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Computing education in England is in a stronger position than it was a decade ago. Increased access to technology, free learning resources for young people, and improved training for computing teachers has put us on a path to success.
But the subject is also at an important inflection point. Young people are growing up in a world shaped by digital technologies like AI, yet not all young people get the same opportunities to discover how computing can be creative, collaborative, and relevant to their lives. This is reflected in the number of learners who choose to study it. In England, GCSE Computer Science entries fell by 4.7% in summer 2025 (93,980 to 89,610), and A level entries fell by 2.4% (19,475 to 19,010)*.
Creating positive, accessible experiences early in a students’ education, and helping them to build confidence and see computing as something they can engage with, has never been more important.
For educators, computing is about much more than preparing learners for a single subject or qualification. Skills England’s 2025 assessment of priority skills up to 2030 estimates that demand for priority occupations will grow by 0.9 million, including 87,000 additional programmers and software development professionals**. These roles are identified as a priority across seven industry sectors, underlining the value of the broader skills students develop through computing: problem-solving, logical reasoning, creativity, and confidence.
That’s why initiatives such as the UK Bebras Challenge and the Raspberry Pi Foundation Coding Challenge matter. They give teachers ready-made, engaging ways to introduce computational thinking and coding without needing students to already see themselves as ‘computer scientists’. Low-barrier, fun, and practical, they help students experience the satisfaction of solving problems for themselves. As Mitchel Resnick, Professor of Learning Research at the MIT Media Lab, puts it: “They are not just learning to code, they are coding to learn.”***
Over the past year, the UK Bebras Challenge (November 2025), and the Raspberry Pi Foundation Coding Challenge (March 2026), have reached hundreds of thousands of students across the UK.
Participation continues to grow. In 2025, 562,924 students took part in Bebras, up from 467,000 in 2024, contributing over 1 million hours of learning. The Coding Challenge also grew, with 85,389 students taking part in 2026, up from 83,096 in 2025.
To understand what this looks like at the grassroots level, we spoke to two teachers about why they run these challenges and what their students gain from taking part.
I was introduced to the Bebras Challenge during my teacher training, where my placement school was already taking part in the competition. Not only was it an affordable way to introduce students to the world of computational thinking, but I quickly realised how useful it was in identifying which students would be a good fit for GCSE Computer Science. Since my initial positive experience, I’ve ensured that all computer science students take part in the challenge, no matter which school I’ve worked in.
From an admin perspective, it is quick and easy to create accounts for a large number of students in one go. Once student data is downloaded from your MIS, importing the spreadsheets is incredibly simple. As a busy teacher, this saves me a lot of time.
For students, the accessibility of Bebras is a big selling point. Due to the on-screen reader, ability to add extra time for SEND students and differentiated challenges, everyone can access the competition. The interactive elements within the questions also make it engaging and enjoyable to work with.
Students can be easily influenced by their teachers. When introducing the Bebras Challenge every year, it’s important to clarify its purpose and the reason behind taking part. With a genuine motive explained, I find students are receptive to attempting something new. Every individual challenge is unique and colourful, operating a simple UI to jump between questions. Students are spurred on to get stuck into the puzzles on their first encounter, responding to the tasks with confidence.
The number one skill that students develop is their ability to problem-solve. When faced with tasks of varying difficulties, they are able to apply the key principles of abstraction and decomposition to tackle problems. If I’m with a GCSE or A Level class, it presents as the perfect opportunity to reinforce their theory knowledge, linking computational thinking concepts to the question in front of them.
Additionally, it can promote the hard skill of literacy. By having separate competitions for different year groups, students are reading texts that are of an appropriate level for their age bracket. And if you’re in a primary school, there’s the option to work in groups, allowing students to develop another soft skill of teamwork.
I’ve previously taught a Year 9 student with a reading age of 10, who was finding all aspects of school a challenge. Utilising the adaptive technology available to them in the Bebras Challenge, they were one of few in their class to achieve a Merit. This student was able to showcase their strong logical reasoning skills, without being hindered by their reading comprehension.
Consequently, they chose Computer Science as one of their GCSE options and wanted to pursue a career as a software developer. It also raised an interesting discussion point with the school, investigating various access arrangements to better support them. The Bebras Challenge has quite literally changed this student’s life.
Just go for it! Given how renowned the competition is, my sixth form students have mentioned their results in university and apprenticeship applications. If students are needing evidence to back up their problem-solving abilities, or wanting to differentiate themselves from others with similar grades, the UK Bebras Challenge gives them an extra string to their bow.
