussions, the activities are adaptable to a range of learning settings, and reflect the real decisions young people are already making online.
For example, in the ‘Trusted Sources’ activity from the ‘Media literacy in the age of AI’ lesson, young people reflect on the ways they look for information related to schoolwork, news and in their free time. They consider which sources are likely to allow the use of generative AI and how that affects their trustworthiness. Rather than labelling sources as ‘good’ or ‘bad’, learners explore questions around responsibility, credibility and oversight, and build practical skills for fact-checking and staying safe online.
Alongside the ‘AI safety’ resources, we have also developed a new ‘Large Language Models (LLMs)’ unit for learners aged 11–13 and 14–17, currently being tested in classrooms. The unit focuses on another important aspect of online safety: how young people interact responsibly with AI tools that generate content. While helpful, learners’ uncritical use of these tools could lead to cognitive offloading and limit the development of their higher-order thinking skills. The confident, persuasive tone of LLMs can also make it harder for young people to judge accuracy, recognise bias or notice missing information in outputs.
To support the critical thinking skills that are essential for staying safe online, the new LLM unit includes research-informed lessons that explore how LLMs are created, why their outputs are not always accurate and how to evaluate AI-generated responses. The unit also encourages learners to reflect on when using an LLM is helpful to their learning, when it is not, and how they can remain in control of their own thinking, learning and skills development.
Helping young people stay safe online in the age of AI doesn’t require having all the answers. Instead, it’s about creating the space to pause, question, and think critically about what they are encountering online. Through carefully designed, research-informed and pedagogically aligned AI literacy resources, we aim to help you start the conversations that empower young people to think critically, stay curious and remain in control of their learning and online lives.
This Safer Internet Day, we invite educators, parents and anyone who supports young people to explore our AI literacy resources and start the conversation. Visit the Experience AI website for more information.
The post Helping young people stay safe online in the age of AI appeared first on Raspberry Pi Foundation.
Are you a primary school teacher in England, Scotland or Wales interested in AI and data science and how students learn about AI and data in computing?
The Raspberry Pi Computing Education Research Centre is starting an exciting new research project investigating how to teach about AI and data in the primary computing classroom, and we would like you to get involved.
The study will look at:
The study involves attending a workshop in Cambridge, co-designing a unit of work, and then teaching it. Where necessary, we can reimburse you for reasonable expenses, such as supply cover, travel, and accommodation.
Our aim for the study is to understand how primary school teachers approach teaching about data-driven technologies, and to find suitable methods for building young people’s confidence in working with data in computing lessons.
Research has suggested that new data-driven technologies such as AI and machine learning (or ML) require a different approach to teaching about problem-solving in the computing classroom. Instead of defining a set of rules (e.g. if-then-else statements, or a rule-based approach), learners must instead collect lots of data to train a model (a data-driven approach) such as using Google’s Teachable Machine to classify image data.
For educators and resource developers, we still lack a clear understanding of how to teach young people about how rule-based and data-driven systems differ, how we can talk about them, and how we develop young people’ mental models. We hope this study will help us to find practical ways for primary teachers to build young people’s understanding of AI data in the primary computing classroom.
If you teach at primary level (Years 4, 5 and 6 or P5–P7) in England, Scotland or Wales and are keen to shape how we teach young people about data-driven computing, we invite you to join our new study.
As part of the study, you will attend a workshop with us in Cambridge to co-design a series of data-driven computing lessons to teach in your classroom.
Following the workshop, you will teach the unit of work in your classroom and we will observe one of your lessons and interview you about your experiences.
If you are interested in taking part, register your interest by clicking the link below:
If you have any questions about the project, you can email bobby.whyte@raspberrypi.org.
The post Join our new study on AI and data-driven computing in UK primary classrooms appeared first on Raspberry Pi Foundation.
C'est le mois de février 2026, et il est temps de découvrir les phénomènes astronomiques qui vont nous émerveiller pendant 28 jours. Pleine Lune, « parade de planètes », éclipse de Soleil... Demandez le programme du ciel !
At the end of last year, Professor Becky Francis published her long-awaited Curriculum and Assessment Review for England, accompanied by the UK government’s official response. Buried within that response — and not actually proposed in the Review itself — was a notable commitment: to “explore introducing a new Level 3 qualification* in data science and AI, to ensure that more young people can secure high-value skills for the future and that we cement the UK’s position as a global leader in AI and technology.”
This announcement reflects a growing global recognition that young people need more than basic digital literacy — they need a deeper understanding of data, automation, and the rapidly evolving capabilities of AI. Countries around the world, from Singapore to the United States, are already wrestling with how to embed AI education into secondary schooling. England now joins that international conversation.
AI is an everyday technology now. Young people interact with AI systems constantly, often without realising it. Whether they pursue careers in medicine, engineering, the creative industries, or public policy, they will need a foundational understanding of how AI systems work, what their limitations are, and the ethical implications around them.
Yet in England — and in many education systems globally — very few students receive formal teaching about AI. The English national curriculum makes no explicit reference to AI, and specifications for exams taken at the end of high school include only scattered mentions. This gap leaves young people navigating one of the most transformative technologies of their generation with limited guidance.
In 2025, we joined forces with Professor Lord Lionel Tarassenko, one of the UK’s foremost researchers in AI and machine learning, and Simon Peyton Jones, a world-renowned computer scientist and long-time champion of computing education. Together with teachers, school leaders, universities, industry specialists, and exam boards, we have been exploring how we might begin to close the emerging gap in AI and data science education for 16- to 18-year-olds.
Over the past eight months, this collaboration has allowed us to refine our shared thinking and gather insights from a wide network of experts and practitioners. We are delighted that England’s Department for Education has recognised the potential of this work by appointing us to draft the subject content for a possible new qualification in Data Science and AI.
We are delighted that England’s Department for Education has recognised the potential of [the work we’ve been doing] by appointing us to draft the subject content for a possible new qualification in Data Science and AI.
Designing a qualification of this kind raises important questions — not just for the UK, but for any country considering a similar path.
A meaningful qualification must go beyond the use of tools. It should help students understand data literacy, model behaviour, bias, ethics, and the societal implications of AI. Balancing technical understanding with critical thinking is challenging but essential.
AI should not become the preserve of already-advantaged students. Any qualification must be designed with equity in mind, recognising differences in school capacity, teacher expertise, and students’ prior experience.
Teacher professional development is a major challenge worldwide. Delivering a qualification in AI will require confidence with concepts that are not yet common in teacher training. Sustainable delivery models — supported by high-quality resources and professional development — will be crucial.
There is an active debate about whether the best route for students in England is a high-stakes qualification or a supplementary course that broadens a core programme of study:
Different countries will make different choices based on their systems, but the underlying questions are the same: how do we create something rigorous, scalable, and future-proof?
In October, the Foundation hosted a workshop with representatives from schools, industry, universities, exam boards, and the Department for Education. Together, we explored key questions including:
The feedback we received has been invaluable and will continue to shape the next stage of development. We believe the UK has a significant opportunity to contribute meaningfully to the global conversation about AI education. You can read the latest version of our discussion paper here.
Although the current proposal focuses on England, the underlying challenge is international: how do we prepare young people everywhere to engage thoughtfully and confidently with AI?