As a teacher, just make sure you’re organised and prepare the accounts well in advance. Once the student accounts have all been created, using the helpful Coordinator Handbook as guidance, you can export their details and create login slips. Coupled with Bebras’ instructions presentation for in-class, it made for a smooth set-up and delivery. As the competition is straight after October half-term, it also allows teachers to ease their way back into school life. We always look forward to Bebras week at my school!
We are always looking for more competitions for students to participate in. We run a few competitions, but most are for the elite only. For example, we run the British Informatics Olympiad and British Algorithmic Olympiad but only about 20 of our 340 students can attempt either.
So when we heard about the Coding Challenge, it was exactly what we were looking for, a competition where everyone could take part and achieve good results. It’s always good to practise more coding skills, so this competition is ideal for us. This year was the first time we had the whole cohort take part, both Year 12 and Year 13.
The vast majority of students are excited about the Coding Challenge. They love competition!
The very first time they see them, it can be a bit overwhelming. There is a lot to do in a short time, which is why we get students to practise before the actual competition.
It has always been about problem solving. This is a skill that is still very much in demand, perhaps more so now. We are still teaching computational thinking when teaching them to code. There are many transferable skills from learning to code that are useful in most careers and industries. The need for resilience and attention to detail are vital skills for all.
It develops resilience and many other skills such as following instructions precisely, which are important in life. Also time management and planning, students need to decide what to try and in what order if they want to maximise their score.
Quite a few students will continue to work on the problems they encountered after the challenge has finished. They are determined to solve the problems! I hear many groups discussing techniques they used to solve the trickier problems which gives them the confidence to use those techniques in their classwork.
Do it! It is so easy to do. We run it in a normal lesson; it is in our schedule of lessons so all teachers do it the same week. From two weeks before the day we choose to set the challenge, we set homework to be practice questions.
The tricky part is making sure the students understand the interface, what code to copy in and how to test and run it, so the homework gives them a chance to do this. One teacher sets up the spreadsheet with all the student names to import to the very easy-to-use teacher admin website. It is then easy to download each class’s usernames and passwords.
Across both challenges, one thing is clear: when barriers are lowered, more students take part and discover that computing is for them. Whether through short problem-solving tasks or coding challenges, these experiences build confidence, develop transferable skills, and help learners see themselves in computing.
For teachers, they also offer something equally important: practical, structured activities that can fit into the school year and work with whole classes or cohorts. In a landscape where the UK needs more young people to develop digital and computational skills, accessible challenges like these can help spark interest early, celebrate success, and give every learner a meaningful first step into computing.
To register your school for the next UK Bebras Challenge coming in November 2026, go to bebras.uk.
References
***https://web.media.mit.edu/~mres/papers/L2CC2L-handout.pdf
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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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Walk into Allison Knoph’s fifth grade classroom in Edina, Minnesota in October and the creative writing station will be the loudest place in the room.
That is by design. “I use it at my creative writing station,” she says of Experience CS, which she has run each fall for the past couple of years. Kids use the station computers to be creative as they work on their programming projects, building characters and testing jokes they have written. Some laugh hard enough that a visitor might wonder if anyone is working.
“Giggling,” Allison says, “is a good problem to have.”
Experience CS is a standards-aligned computer science curriculum for grades 3 to 8 (ages 8 to 14) that integrates computing into core subjects like math, science, and art. Allison has completed two units with her students, The me project, designed for ages 9 to 10, and Ecosystems, designed for ages 12 to 13.
Of the two units, the storywriting-themed The me project was the stronger fit. It mapped cleanly onto language arts work her students were already doing. Students hit the signposts the unit lays out, but what they did inside that structure was theirs. One wrote the minimum and moved on. Another built a multi-scene story with custom sound effects, spending three weeks developing their project and making improvements.
The Ecosystems unit was more challenging for her students, and the students who finished “were the ones who were kind of the more gung-ho ones that were really into it.” She is not dismissing it, but she is clear that placement matters.
Allison highlighted that some of the students who were thriving in the Experience CS lessons were students who were experiencing difficulties in other areas of the curriculum. “The kids who don’t normally shine often do here,” she says. Describing one experience from her classroom, she shared that a student who had difficulties with reading quickly understood how a program should be sequenced. Within a week, classmates were walking to that desk for help. The peer support model is not something she engineers. It builds itself.
Running the lessons in October sets a tone she draws on all year. Something breaks, a student gets stuck, and the answer in her room becomes “We can figure it out.” That carries into math, into writing, into everything after.
Allison is not arguing that every student will grow up to write code. Her argument is that computational thinking transfers, and that it looks a lot like the writing instruction she is already doing: syntax, structure, sequence. Debugging a story and debugging a program are closer cousins than most people think.
In the age of AI, computational thinking and coding skills are especially important. Access to AI tools makes it easier for more people to generate code. Fewer people can read it, judge whether it is any good, and change it. Allison saw this on her son’s robotics team: the students who understood their code could adapt it when the robot did something unexpected. The ones who had generated code and dropped it in could not. Same tools, different outcomes, and the difference was comprehension.
Minnesota ranks near the bottom nationally for computer science access. In the most recent state-by-state accounting, 34 percent of its public high schools offered a foundational CS course, against a national average of 60 percent.
As a fifth grade teacher, Allison believes that if students may not have opportunities to learn CS in high school, the introduction has to happen earlier, in a classroom not labeled computer science.
She is candid that the barrier for elementary teachers is not interest. It is time, cost, and the fear of being asked a question they cannot answer. She sums up the Experience CS offering to her colleagues in a few words: free, little direct instruction, fits standards she is already teaching, and the kids like it enough to be loud about it.
“We can figure it out” is a good thing for a teacher to say out loud. It is a better thing for a room of ten-year-olds to start saying back.
Find out more about introducing Experience CS in your classroom: head to experience-cs.org today.
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Are you a computing teacher in England, Scotland, or Wales who works with 11- to 14-year-olds and is interested in how young people learn about AI ethics and sustainability?
The Raspberry Pi Computing Education Research Centre is launching an exciting new research study, and we would love you to get involved.
In this study, we will explore teaching about the impacts of AI on people, the environment, and society. Our aims for the study are to:
The study involves attending two in-person workshops in Cambridge, co-designing and teaching a unit of work, and taking part in evaluation activities such as surveys and interviews. Where necessary, we can offer support with the costs of attending the workshops, such as travel, accommodation, and supply cover.
According to a recent report, in the UK more than half of 8- to 17-year-olds use AI tools, and as these systems become an increasingly significant part of everyday life, helping young people to critically evaluate their impact is vital.
We recently conducted a scoping literature review to explore what ethical concepts are covered by AI literacy interventions for lower secondary students (11- to 14-year-olds). This work has been accepted for publication at the Frontiers in Education conference, which is taking place in October.
In this work, we used the 10 principles set out in UNESCO’s Recommendation on the Ethics of Artificial Intelligence as a framework for analysis of the ethical concepts covered by the interventions.
Our research showed that when AI ethics is taught to lower secondary students, interventions tend to focus most on concepts such as fairness and privacy. For example, multiple activities explored the important issue of algorithmic bias, teaching students about the causes of bias (such as training AI systems with small or unrepresentative datasets) and discussing real-world examples of bias in AI.
However, we found that other ethical concepts, such as proportionality and governance, are not often taught. And while a few interventions featured the theme of sustainability, we found that only one directly taught students about the environmental impact of AI itself.
Our new study aims to help address this gap by supporting young people to explore the social and ethical impact of AI tools from a sustainability perspective. Teachers will use real-world examples to engage their students in discussion and ethical reasoning. We hope the findings will support more computing teachers to teach about AI ethics in their lessons, and help students to think critically about the use of AI tools.
We are using a design-based research approach to work collaboratively with teachers to co-design a unit of lessons focused on AI ethics and sustainability.
Participating in the study will involve:
If you teach computing at lower secondary level (Years 7–9 or S1–S3) in England, Scotland, or Wales and would like to help shape the way we teach young people about ethical issues around AI, we would love to hear from you. Please register your interest via the form below.
http://rpf.io/sage-application
If you have any questions about the project, please email katharine.childs@raspberrypi.org.
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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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The UK Bebras Challenge, the nation’s largest computing challenge, is back!
Last year, more than 526,000 students from across the UK took part, tackling fun and thought-provoking puzzles that introduce key ideas in computational thinking with no extra preparation needed.
Read on to learn how your school can get involved.
The UK Bebras Challenge is a free-to-enter annual challenge that is designed to spark interest in both computational thinking and computer science among students aged 6 to 19. The 45-minute challenge is accessible to everyone, offering age-appropriate but challenging interactive tasks for students at different levels, including a tailored version for secondary students with severe sight impairments.
The tasks are designed to give every student the opportunity to showcase their potential and all participating students receive a certificate. There are also certificates based on performance within school and gold certificates based on national boundaries. With self-marking tasks and no text-based programming required, it’s easy to have your school participate in the UK Bebras Challenge.