We would love to hear from educators, researchers, and policymakers across the world:
To share your ideas or feedback, please get in touch. We’d be delighted to learn from your experience as this important work progresses.
* Level 3 in England is the stage of learning for 16- to 19-year-olds, typically ending in qualifications that pave the way for higher study or advanced apprenticeships.
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In Germany, as in many countries, AI topics are rapidly entering formal computer science education. Yet, this haste often risks us focusing on fleeting technological developments rather than fundamental concepts. As computer science educator Viktoriya Olari, from Free University of Berlin, discovered in her research, the fundamental role of data, which powers most modern AI systems, is critically underestimated in many existing frameworks. If students are to become responsible designers of such systems, they can’t afford to treat AI as an opaque box. Rather, they must first master the messy, human process that begins with the data itself.
In our October research seminar, Viktoriya shared the results of her work over the last four years on how schools can shift the focus from the latest technologies to the underlying data. Her research offers a clear structure for what young people should learn about data and how teachers can make it work inside ordinary classrooms.
Viktoriya’s analysis of existing AI education frameworks found the data domain is underrepresented, with essentials such as data cleaning often not addressed at all. She argues that, because modern AI systems are data driven, students need both language and routines for working with data: being able to name concepts like training vs test data, data quality, and bias, and to explain practices such as collection, cleaning, and pre-processing. That’s the rationale for teaching data concepts and practices first, and then placing modelling inside an explicit, staged lifecycle.
Her talk presented this argument in the German school context, where AI topics are entering state curricula quickly. Her critique targets how existing frameworks fail to address data and how that gap undermines responsible evaluation and design. The proposed model centres data by pairing an eight-stage, data-driven lifecycle with a curated set of key concepts and practices, and by making “data-based judgment skills” a key outcome.
Viktoriya’s work organises this understanding into two foundational components: data concepts (the vocabulary, e.g. training/test data, data quality, overfitting) and data practices (the actions, e.g. collect, clean, train, evaluate).
Viktoriya’s framework is built around an eight-stage data lifecycle, stretching from defining a task through gathering, preparing, modeling, evaluating, and finally sharing or archiving results. Inside that backbone she has identified two layers of learning targets:
More details are available in her work on data-related concepts and practice.
Viktoriya’s 8-stage process model of the data-driven lifecycle. It serves as a guide for curriculum developers and teachers, outlining 28 key data-related practices and providing 69 examples of subpractices for use in K–12 computer science education.
A collection of 133 key data-related concepts. These concepts are organised according to the eight stages of the data-driven lifecycle and provide the foundational vocabulary for teaching AI education.
Viktoriya’s team set out to redesign the format so that real data work could happen within ordinary lessons. They ended up with three “Data Case Study” architectures, each using authentic datasets and domain questions. The materials are supported by Orange 3, an unplugged machine learning and data visualisations tool familiar to the teachers participating. Variants emerged across three design cycles to address specific challenges, but teachers choose among them based on learning objectives and class context.
The data case study method uses real-world data and context to help students achieve three key learning outcomes: go through the data-driven lifecycle, reflect on data practices and concepts in a criteria-guided manner, and develop data-based problem-solving and judgment skills.
Viktoriya’s team ran three design cycles with small groups in Germany, with students aged 14 to 15. Each cycle lasted around 48 hours of teaching. Because participating teachers already knew Orange 3, the emphasis was on pedagogy rather than software training.
The projects drew on manageable real-world data: spreadsheets, time-series sets, a few geographical samples. Two examples are:
Some groups experimented with collecting their own sensor data, a plan that occasionally failed when the hardware didn’t cooperate. However, even that became part of the lesson: reliability, risk, and missing data are real features of data science, not mistakes to hide.
Student work reflected the three architectures. In the bottom-up groups, guided builds produced complete workflows and concise reflections, while top-down groups submitted annotated screenshots and critiques, and the puzzle-based lessons ended with posters and verbal presentations. Across them all, assessment focused on reasoning: not whether the “right” model appeared, but whether students could explain the stage they were in and justify their choices.
Everything Viktoriya described is open and classroom-ready (currently in German). The computingeducation.de/proj-datacases hub hosts teacher guides, student tasks, and sample Orange 3 files. The growing library of data cases covers topics from climate data to air quality analytics.
In the UK, a curriculum review has been recently released and along with the Government’s response. Across Europe and beyond, education systems are racing to add AI content to their curricula. Tools will come and go, and benchmarks will keep moving. What endures is the capacity to reason about data: to know what stage of work you’re in, what evidence supports your decisions, and what trade-offs you’re making. That is why Viktoriya’s contribution is unique — it gives teachers a map, a shared vocabulary, and practical ways to make data visible and the focus of discussion in schools.
You can read this blog to see how we’ve used Viktoriya’s framework in our work designing a data science curriculum for schools.
Join us at our seminar on Tuesday 27 January from 17:00 to 18:30 GMT to hear Salomey Afua Addo talk about how to teach about neural networks in Junior High Schools in Ghana.
To sign up and take part, click the button below. We’ll then send you information about joining.
We hope to see you there. This will be the final seminar in our series on teaching about AI and data science — the next series focuses on how to teach about applied AI across subjects and disciplines.
You can view the schedule of our upcoming seminars, and catch up on past seminars on our previous seminars page.
WKS2 teachers, participate in our new study!
We’re launching a new study to explore how to teach learners aged 9 to 11 about data-driven computing. The study will take place in collaboration with KS2 teachers (Y4/Y5/Y6) in England, Scotland and Wales and look at:
Our goal is to find practical ways to help teachers build children’s confidence in working with data in computing lessons. The study will be collaborative, with two workshops held throughout 2026, and we’re inviting KS2 teachers (Y4/Y5/Y6) to take part.
You can express your interest in participating by filling in this form: rpf.io/data-science-study-blog
The post How to put data first in K–12 AI education by using data case studies appeared first on Raspberry Pi Foundation.
For the last five years, once a month, we have hosted an online seminar sharing computing education research. Seminars are organised as usually year-long series with changing themes. In 2025, for example, our theme was ‘Teaching about AI and data science’. In 2024, it was ‘Teaching programming (with or without AI)’.
It is not surprising that for the last few years our focus has been on AI technology, and for 2026 we will continue this. But we will shift from showcasing how computing education research is changing teaching and learning in computing lessons, to showcasing how computing education research in other disciplines, such as art or geography, is starting to include teaching about AI. For example, art lessons may change so that learners find out how professional artists are using AI tools to create arts. Or geography lessons may change so that learners discover how professional geographers are using AI to make predictions about physical or human aspects of geography, such as volcanic activity and global warming.
Our series for 2026 is called ‘Applied AI’. This title recognises that AI technology is applied across contexts, across careers, across disciplines, and this means what we teach across school subjects will change.
The majority of resources and professional development material related to teaching about AI have been developed by the computer science community. For example, we have developed the popular Experience AI resources in collaboration with Google DeepMind. In these resources, the contexts were carefully selected to represent real-world examples across disciplines, and to to enable the teaching of particular technical or social and ethical concepts. This could be described as “a push” of content from computing towards other disciplines. For example, to enable teaching about the ethical issues around plagiarism, an art context is used in the Experience AI resources; to enable teaching about the potential benefits of using AI tools, an ecological geography context is used.
AI applications are always situated within a particular topic. Most current AI applications are data-driven: vast amounts of data are collected and processed to produce models that can then either be used to generate outputs or make predictions. For example, data about artworks can be collected and used to train a model for generating outputs similar to the artworks; this is an application of AI in the art discipline. Or data on wild fires can be collected and used to train a model for making predictions about current or prospective fires; this is an application of AI in the geography discipline.
In reality, the best people to recognise how AI technology is being applied in a discipline and what students in that discipline should be taught about these applications are the people working in the discipline, for example the art and geography teachers. Computer science educators can work to build the technical understanding and the general social and ethical understanding that is common across applications. But the detail of how AI technology is changing a discipline can only truly be understood by the respective community, by the artists and art educators, by the geographers and the geography educators.
At present, though, most educators are grappling with how they can use AI tools for productivity, such as creating lesson plans, or answering emails. Or they are looking at how they can use AI for general teaching and learning, for example for personalisation, say for students with additional needs. The idea that their underpinning discipline is changing is, perhaps, not yet on teachers’ radar. But at universities, such as in undergraduate courses, and in the world of work, education and training are changing. Data science courses are now being offered across faculties, including science, geography, language, and art faculties. These changes will start to filter down to school-based education via curriculum change. While some resources and professional development materials addressing this shift are already becoming available, change is still fragile and patchy.
The aims of our Applied AI research seminar series in 2026 are to start to:
If we can start to agree on what common concepts could be taught in the arts, sciences and humanities, it gives us a better chance to:
To make our 2026 series a success, we need to spread the word about our seminars to groups of educators, researchers, industry and policy makers across the arts, sciences, and humanities.
Please tell those you know in these groups about the seminar series, and share it through your social media and other networks. If you have ideas for subject associations we could connect with or publications where we can write about our series, please let us know.
We have already arranged the following seminars across 2026 and will add more speakers for the remaining monthly slots soon. Seminars always take place online on Tuesdays at 17:00 to 18:30 UK time.
To sign up and take part, click the button below. We’ll then send you information about joining. We hope to see you there.
You can view the schedule and details of our upcoming seminars on this page, and catch up on past seminars on our previous seminars page.
PS If you are teaching upper primary school learners in England, you can currently register your interest in our upcoming collaborative study on data science education. You’ll find out more about some of the research we’ve done in this area in this blog post.
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Research indicates that teaching learners to use and create with data-driven technologies such as AI and machine learning (ML) requires an entirely different approach for solving problems compared to traditional programming activities.
In this blog, we share the new data paradigms framework that we have developed through research and used to help improve our understanding about how to teach and learn about AI and data science. We also invite you to register your interest in participating in our next collaborative study on the topic.
Let’s start by highlighting an important distinction between different approaches to designing systems. In a knowledge-based approach to system design, a set of rules (e.g., if-then statements) are written for the system to execute. Every rule is explicitly defined. This approach is called ‘rule-based’, ‘symbolic’, or ‘logic-based’. For example, a developer could create a program that simulates dialogue by writing specific lines of code to handle a greeting, such as “IF user says “Hello” THEN output “Hi!”. If the user types “Greetings!” instead, the program fails because it has no rule for that specific word.
Knowledge-based models are often said to be explainable by design. This means the logic is accessible and interpretable and developers can trace the exact steps taken to produce an output. For example, if developers manually classify restaurant reviews as positive or negative using a pre-defined set of criteria, the rules their restaurant classifying system follows are entirely explicit, and the path from input to output is clear and explainable.
By contrast, in a data-driven approach to system design developers do not write specific rules. Instead, they collect lots of data and train a model. In the dialogue simulator example, they would collect hundreds of examples of greetings and train a model to the pattern of a greeting. If the user types “Greetings!”, the system generates a response based on the patterns in its training data.
Data-driven models are often opaque. In other words, the internal workings of these ML models are hidden. While we can see our input and the system’s output, the internal mathematical process is so complex — often involving layers of calculations and abstractions — that we cannot simply “explain” why a specific output was produced. For example, developers can create a classification model by training a neural network using thousands of images. Due to the large quantity of data used to train the model, and complex internal parameters and hidden layers, developers and users of the system cannot understand or explain the logic or features that lead to a specific output. These kinds of models are often referred to as a “black box” (as opposed to a “glass” or “clear” box).
Researchers have argued that the move from knowledge-based (or rule-based) programming to data-driven system design represents a paradigm shift and creates unique challenges for educators. The challenge is helping students shift from the expectation that a system produces a single ‘right’ answer — characteristic of traditional rule-based programming — toward an understanding that systems trained on large quantities of data produce outcomes that aren’t always fixed or explainable. If the current instruction in the classroom still relies heavily on traditional rule-based programming approaches, we might be setting students up for misconceptions.
In our research work on AI and data science at the Raspberry Pi Computing Education Research Centre, we analysed 84 research studies about the teaching and learning of data science. We categorised learning activities used in the studies to understand whether they were (i) knowledge-based or data-driven, and (ii) the extent to which the underlying models used were transparent or opaque. This led us to define four distinct data paradigms:
The data paradigms framework helps us to distinguish between different kinds of modeling activities students take part in and how instructional approaches could be classified across one or more paradigms. For instance, we found that most data-driven activities were also opaque (DD + O), usually meaning that students collected and used data to train a model, but how the system worked was opaque. This pattern, where the data is visible but the model is not explainable, risks students forming misconceptions about the capabilities and limitations of data-driven systems. Without understanding how outputs are generated, students may expect data-driven ML systems to operate like fully explainable (or transparent) ones.
We think that lessons are needed in the data-driven opaque (DD + O) quadrant to explicitly teach students about how data-driven systems work and the role they play in everyday contexts. However, when teaching data-driven opaque (DD + O) activities, learners’ attention needs to be directed to concepts such as model confidence, data quality, and model evaluation. Since an ML model is not inherently explainable, we need to teach students to use post-hoc explanation methods, such as testing different inputs to see how a system’s output changes. To prepare students for this learning experience, we think that first introducing activities about rule-based systems (knowledge-based + transparent; KB + T) or simple data exploration, such as linear regression or data visualisation (data-driven + transparent; DD + T) may serve as a ‘bridge’ to understanding data-driven modeling by helping students to distinguish between systems built from specific logical rules and systems trained on data.
We believe the idea of data paradigms can serve as a way of framing teaching activities about data science and help educators and students to consider the transition between different paradigms when engaging with the systems we interact with every day.
KS2 teachers, participate in our new study!
We’re launching a new study to explore how to teach learners aged 9 to 11 about data-driven computing. The study will take place in collaboration with KS2 teachers (Y4/Y5/Y6) in England, Scotland and Wales and look at:
Our goal is to find practical ways to help teachers build children’s confidence in working with data in computing lessons. The study will be collaborative, with two workshops held throughout 2026, and we’re inviting KS2 teachers (Y4/Y5/Y6) to take part.
You can express your interest in participating by filling in this form:
The post A research-led framework for teaching about models in AI and data science appeared first on Raspberry Pi Foundation.