“The UK Bebras challenge provides an engaging way for students to apply their problem-solving skills in a fun, competitive environment. It complements our coding curriculum, encouraging critical thinking and enhancing computational thinking,” – Jonathan Phillips from Hereford Cathedral School
Each Bebras task’s background section is now linked to a related topic on the Ada Computer Science platform. Following the live challenge, teachers and students can explore ithe detailed explanation of the computer science concept behind each Bebras task, along with the computational thinking skills students may use to solve that task.
“We have thoroughly enjoyed delivering the UK Bebras Challenge at our school. It has been an invaluable resource and by participating in this exciting event, our students have been introduced to the world of computational thinking in a fun and engaging way. The challenges are designed to push students to think critically and creatively, developing essential problem-solving skills that are applicable both inside and outside of the classroom.” – Sharon Pendreigh from Brownedge St Mary’s Catholic High School
If you are either a UK school or teach a UK-based curriculum, then visit the UK Bebras website for more information and to register your school.
Once you’ve registered, you’ll get access to the entire UK Bebras set of questions from previous years, allowing you to create custom quizzes for your students to tackle at any time throughout the year. These quizzes are self-marking, and you can download your students’ results to keep track of their progress. The questions are perfect for enrichment activities, end-of-term quizzes, lesson starters, and even full lessons to develop computational thinking skills and promote computing concepts.
Register for free at bebras.uk/admin.
Here’s a question we ran in 2018 for the Juniors group (ages 10 to 12). Can you solve it?
This is the map of a park:
The green circles with letters represent the trees and the brown lines are paths. Note that some letters are used to label more than one tree. Walking from tree F to tree B can be described as F D E C A B.
Last Sunday two families walked in the park.
The Wilde family’s walk was B A A A C E D E E D A.
The Gilde family’s walk was F D C D A E A D E D A.
Both families started their walks at the same time.
Walking from one tree to another tree, down one path takes the same amount of time.
How many times did the two families meet at a tree?
They never met at any of the trees.
Computer scientists often use graphs (and then they speak about vertices and edges instead of trees and paths connecting them).
Another interesting point about this task is the representation of the walks in the park. Despite the fact that some of the trees (vertices) are marked by the same letter, the walks that start from B or F can be unambiguously described by the sequence of letters along the walk. It means that one sequence of letters describes only one walk. It is because the neighbours of each tree (neighbours of tree X are the trees that are directly connected to X by a path) are always labeled with different letters. So if we know where we are at some moment of the walk and we see the next letter in the walk’s representation then there is no doubt which tree we should visit next.
This Bebras task was developed by the Bebras team in South Korea and refined by members of the international Bebras community.
In 1936, a Hungarian mathematician called Dénes Kőnig wrote the first ever textbook on graph theory. Today, graph theory is fundamental to computer science and in particular networking, operation research and data structures.
Did you get it right?
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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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When faced with a programming problem, computing educators often start solving it without a second thought. Before writing a single line of code, experienced programmers use a range of skills to choose their approach and make other key decisions. Thanks to repeated practice, many of these decisions feel automatic and require little conscious thought. However, for beginner programmers, starting a programming problem can feel overwhelming, requiring intense thought and concentration.
So how can we be more aware of the automatic steps we take and choices we make so that we can break them down for learners, and make it easier for them to solve problems?
In today’s blog, we look at what ‘subgoal labels’ are and how they can be used as a form of scaffolding to support learners in breaking down computer science and programming problems.
We also share the new subgoal labels Quick Read, which you can download for free to:
Subgoal labels are simple instructions that can be added to tasks — they are particularly useful when added to worked examples. They act as signposts to provide clear directions for a larger task.
Let’s look at the following example: You’re working through a programming problem with your students. The task requires them to draw a shape using a turtle sprite. Instead of just showing learners a complete script, you could instead add these subgoal labels to the worked example.
Programming problem: Draw a blue square in the top right corner of the screen using a screen turtle.
The labels act as scaffolding for students, helping them break down the task and work through it. While the exact number or specific context of the goals might change from one project to the next, the idea is always to break down tasks into smaller, more manageable actions.
You do not need to redesign your entire curriculum to start seeing the benefits of this approach. Here are three practical ways to introduce subgoal labels in your next computing lesson:
Integrating these labels into your teaching practice offers lots of benefits, for example:
Download the full Subgoal Labels Pedagogy Quick Read as a PDF:
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We work with partners globally to promote AI literacy through Experience AI, our programme created in collaboration with Google DeepMind. With resources available in 19+ languages and a network of partners in over 38 countries, a key part of our work is translating and localising our materials so as many young people around the world as possible build the confidence to engage with AI critically and responsibly.