In the age of AI, we believe kids still need to learn to code. We’re also testing how AI tools can be used to help with teaching and learning programming. This blog dives into our new classroom unit ‘Programming with AI’, which we’ve developed for teachers to introduce students to learning to code using tools built on large language models (LLMs). We share an overview of the unit, and the early excitement of educators and learners with whom we’re exploring this new approach to teaching and learning programming.
As India is accelerating its efforts to modernise digital and computing education, it’s the ideal place for us to test how best to introduce learners to AI-assisted approaches to programming and teach them responsible AI use.
We decided to design a ‘Programming with AI’ unit of eight lessons that support students aged 14–16 to progress from block-based programming to text-based Python programming, Python being one of the most widely employed programming languages in industry and academia. In the new unit, students are guided to use LLM-based tools safely, ethically, and responsibly in order to undertake data analysis projects in Python. Students learn how to use the tools, such as Gemini and ChatGPT, to deepen their understanding, experiment with ideas, and connect classroom concepts with real-world technological applications.
The ‘Programming with AI’ unit is currently being piloted across five residential schools under our partner, the Telangana Social Welfare Residential Educational Institutions Society. In the coming months, we will evaluate the impact of this AI-supported approach to learning programming on student engagement, motivation, and overall learning outcomes.
During the design phase of the unit, we identified three main challenges we wanted to address:
To solve the first problem, we decided that the initial lessons in the unit should start by showing familiar Scratch blocks right next to the equivalent Python syntax. This direct comparison makes the transition from block-based to text-based programmer smoother.
To teach prompting, we built a gradual path. The unit starts with a debugging code activity, where students get code with errors and a prompt to make an LLM tool fix the errors. Later, they learn to break down a prompt into their goal (what output they want) and the context (additional information for the LLM tool). Eventually, they use a structured framework to prompt the tool to generate complete, bug-free code.
Handling the unpredictable outputs of LLM tools we turned into a learning activity. In one lesson, students are given different chatbot-generated code segments and asked to analyse and reflect on the code quality. This crucial exercise helps them build the critical thinking and code evaluation skills they need for programming with AI tools.
Over the eight lessons, there is a progression of Python concepts and generative AI concepts, as shown in the image above. They lead students to complete two challenging, real-world data analysis projects.
To ensure the successful pilot of the ‘Programming with AI’ unit, a comprehensive teacher training program was essential. We understood that teachers needed confidence and competence not only in the subject matter — programming — but also in guiding students to ethically and effectively use LLM tools.
The training was an intensive, three-day programme: two days were held in-person for deep collaboration and one day was virtual for reinforcing key information. We covered five critical areas:
This training ensured our educators were not just teaching a new unit, but felt like confident facilitators ready to lead the shift towards AI-supported learning.
The pilot of the ‘Programming with AI’ unit began during the second week of December 2025 across five selected schools. It was immediately met with enthusiastic student engagement — they were highly excited to learn Python programming using LLM tools.
When teachers initiated discussions about AI, students demonstrated their familiarity with LLMs, chatbots, and related concepts. They were not only aware of generative AI but could readily name and describe the functions of LLM tools, telling us they were actively using these tools to create projects in various other subjects.
Students could easily connect with the activities in the lessons as they had foundational understanding of programming, which they developed while learning block-based coding in Scratch. This prior knowledge enabled their seamless transition to the new learning. Perhaps most impressive was the students’ rapid adoption of LLM tools to debug their code. This skill can ease what is traditionally a steep learning curve when students first learn text-based programming.
We are now eagerly anticipating their curiosity and progress in the upcoming lessons.
With the initial enthusiasm confirmed, the pilot program moves into its next critical phase. Teachers will continue to deliver the unit over the next few months in the five selected schools in Telangana.
During this extended observation period, our primary focus will be on rigorous data collection and impact assessment. We will closely monitor how students interact with the provided learning materials and how they use LLM tools throughout the unit. We will systematically assess whether the new teaching approach successfully helps students achieve the defined learning objectives for Python programming. Crucially, we will capture comprehensive data to understand the impact of this AI-integrated approach on engagement, skill acquisition, and problem-solving compared to traditional methods.
Based on the synthesis and analysis of all the gathered data, we will share our gathered insights early next year. The findings will inform our plans for more widely introducing the AI-integrated unit and for developing similar content for other settings and age groups.
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It’s been over a year since I last wrote an update on this blog about our research and as we’ve just published our 2025 Annual Report, this is an ideal opportunity to share what we’ve been working on at the Raspberry Pi Computing Education Research Centre.
We are a research centre based in the Department of Computer Science and Technology at the University of Cambridge, with a team that spans the university and the Raspberry Pi Foundation. We conduct research into many aspects of the teaching and learning of computing and AI and we work closely with schools, teachers and young people to ensure our research is applicable to practice.
Below I highlight some of the projects we’ve worked on in the academic year 2024-2025:
As I’ve written on this blog before, computing education is a global challenge. In one of the Research Centre’s projects, we are looking at how computing education is spreading around the world.
We found that between 2019 and 2024 the number of countries offering computing education had doubled, and that two thirds of all countries now offer, or have concrete plans to offer, computing education. This research has already been highlighted in the Stanford AI Index, and we are considering repeating the analysis in future years in order to have the most accurate and up-to-date information displayed in our map.
We have a number of projects in the area of AI education.
We are very interested in how to teach about AI, and held a workshop with teachers who were interested in the teaching of AI in February. Following on from the workshop results, we are interviewing more stakeholders, including UK-based experts, teachers and students, about their perspectives on concepts and skills that should be taught as part of an AI curriculum.
We’re also researching data science and data ethics education, which are foundational aspects of AI literacy. Most of the current AI systems are data-driven, having been trained on vast amounts of data. Therefore students need to understand about data and data science if they are to learn about AI systems. Therefore we’ve conducted two detailed literature reviews on data science and on data ethics this year. The first of these will be published in March at the WiPSCE conference.
One of our PhD students, Salomey Addo, has been examining how AI is taught in Ghana, where it is part of the curriculum for young people between ages 12 and 15. This year Salomey published a paper reporting that Ghanaian teachers have positive attitudes towards teaching AI but feel unprepared for it. She’s also developed unplugged resources to teach about artificial neural networks (ANNs).
ANNs are a fundamental technology used in a variety of AI systems, including image recognition and language translation systems. While ANNs are included in the Ghanaian AI curriculum, Salomey observed that teachers had difficulty with this particular topic The resources she developed are directly inspired by this, and involved teaching through role play and a board game.
This is an area we’ve also done some research in in the past year.
Carrie Anne Philbin published a paper in September showing that — at least in higher education — much of the use of generative AI in computing education is just duplicating the way teachers might already teach, and is primarily passive from a students’ perspective.
Meanwhile Katharine Childs and Veronica Cucuiat have been looking at how large language models (LLMs) can support secondary school programming education by helping students understand programming error messages.
Text-based programming is a topic featured in many computing curricula around the world. Teachers and researchers know that younger learners, for example at the lower secondary school level, can find debugging text-based programs very challenging. Although we’ve seen decades of research around programming and debugging focusing on learners who are in higher education, very little research has been done with school-age students.
In his research, Laurie Gale, a final-year PhD student at the Research Centre, found that learners were impatient to fix programs by trial and error, without figuring out what the real problemwas with their code or the underlying algorithm. He subsequently developed a tool called PRIMM Debug, which supports a more reflective and systematic approach to debugging. This tool enables learners to slow down when they are programming and to be more reflective. You can read more about it on the Research Centre website, and also catch up on the Foundation research seminar where he presented his work.
As part of a 5-year longitudinal project, running across the whole UK and the first project of its kind, we are looking at how physical computing impacts primary and secondary school learners. We’re investigating the effect of physical computing on learners’ creativity, agency and confidence, over time and at particular points known to be important for their subject choices. We are working with a wonderful set of partner primary schools who we visit each year.
This year we reported some of our first results, which point to teachers’ perceptions of physical computing being engaging and inclusive for primary-aged children. Starting next spring, we will be carrying out our third year of data collection with our pupil cohort, who have reached the age of 10 to 11.
We’ll also be running a survey next summer for upper primary-aged children and their teachers. Please sign up for our Teacher Research Network newsletter to be the first to hear about taking part in this survey.
As part of our TICE project we support teachers to conduct their own action research projects. This is a collaborative project, involving academics across the UK who volunteer to support teachers. The goal is to enable teachers to take a deep dive into a curriculum topic, a pedagogical approach, or a new resource, or to address a wider issue such as gender diversity or accessibility, to inform a change in their practice.
This year 16 teachers published their reports in our Teacher Research Booklet, and many also presented their findings online at CAS events or at the KCL-CAS London conference and the CAS National Conference. We’re very proud of them!
Two of our TICE teachers will be also presenting their research at an academic conference to be held in January. Congratulations to Will Grey and Joanne Hodge!
There are many other projects you can find out about on our website and in our annual report, so I hope that you will keep reading. It goes without saying that I’m incredibly proud of the team who’ve worked on all of these projects!
To summarise, here’s how you can stay up to date with our work and maybe even get involved in studies:
Finally, I am pleased to announce that we will be hosting the UKICER 2026 conference for researchers and teachers in Cambridge on 3 and 4 2026 September. More details will follow on the UKICER website and on the Research Centre website in due course.
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At a time when many young people are using AI for personal and learning purposes, schools are trying to figure out what to teach about AI and how (find out more in this summer 2025 data about young people’s usage of AI in the UK). One aspect of this is how technical we should get in explaining how AI works, particularly if we want to debunk naive views of the capabilities of the technology, such as that AI tools ‘think’. In this month’s research seminar, we found out how AI contexts can be added to current classroom maths to make maths more interesting and relevant while teaching the core concepts of AI.
At our computing education research seminar in July, a group of researchers from the CAMMP (Computational and Mathematical Modeling Program) research project shared their work:
They talked about how maths already taught in secondary schools can be used to demystify AI. At first glance, this seems difficult to do, as it is often assumed that school-aged learners will not be able to understand how these systems work. This is especially the case for artificial neural networks, which are usually seen as a black box technology — they may be relatively easy to use, but it’s not as easy to understand how they work. Despite this, the Austrian and German team have developed a clear way to explain some of the fundamental elements of AI using school-based maths.
Sarah Schönbrodt started by challenging us to consider that learning maths is an essential part in developing AI skills, as:
Sarah explained how the CAMMP team have developed a range of teaching and learning materials on AI (and beyond) with an overall goal to “allow students to solve authentic, real and relevant problems using mathematical modeling and computers”.
She reflected that much of school maths is set in contexts that are abstract, and may not be very interesting or relevant to students. Therefore, introducing AI-based contexts, which are having a huge impact on society and students’ lives, is both an opportunity to make maths more engaging and also a way to demystify AI.
Workshops designed and researched by the team include contexts such as privacy in social networks to learn about decision trees, personalised Netflix recommendations to learn about k-nearest neighbour, word predictions to learn about N-Grams, and predicting life expectancy to learn about regression and neural networks.
For the seminar, Sarah walked through the steps to learn about support vector machines. This is an upper secondary workshop for students aged 17 to 18 years old. The context of the lesson is an image problem — specifically, classifying the data representing the colours of a simplified traffic light system (two lights to start with) to work out if a traffic light is red or green.
She walked through each of the steps of the maths workshop:
Throughout the presentation, Sarah pointed out where the maths taught was linked to the Austrian and German mathematics curriculum.
As well as learning about lines, planes, distances, dot product and statistical measures, learners are also engaged in discussing the social and ethical issues of the approach taken. They are encouraged to think about bias, data diversity, privacy, and the impact of errors on people. For example, if the model wrongly predicts a light as green when it is red, then an autonomous car would run through a red traffic light. This would likely be a bigger consequence than stopping at a green traffic light that was mis-predicted as red. So should the best line reduce this kind of error?
To teach the workshops, Sarah explained they have developed interactive Jupyter notebooks, where no programming skills are needed. Students fill in the gaps of example code, explore simulations, and write their ideas for discussion for the whole class. No software needs to be installed, feedback is direct, and there are in-depth tasks and staggered hints.
Stephan went on to introduce artificial neural networks (ANNs), which are the basis of generative AI applications like chatbots and image generation systems. He focused on regression models, such as those used in weather forecasting.
ANNs are very complex. Therefore, to start to understand the fundamentals of this technology, he introduced a ‘toy ANN’ with one input, three nodes, and one output. A function is performed on the input data at each node. With the toy network, the team wants to tackle a major and common misconception: that students think that ANN systems learn, recognise, see, and understand, when really it’s all just maths.
The learning activity starts by looking at one node with one input and one output, and can be described as a mathematical function, with a concatenation of two functions (in this case a linear and activation function). Stephan shared an online simulator that visualises how the toy neural network can be explored as students change two parameters (in this case, weight and bias of the functions). Students then look at the overall network, and the way that the output from the three nodes is combined. Again, they can explore this in the simulator. Students compare simple data about weather prediction to the model, and discover they need more functions — more nodes to better fit the data. The activity helps students learn that ANN systems are just highly adjustable mathematical functions that, by adding nodes, can approximate relationships in a given data set. But the approximation only works in the bounds (intervals) in which data points are given, showing that ANNs do not ‘understand’ or ’know’ — it’s just maths.
Stephen finished by explaining the mutual benefits of AI education and maths education. He suggested maths will enable a deeper understanding of AI, and give students a way to realistically assess the opportunities and risks of AI tools and show them the role that humans have in designing AI systems. He also explained that classroom maths education can benefit from incorporating AI contexts. This approach highlights how maths underpins the design and understanding of everyday systems, supports more effective teaching, and promotes an interdisciplinary way of learning across subjects.
I have been researching the teaching of AI and machine learning for around five years now, since before ChatGPT and other similar tools burst on the scene. Since then, I have seen an increasing number of resources to teach about the social and ethical issues of the topic, and there are a bewildering number of learning activities and tools for students to train simple models. There are frameworks for the data lifecycle, and an emerging set of activities to follow to prepare data, compare model types, and deploy simple applications. However, I felt the need to understand and to teach about, at a very simple level, the basic building blocks of data-driven technologies. When I heard the CAMMP team present their work at the AIDEA conference in February 2025, I was entirely amazed and I asked them to present here at our research seminar series. This was a piece of the puzzle that I had been searching for — a way to explain the ‘bottom of the technical stack of fundamental concepts’. The team is taking very complex ideas and reducing them to such an extent that we can use secondary classroom maths to show that AI is not magic and AI systems do not think. It’s just maths. The maths is still hard, and teachers will still need the skills to carefully guide students step by step so they can build a useful mental model.