But localisation introduces a unique hurdle: sometimes, languages are divided into ‘small languages’ and ‘big languages’.
As a speaker of Croatian, a ‘small language’, I think about this divide often. What makes a language small? A small language has a small number of speakers and is usually not considered a key market. Practically, this means resources for translation are often directed towards ‘big languages’. This makes sense from an impact perspective: translating into languages with more speakers maximises reach.
That is why, as a localisation coordinator at the Foundation, I am delighted that we partnered with Croatian organisation Suradnici u učenju to ensure that for Experience AI, Croatian is not treated as a small language — it is simply a language.
But translating into Croatian can present hurdles. When I got my first computer in Croatia, the user interface was in English. As a result, I never learned how to say “copy/paste” in Croatian. Those types of menus and tools had simply not been translated yet when I lived there. This happens a lot, especially with software and fast-emerging technologies like AI. Native speakers have no other choice but to use English words: the English words enter the language, and sometimes they stay.
As a result, translating key Experience AI terms into Croatian wasn’t easy. Even terms like ‘AI’ went through several rounds of discussion. Should we use the English ‘AI’ (artificial intelligence) or the Croatian ‘UI’ (umjetna inteligencija)? How should we pronounce ‘AI’ or ‘UI’ in spoken language?
Sometimes, it can be hard to know what the right thing to do is. While the English terms have been normalised, the Croatian word can seem foreign and out of place. When making a decision about how we translate terms for Experience AI resources, we also have to think about fairness and accessibility. Is it fair to assume that all young people and educators understand English words? If we assume incorrectly, we are preventing some learners from fully accessing and understanding our materials.
That’s why our in-country partners are a big part of our translation process. When we aren’t sure about whether we are making the right choice for educators and learners, we can rely on their expertise. They are not only native speakers, but also subject matter experts. For Croatian specifically, Suradnici u učenju did a very thorough review of the translation.
Beyond AI terminology, localisation also meant aligning resources with the terminology and conventions already used in Croatian schools and curricula. This ensured that the resources felt familiar to teachers and reflected the language they already use in classrooms, textbooks, and educational guidance.
As Lidija Kralj, from Suradnici u učenju explained:
“It was very important that an Informatics subject expert worked together with a Croatian language expert, and that both of us are teachers. We discussed whether to use the English abbreviation ‘AI’ or the Croatian ‘UI’, and ultimately felt it was our responsibility as educators to use Croatian terms where they already exist. We also wanted to be consistent with terminology that teachers already know from textbooks and the Croatian education system.
This combination — a subject expert, a language expert, and a Raspberry Pi Foundation localisation expert who understands our language — helped us create resources that feel natural and ready for classroom use. The response from participants in our first Experience AI course in Croatian has already shown us how much teachers value having fully localised materials.”
We opted for the approach where, if a Croatian word exists, we will use the Croatian word. Personally, I enjoyed seeing words like ‘offline activity’ slowly disappear from our resources and get replaced with Croatian words like “aktivnost bez računala” (activity without a computer).
As a result, the Croatian translation of our resources now flows very naturally, is accessible, and doesn’t read like a translation. You can check out our Croatian resources online.
We work with partners worldwide to bring AI literacy education to millions of young people. Discover all Experience AI partners here.
And to learn more about our resources and to see what other languages we translate into, check out the Experience AI website.
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There’s something really special about a Code Club. It’s a unique space for young people to get creative with technology on their terms, explore what interests them, and learn through experimenting and having fun. But speak to any Code Club leader and they’ll tell you: Code Club delivers so much more than just coding skills.
In the thousands of free Code Clubs across the world, you’ll see young people not only make amazing tech creations, but also grow as people. You’ll see young people find their voice and grow in confidence as they present what they’ve made to a room full of friends and parents. You’ll see them show a problem in their code to their friends and work as a team to fix it. You’ll see young people come up with the most creative digital solutions to challenges they see around them.
We’re delighted that the UK government recently recognised Code Club as a key resource to help schools enhance their enrichment offer. This confirms what the community tells us, and what independent research shows: that Code Club works. Not only does it help young people develop their programming skills, but it also builds life skills including confidence, resilience, and problem solving. And Code Club is completely free. Schools, libraries, and community centres can set up Code Clubs using their existing equipment, and register their clubs to access all our free resources and support for getting started and running successful club sessions for the long term.