I think we can simplify these ideas further, and create unplugged activities, simulations, and ways for students to explore these basic building blocks of data representation, as well as classification and representing approximations of complex patterns and prediction. I can sense the beginnings of new ideas in computational thinking, though they’re still taking shape. We’re researching these further and will keep you updated.
If you would like to find out more about the CAMMP resources, you can watch the seminar recording, look at the CAMMP website or try out their online materials. For example, the team shared a link to the jupyter notebooks they use to teach the workshops they demonstrated (and others). You can use these with a username of ‘cammp_YOURPSEUDONYM’, where you can set ‘YOURPSEUDONYM’ to any letters, and you can choose any password. They also shared their toy ANN simulation.
The CAMMP team are not the only researchers who are investigating how to teach about AI in maths lessons. You can find a set of other research papers here.
In our current seminar series, we’re exploring teaching about AI and data science. Join us at our last seminar of the series on Tuesday, 27 January 2026 from 17:00 to 18:30 GMT to hear Salomey Afua Addo talk about using unplugged approaches to teach about neural networks.
To sign up and take part, 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 page.
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Over the first weekend in November, members of the global Code Club community came together for two inspiring days of learning, creativity and connection. The annual event celebrates the people who make Code Clubs happen, allowing them to share ideas, explore new tools, and connect with others who help young people learn to code.
Saturday began with hands-on sessions that brought creativity and technology together, exploring large language models and prompt engineering in Collaborating with LLMs and being a prompt boss. There was a lot of laughter from attendees about how large language models can produce confident but incorrect answers if given vague prompts, but many left inspired to experiment with new technologies in their own clubs.
“First time there and it was amazing. Met loads of great people and the amazing code club crew. I learnt loads of new skills around AI and Arduino.” – An attendee
Explore AI with creators in your club using our AI and machine learning projects.
Collaboration that counts brought mentors together to discuss common challenges like volunteer retention, limited resources, and communication barriers. A crowd favourite was a shared volunteer toolkit, as well as event checklists and safeguarding resources.
“What I enjoyed most about the Clubs Conference was the opportunity to meet other facilitators and hear their stories — their successes and challenges. These conversations validated the volunteer work I do and reminded me of the impact of our clubs.” – An attendee
From the theatre sessions, you can watch Inclusive learning – Supporting Deaf learners in clubs which was both moving and insightful. We learnt that visual demonstrations, colour cues, and repetition were key to supporting Deaf learners. One memorable quote captured the spirit of the session:
“The children couldn’t speak to us. The children — we couldn’t hear their voices but by the eighth week we were able to hear their voices from what they built on the screen and it was echoing all around the classroom.” – Chidi Duru
Find out more about Chidi’s joy of coding alongside Deaf creators.
The weekend’s talks showcased the reach of Code Club worldwide, with volunteers sharing their experiences of collaboration, sustainability, and creativity.
Watch Lessons from resourceful Code Clubs in India, which highlighted the ingenuity of young learners in under-resourced settings, while Hands-on with the Raspberry Pi Pico showcased low-cost, high-impact projects from Kenya and South Africa.
Speakers showed how community clubs adapt to local needs with unplugged activities and coding games inspired by cricket and kabaddi, empowering young people to solve real problems and celebrate curiosity through play. Excitingly, these new resources will be launching early next year; keep an eye on our activities page to be among the first to try them out!
In the session Code Club Projects Unplugged, facilitators shared the idea of “hiding the vegetables” — hiding the learning inside the fun. Whether through a collaborative Scratch game, a micro:bit prop on stage, or a Pico gadget solving a real problem, this approach helps young people learn through play. They remember the joy, and the skills come naturally.
Teaching tech away from the computer screen shared a fun unplugged cybersecurity activity, The Chicken Shop, where learners role-play social engineering scenarios. Its success came from clear printed instructions, movement, humour, and strong debriefing.
Learning coding outside the box explored how to engage young people with diverse learning styles while the Arduino crash course gave attendees a taste of physical computing and C++ programming in action. Workshops on AI, sustainability, and youth empowerment with Raspberry Pi computers and Unlocking Code Club resources helped club leaders discover practical ways to inspire problem-solving and make use of all the support available through Code Club.
The message from the sessions was clear: young people learn best when technology is human and hands-on.
Coolest Projects – get involved! championed creativity over competition. Any young person under 18 can submit their project, including unfinished ideas. In-person and online showcases celebrate progress, imagination, and teamwork.
Speaking on the closing panel, Code Club leader Rachael Coultart talked about the importance of Coolest Projects as a rare platform for children to talk about their learning. She spoke about the experience of one particular child, explaining that it had made a powerful impression on her, saying:
“It had such a huge impact. I felt so proud of her and what she’d achieved. Afterwards, her parents told me that they felt it was the first time she had really been seen.”
The community is united in its commitment to making Code Clubs inclusive, creative, and sustainable.
“Thank you. Clubs Conference is a highlight of my year.” – An attendee
If you want to stay up to date with the latest news, events and opportunities from Code Club, sign up for our newsletter and be part of the growing global community.
The post Code Club Conference 2025: Creativity, community, and collaboration in Cambridge appeared first on Raspberry Pi Foundation.
Social media can have a powerful impact on the way we see and experience the world. What we see in our feeds is not random: it is determined by AI-driven systems that collect vast amounts of data, build user profiles, analyse engagement, and generate recommendations. But while young people are prolific users of social media, studies show that many have little understanding of what is happening ‘under the hood’
In our September research seminar, we welcomed back Henriikka Vartiainen and Matti Tedre from the University of Eastern Finland. They introduced Somekone, a social media simulator that is designed to help learners understand some of the fundamental processes behind social media platforms. Their team has been developing AI education materials and tools since 2019, including GenAI Teachable Machine, which they presented at our May research seminar.
Henriikka explained that the development of the Somekone tool emerged from the team’s long-term collaboration with teachers and schools in Finland. They co-developed the tool with the aim of making concepts like data collection, engagement, profiling, recommendations, filter bubbles, and polarisation visible and explainable for students aged 11 to 13 years old.
Henriikka described the pedagogical model that the team follows in all of their AI education interventions. Their goal is not only to support students to develop their understanding of AI concepts, but also to foster ethical awareness and a sense of agency.
Matti then gave a live demonstration of Somekone. Nothing compares to seeing the tool in action, so do check out the video of his demo here!
Students log on to the tool and are presented with an Instagram-style feed of images. They scroll through the feed and like, share, or comment on images that catch their attention or match their interests. For many students this is a very familiar type of environment, and they really enjoy playing with the app!
However, the unique value of Somekone is that it provides students with a real-time view of the way data is collected from every single user interaction, and demonstrates what is done with that data. It also allows students to experiment with a social media tool in the classroom without any data protection issues, as all of the data is stored locally.
Learners explore:
The simulator creates clusters of users with similar interests, which update in real time as students interact with posts on their feeds
The tool provides an explanation for why each post is recommended
Two users’ heatmaps presented side by side, showing their respective filter bubbles
For teachers, the tool has a simple guided interface to make it easy to use in class. There is also a button that teachers can use to pause the app, stopping students from scrolling (much to their dismay!) in order to focus their attention on the teacher when they are explaining concepts.
The research team used pre- and post-tests to evaluate what impact the intervention had on students’ understanding of social media mechanisms and on their sense of agency in relation to data. They conducted the post-test a week after the intervention, and then also did a delayed post-test six months later to see whether any changes were sustained. They found:
The Somekone tool is freely available online — in Finnish, English, German, and French — at somekone.gen-ai.fi. The developer Nick Pope has also made the source code available on GitHub at github.com/knicos/genai-somekone.
However, the supporting materials and teacher resources are currently only available in Finnish and the underpinning pedagogies relate to the Finnish context.
Join us at our next seminar on Tuesday, 11 November from 17:00 to 18:30 GMT to hear Karl-Emil Bilstrup (Copenhagen University) speak about using the micro:bit to explore machine learning practices. We hope to see you there!
To sign up and take part in our research seminars, click below:
You can also view the schedule of our upcoming seminars, and catch up on past seminars on our previous seminars page.
The post How AI shapes your feed: An explainable social media simulator for the classroom appeared first on Raspberry Pi Foundation.
We’re happy to celebrate National Engineering Day in the UK with our friends at the Royal Academy of Engineering. Today they launch the AI-Z of Engineering, an online collection showcasing more than 100 current engineering jobs, and over 100 more engineering jobs envisaged for the future. Their aim is to inspire more young people to pursue engineering careers in areas ranging from artificial intelligence (AI) and robotics to medicine, software engineering, space, and sustainability.
Engineers shape the technologies and industries that define the future — and the systems they build affect jobs and tasks in every sector. As Sir John Lazar, President of the Royal Academy of Engineering and our Chair of Trustees, says about AI technology:
“I’m not a believer that all jobs will just disappear because of AI. If you think of a job as an assemblage of tasks, there’s no question that the tasks in your job will change because AI will work with you on a bunch of things, and this will in turn reshape your job or role. The people who will thrive through this transformation will be those who engage with curiosity, intellectual rigour, scepticism, creativity, problem-solving and teamwork – and these are the skills and attitudes that are taught by engineering and computational thinking.”
Sir John Lazar, President of the Royal Academy of Engineering and Chair of Trustees, Raspberry Pi Foundation.
At the Raspberry Pi Foundation, we believe AI literacy is crucial for all young people. We also believe all young people need to learn to code to be able to shape our future, where AI systems are integrated into all aspects of life. Our position paper “Why kids still need to learn to code in the age of AI” presents five reasons why:
Our free resources for young people help them learn to code and get creative with technology to bring their ideas into the world, building their confidence. So whether your kids are just starting their coding journey, or are looking for a new challenge, you can use our resources to support them.
Understanding coding and computers is critical in many engineering roles, so inspiring kids about engineering can also motivate them to try their hand at coding.
There are plenty of ways you can celebrate National Engineering Day. Inspire young people to embark on careers in engineering by sharing the AI-Z of Engineering collection with your school or college, on social media, or on your organisation’s website. The collection is a living resource that will be updated, and contributions are always welcome — both for current jobs and future ones.To find out more ways to get involved with National Engineering Day, you can go download the toolkit.
The post Celebrating the UK’s National Engineering Day 2025 appeared first on Raspberry Pi Foundation.
Current artificial intelligence (AI) methods, especially machine learning (ML), rely heavily on data. To complement our work on AI literacy, we have been investigating what data science teaching resources and education research are currently available. Our goal is to work out what data science concepts should be taught in a data science curriculum for schools.
Read on to find out what resources and materials we have reviewed, and what concept themes we have identified.
Data science is an interdisciplinary science of learning from large datasets, aided by modern computational tools and methods (Ow‑Yeong et al., 2023). We see data science skills as fundamental for using, creating, and thinking critically about:
To navigate a world where decision making in many areas is influenced by data-driven insights and predictions, young people need to be taught about data science. Data science skills empower young people to become critical thinkers, discerning consumers, adaptable professionals, and informed citizens.
Worldwide, countries are taking a variety of approaches to introducing data science into their education systems, as highlighted in a 2024 report from the coalition Data Science 4 Everyone.
In some countries, such as India and Israel, data science education is an established school subject. It is taught as part of the curriculum in at least one of the primary, secondary, or post-16 age phases. Meanwhile in other countries, for example Canada, Germany, and Poland, data science is a very new school subject, or there are still only recommendations to develop it into a school subject.
While we are currently considering what a comprehensive data science curriculum should include, we already offer several resources to support you with your teaching about data science and data-driven technologies. You can find a list of these resources at the end of this blog. Now, however, I’ll give you an overview of our recent work to identify concepts for a data science curriculum that fits with our approach to AI literacy.
To answer the question ‘What should we teach about data science to learners aged 5 to 19?’, we undertook a grey literature review of data science teaching materials. A grey literature review is structured like an academic literature review and conducted with the same rigour. The difference is that a grey literature review also considers publications that have not been peer-reviewed, including reports, white papers, curriculum materials, and similar resources.
To orient our work, we combined four frameworks for data science and AI/ML education:
With these combined frameworks as our map, we reviewed 79 data science learning resources. The resources varied:
From among the 79 resources, we chose 9 that included clear learning outcomes, and that together covered a wide field of concepts. We examined these 9 in detail to extract 181 explicit and implicit data science concepts. Next, we grouped the concepts into themes, and finally we refined these themes by comparing them against the four frameworks listed above.
The themes we have identified for a data science curriculum are:
This set of themes both fits with the frameworks by Olari and Romeike and Data Science 4 Everyone, and expands them by covering ML principles and programming approaches and calling out data bias and ethics.
Through our grey literature review on data science education, we’ve:
Our next step is to shape these candidate concepts into a progression framework to describe their relationships and establish which concepts could be taught at each age or phase of schooling.
The literature review also gave us an overview of the pedagogical approaches and tools used for teaching data science concepts. These findings will become useful once we start designing learning activities.
You’ll hear more about how this work is going here on our blog and on our social channels. In the meantime, comment below to let us know what you think about the themes, or to tell us what you’d like to see in a data science curriculum for the learners you work with.
You can read about our thinking behind the data science-related teaching resources we’ve created so far in our ‘Data and information within the computing curriculum’ report from 2019.
You might also be interested in exploring the Experience AI programme, which offers everything teachers need to help students develop a foundational understanding of data-driven AI technologies, their social and ethical implications, and the role that AI can play in their lives.
Our free online course ‘Teach teens computing: Machine learning and AI‘ helps teachers understand and explain the types of problems that ML can help to solve, discuss how AI is changing the world, and think about the ethics of collecting data to train a ML model.
Teaching young people to understand data-driven AI technologies means teaching them thinking skills that are different to those needed to understand rule-based computer systems. You can read about these Computational Thinking 2.0 skills in our Quick Read PDF.
Our current research seminar series focuses on teaching about AI and data science. Sign up for an upcoming seminar session (the next one is on 11 November) or catch up on past sessions to find out what the latest research findings are in this area. You can also revisit our 2021/22 series on the same topic to see how work in this area has developed. The Raspberry Pi Computing Education Research Centre also has ongoing projects in the area of AI education for you to explore.