And what about AI? There’s no getting around it, at the moment AI dominates almost every conversation about computing education, and this includes enrichment and non-formal activities too. All young people should have the power and agency to understand, question, and shape the AI systems increasingly affecting their lives. We believe that kids still need to learn to code in the age of AI, and we’ve created resources and coding projects for Code Clubs that help young people understand how AI tools work, and how to use them carefully.
Last year we announced our ambition to support every school and library in the UK to set up a free Code Club so that young people can develop the skills and knowledge they need to thrive in the age of AI.
Now we’re pleased to announce a package of free support for UK trusts and local authorities that are interested in becoming growth partners and setting up Code Clubs across their networks. If you join us as a growth partner, you will get:
If you would like to find out more about how Code Club can enhance your enrichment offer, how easy it is to get started, and how we can help, please get in touch!
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Last fall I met with Mark Nechanicky and Taryn Israel Nechanicky, two teachers from Albert Lea, Minnesota. Mark and Taryn are bringing computer science into their classroom with Experience CS, our free cross-curricular resource teaching computer science concepts and reinforcing students’ content knowledge in areas including math, science, language arts, and more.
We talked about some of the challenges they faced, and how they’ve worked around them, including constraints on their time and the requirements around teaching their core content areas. But most of our conversation was on the impact the Experience CS resources had on their students. Mark and Taryn shared how teaching cross-curricular computing helped their students to find joy in their learning, express creativity in their projects, and build a sense of leadership within the classroom.
In a little under a week Mark, Taryn, and I will be presenting a breakout session titled Foster Student Engagement, Confidence, and Collaboration through Integrated CS in New Orleans, Louisiana at the Computer Science Teachers Association (CSTA) annual conference. You can read more about our session at the end of this blog post. For now, here’s a preview.
When it was time to code, Taryn’s students cheered. She said, “I believe this is because the curriculum makes coding feel like play, which is the basis of how Scratch is designed. Students have choices, agency, chances to make mistakes, and problem-solve individually and together.”
Taryn shared an example of student agency she saw when teaching the first lesson in Weather watchers, a unit designed for students in third grade (ages 8–9) that integrates math and science concepts with programming. During that lesson a student was able to bring her beloved dog, Chewy, into her program as a sprite, and create something meaningful to her.
While we were designing the units of Experience CS, we focused on ensuring that our resources provided students and teachers with properly scaffolded learning experiences. We did this to support all students in their computing journey, and to provide them with the opportunity to be creative and express themselves in their programs.
In the Weather watchers unit, students collect weather data and create picture graphs using sprites that represent different weather conditions. Mark’s class let us know the starter project for the unit was missing something — keep an eye out for a tornado sprite in the future! With a little creativity and problem solving, they were able to design just the sprite they needed.
Mark added, “students aren’t just following step-by-step directions to create identical projects. As they work through the lessons, they have the flexibility to add their own sprites, design additional interactions, customize characters, and extend projects in ways that reflect their interests.”
The largest part of our discussion was how teaching cross-curricular computer science created environments for all students to become leaders. “One thing I’ve noticed is that coding creates leadership opportunities for students who don’t always get the chance to be seen as the expert. Because it is a new experience for almost everyone, it changes the dynamic in the classroom. Students with Individualized Education Plans (IEPs), English Language Learners, introverted students, and students who may struggle in other academic areas are often the ones discovering new ideas, solving problems, and showing classmates how to do something cool.”
Mark also shared an experience he had while teaching Digit dash, a game design unit that reinforces multiplication fluency, designed for students in fourth grade (ages 9–10). That unit was the first time his students had explored how variables can be used to store a score in a game. The first student to figure out how to use variables in their program was introverted, but became the class “expert” on variables and modeled it for his fellow students.
If you’re heading to New Orleans, we hope to see you at our CSTA breakout session, Foster Student Engagement, Confidence, and Collaboration through Integrated CS. During our session, you will be able to hear directly from Mark and Taryn on the impact that teaching Experience CS has had on their students in the last school year. We will include examples from the Experience CS units Weather watchers, Logic and lore, Picture this!, and Digit dash.
Our CSTA session will take place on July 17th, from 3:00 PM to 4:00 PM CT, in Borgne (Floor 3).
And we’d love to hear from you. Have you used our Experience CS resources in your classroom? How did it go?
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Learning something new requires effort. Learners take in new information by listening and observing. When a lot of information is presented at once in a lesson, that can create too much cognitive load for learners — a barrier to understanding and engagement.
To help you design and deliver great computing lessons, we’ve written two new Pedagogy Quick Reads focused on Mayer’s Principles of Multimedia Learning. These research-backed principles give you practical strategies to lower your students’ unnecessary cognitive load during lessons, leading to better learning outcomes.