The post What should be included in a data science curriculum for schools? appeared first on Raspberry Pi Foundation.
How do you teach AI in a way that resonates with 11- to 14-year-olds long after the lesson ends? In two recent Experience AI webinars, we explored that question with collaborators from Google Research, Google DeepMind, and the Stanford Accelerator for Learning. During the webinars, we also showcased AI Quests, a gamified, classroom-first experience where learners use AI concepts to solve real problems.
“The AI technology you’ll experience is amazing, but it’s not magic. Success depends on the decisions you make.”
That line, delivered by Professor Sky, the in-game mentor, captures the core message of AI Quests: AI systems are built by people and shaped by human judgment at every step.
We’ve embedded AI Quests into the Foundations of AI unit in Experience AI, our free AI literacy programme created with Google DeepMind.
As Google Research’s Liat Ben Rafael explained, “AI Quests is a gamified experience… where students discover firsthand how AI is used in the real world to create positive impact.” Each quest is grounded in a real research programme and mirrors the AI project lifecycle you’ll recognise from our Experience AI lessons: define the problem, prepare data, train, test, deploy.
The first quest, Market Marshes, asks students to help Luna, one of the central characters, to protect a riverside market from flooding. Players roam, gather candidate data (from rainfall stats to town gossip), clean it, choose relevant features, and train a model. If the model underperforms, they iterate, exactly as real AI developers would.
Emma Staves, Learning Manager at the Foundation, notes that a key moment is when learners test their model: “It’s made really clear that the data that’s being used to test the model is historic data.” That simple design choice can help you to unlock rich discussions with your learners about validation, reliability, and what counts as “accurate enough” for real decisions.
Developed in collaboration with learning scientists at the Stanford Accelerator for Learning, the quests reflect what Victor Lee, Faculty Lead for AI and Education at Stanford, describes as “enduring understanding”:
“The enduring understanding is about how humans can initiate and design AI applications that can address some of humanity’s biggest unsolved challenges.”
To keep that focus, the team blends:
In other words, the quests are all about playing with purpose.
We piloted AI Quests with some teachers, including Dave Cross, Curriculum Leader for Computer Science at North Liverpool Academy, who tested the quests with his Year 7 students, before extending it to his GCSE classes:
“We see it moving forward as a really solid foundation… for that further learning.”
He also saw strong cross-curricular ties: geography colleagues spotted “massive opportunities” to use the flood quest in their own units, while broader staff discussions turned to digital citizenship, data literacy, and fairness. The cross-disciplinary nature of AI is increasingly apparent, and so AI literacy shouldn’t be limited to computing — students need to encounter AI across multiple subjects and in everyday life.
The second webinar connected the classroom experience to the real project it’s modelled on: Google Research Flood Forecasting. Gila Loike, Product Manager, set the scene:
“Our research team develops AI models that predict flooding all over the world, five to seven days before the flood occurs.”
Deborah Cohen, the Research Scientist leading the team at Google Research focused on flooding, also explained that traditional models can’t easily predict floods in places with little data. However, AI can fill those gaps by combining information from rivers, weather forecasts, and satellites, to give accurate warnings around the world:
“With AI we were able to expand our coverage to the entire world.”
The results are real and practical. Accurate predictions help:
To make sure their models work well, the team compares predictions with real river data, where available, and with satellite images showing flooded areas. Students explore these same ideas in the AI Quest game, cleaning messy data, testing their models, and checking how accurate their results are.
“Students are really engaged by the real-world challenge,” said Emma Staves about the Market Marshes quest. “That authenticity makes learning come alive.” It helps students see how classroom ideas, like features, accuracy, bias, and model cards, connect directly to real decisions and their consequences.
Liat also gave a sneak peek at the next quest, a health-focused story on blindness prevention. It introduces new layers — privacy, diverse data, field testing — while following the same lifecycle. More quests are in development, with additional languages planned from early 2026.
The key message from both webinars is clear: AI literacy isn’t just about using technology — it’s about understanding our role in shaping it. As one Stanford researcher put it, “AI isn’t this magic thing that just happens to us. Humans decide how to use it, and how choices around data affect accuracy and fairness.”
Our goal with Experience AI is to help young people become thoughtful, creative problem-solvers who can navigate an AI-powered world with confidence and integrity — and AI Quests fits perfectly with that.
You can watch both webinars anytime on our YouTube and LinkedIn channels
Webinar 1: LinkedIn, YouTube
Webinar 2: LinkedIn, YouTube
Explore our Experience AI resources — already used by nearly two million learners and educators to understand, question, and create with AI — to bring them and AI Quests into your classroom. You’ll find the Foundations of AI unit, alongside materials on large language models, ecosystems and AI, and AI safety, at rpf.io/experienceai-resources
The post Play, pedagogy, and real-world impact: What we learned from the AI Quests webinars appeared first on Raspberry Pi Foundation.
I am very proud to share the news that Experience AI has been recognised as a laureate for the 2025 UNESCO King Hamad Bin Isa Al-Khalifa Prize for the Use of ICT in Education.
Experience AI is a free educational programme that helps teachers and students learn about artificial intelligence (AI). It was developed by the Raspberry Pi Foundation in partnership with Google DeepMind and includes lessons, classroom resources, and hands-on activities to help students develop a foundational understanding of AI technologies, their social and ethical implications, and the role that AI can play in their lives.
It is based on original research into AI literacy and highlights real-world applications of AI technologies, including through videos featuring research scientists that help to bring the lessons to life for students.
Since we launched the first Experience AI resources in April 2023, they have been used to teach over 2 million students, and that number is growing fast.
This reach is possible thanks to a global network of Experience AI education partners who work with us to localise and translate the resources and deliver large-scale teacher training in their regions.
Experience AI was one of four laureates of the prestigious global Prize selected by the Director-General of UNESCO, based on recommendations from an independent, international jury. The jury commended the programme for its strong ethical foundation and wide international reach.
This year, marking its 20th anniversary, the Prize focused on the theme ‘Preparing learners and teachers for the ethical and responsible use of artificial intelligence’.
The Prize was awarded at a ceremony at the University of Bahrain attended by the Director-General of UNESCO, Ministers of Education of the Gulf Cooperation Council, and members of the Raspberry Pi Foundation and Google DeepMind teams.
I want to say a heartfelt congratulations and thank you to everyone who has worked on Experience AI so far. It has been a fantastic, collaborative effort from colleagues across the Foundation, Google DeepMind, and all of our partner organisations.
I also want to pay tribute to all of the teachers — all over the world — who have engaged so enthusiastically with Experience AI, for helping us develop the materials, including testing them in your classrooms and providing such thoughtful feedback, and for everything you do, every day, to inspire your students. This recognition is for all of your hard work, diligence, and care. Congratulations and thank you.
Experience AI is provided at no cost to schools, teachers, or students thanks to generous funding from Google.org. We are also very grateful to Broadcom Foundation, which has provided additional funding to support the programme.
We are exceptionally proud to have received this recognition for Experience AI, but we aren’t complacent. Equipping all young people, and their teachers, with a foundational understanding of AI technologies is one of the most urgent challenges facing all education systems.
We have made a great start, and we know there is much more to be done. That’s why we have lots of important updates and developments coming soon, including:
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