In this blog, we introduce the two new Quick Reads (Designing multimedia for clarity and Designing multimedia for understanding), which you can download for free to:
The blog also includes some examples for how to apply the principles in your computing lessons.
If you’d like an introduction to the idea of cognitive load, you can find the Quick Read about cognitive load theory here.
Mayer’s Principles of Multimedia Learning are practical principles that will help you create clearer resources and present information in a way that avoids unnecessary cognitive load for your learners.
Mayer’s Principles are based on three related facts:
Our first new Quick Read focuses on the following Mayer’s Principles for making your lessons as clear as possible, so that learners can connect the information they see and hear in real time.
Our second new Quick Read shares three Mayer’s Principles for how you can structure your lesson delivery to support your learners’ understanding:
We suggest you consider implementing Mayer’s Principles when you next design new lessons or want to adapt materials that you reuse regularly.
Here are some ideas on how you use both sets of principles in common computing teaching scenarios.
When displaying a new Python script or Scratch project, avoid adding long, written paragraphs of commentary to explain the code. Instead, place short text annotations or sub-goal labels directly next to the relevant lines or blocks. As you run through the code, use your pointer or live typing to guide your learners’ focus (signalling) and explain in words how the program works at the same time.
Before students move to a new topic, for example networking, consider what words or concepts your class needs to be familiar with. Allocate a few minutes at the start of your lesson for pre-training to introduce terms like LAN or bandwidth and how they relate to the lesson.
Consider how your lesson can be divided into stages to allow for better understanding (segmenting). Each stage should build on the previous one and feed into the next one. For example, when you explain how data moves across a network, you can introduce each step separately before combining them all into a complete model of a network.
Consider how you display visual information to your class. Ensuring related diagrams and labels appear close together, only include relevant materials and no decoration on your slides (coherence), and avoid simply reading out words on the slide identical forms of information (redundancy).
Mayer’s Principles are even more important for educators teaching multilingual learners or non-native speakers. When learners need to work harder to understand the language, poor lesson design can slow down their learning significantly.
Mayer’s Principles can help you with this challenge:
By intentionally designing and presenting lessons to give the right amount of information in the clearest way, you make it easier for your students to focus and build a lasting understanding of computing concepts. When your lesson materials align with how our brains process information, learners can build stronger mental models and approach independent learning activities with greater confidence.
Read our new Quick Reads to find out more and discover the research behind Mayer’s Principles:
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The UK Government is undertaking the first major review of England’s curriculum and qualifications system since the current national curriculum was introduced in 2014. We believe that this is an excellent opportunity not simply to update the computing curriculum content, but to reconsider what computing education is for, who it serves, and how it prepares young people for life in a digital society.
Today, we are launching a report featuring 6 key priorities for curriculum reform based on discussions we have had with students and teachers.
Too often, curriculum reform happens around students and teachers rather than with them. Yet these are the people who experience computing education every day, and they have valuable insights into what is working, what is not, and what needs to change.
Our new report is based on a series of student focus groups and teacher workshops held by us and the University of Cambridge in Manchester, London, and Cambridge during spring 2026.
The student discussions included young people currently studying computer science at GCSE and A level (ages 14–18), as well as young people who had decided to not continue with the subject. We also brought together 18 computing teachers from secondary schools across England to explore curriculum priorities and challenges for implementation.
Although participants in our workshops had differing perspectives, they consistently pointed to the same underlying challenge: the current curriculum no longer reflects the realities of technology, work, or young people’s lives.
“AI is everywhere now, we need to understand how it works, not just be told not to use it.”
– Student, London
In particular, many students highlighted a lack of confidence in their practical digital skills despite using technology constantly in everyday life.
“I can code a bit, but I don’t know how to use Excel properly, that’s what I’ll actually need.”
– Student, London
Students and teachers are not calling for a less rigorous curriculum. Nor are they arguing that computing should lose its technical foundations, with teachers consistently emphasising the value of understanding computational thinking, programming, algorithms, data, and computer systems and networks.
Instead, students and teachers want to make computing education more practical, relevant, inclusive, and future-facing.
Teachers particularly emphasised the importance of helping students understand how AI systems function, including issues such as bias, training data, limitations, and ethical implications. Participants argued that computing education should help young people understand the technologies shaping their lives, not simply prepare them for examinations. Perhaps the strongest area of agreement was that curriculum reform will only succeed if it is matched by investment in teaching.
Based on our discussions with students and teachers, we have identified several priorities for curriculum reform.
All students should leave school with a strong foundation in digital literacy, online safety, data awareness, and AI literacy. These should be treated as essential components of modern education, not optional extras.
The curriculum should focus on contemporary technologies and concepts, including AI, cybersecurity, and data, while trimming content that is overly specific, outdated, or disconnected from modern practice. Foundations such as programming, algorithms, and computational thinking should remain central.
Computing should be taught through making, experimentation, and problem solving. Project-based learning, physical computing, and relevant, real-world applications should become central approaches across all ages.
The revised curriculum should balance a shared foundation with opportunities for students to specialise in areas aligned with their interests and aspirations.
Any reforms should actively address barriers to participation by ensuring inclusive teaching approaches, diverse, relatable role models, and accessible learning experiences.
Curriculum reform must be accompanied by sustained investment in teacher recruitment, professional development, and classroom resources. Without this, reforming the curriculum risks widening existing inequalities in provision.
We believe listening to students and teachers should be central to curriculum reform. You can read the full report now:
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What does great training for computing teachers look like?
High-quality professional development (PD) is one of the most effective ways to improve your students’ outcomes, and participating in PD is a core part of being a teacher. By changing and refining your teaching practices, you can create a direct, positive impact on the young people we teach.
In this blog, we share our new professional development Quick Read, which you can download for free to:
Professional development is vital for anyone teaching computing. In many subjects, the curriculum stays the same for decades. Computer science, however, can change quickly — as with the development of new technologies such as AI and quantum computing, and new hardware — and PD can help you stay up to date.
Another challenge is that many educators who teach computing are not necessarily subject specialists. So educators often benefit from building their own subject knowledge and learning new computing-specific pedagogical approaches through PD.
Drawing on academic research and our own expertise, we’ve identified several key principles that make professional development effective for computer science educators:
There may be factors that influence your teaching that you have little control over: you might teach computing as a standalone subject, or you may be required to weave computer science into other lessons across the curriculum. Or you might be working with older devices or limited internet access, all of which have an impact on your practice.
Effective PD should recognise these realities, and offer practical tools that work for your specific classroom and students.
You can use the principles in our latest Quick Read when you’re looking for your next training course or if you are designing a session for your team. The full list of principles is available in our Quick Read, but here are some ideas for you to consider:
You can read more about the principles of effective PD in our Quick Read.
Potential benefits for teachers:
Potential benefits for learners:
Our new Quick Read shares tips on how to best use these principles in your setting.
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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.
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The complete set of Experience CS units is now available.
With the release of the final six units, Experience CS now provides 18 cross-curricular, project-based computer science units for grades 3–8. It offers educators a collection of free, standards-aligned units to bring computing into their classrooms with confidence.
This milestone marks the culmination of a year of development, iteration, and collaboration with educators. The result is a set of learning resources designed by educators for educators, for real classrooms and real teaching needs.
Experience CS is built to make computer science (CS) accessible, engaging, and relevant for all learners, and teachable for all educators.
Rather than treating CS as a standalone subject, Experience CS integrates computing into core subjects like science, math, and the arts. Students learn key concepts such as sequencing, loops, cybersecurity, and networks while exploring meaningful, real-world themes. The Experience CS platform includes a school-safe version of Scratch that is easy for teachers to use and where students can explore making coding projects.
Every Experience CS unit includes:
No prior computer science experience is required — Experience CS makes it easier than ever for schools to get started.
Since we launched Experience CS in June 2025 with the first 6 units, we’ve worked steadily to create more resources, guided by feedback from educators and classroom testing.
Now complete, Experience CS offers:
Along the way, we have also added:
The final Experience CS units give students more opportunities to create, explore, and apply their learning:
While the curriculum is now complete, our work to expand access is just getting started.
Throughout 2026, we will host sessions, workshops, and conference events to support educators across the US in bringing Experience CS into their classrooms. These free professional development opportunities will help teachers build confidence, explore best practices, and connect with a growing community of educators.
We will host a webinar on April 30 at 3pm CT / 4pm ET to highlight the new units. Register today to join:
And you’ll find us at the following events this spring and summer:
And more! Keep up with us on social media to stay in the loop with conference appearances and sign up for Experience CS mailing updates to hear about upcoming PD opportunities.
Experience CS is already being implemented in schools and districts across the United States. With Data Privacy Agreements (DPAs) now in place in multiple states, even more districts can adopt the curriculum.If you have any questions about DPAs or district expansion, please reach out to experience-cs@raspberrypi.org.
The completion of Experience CS marks a big step forward in making computer science education accessible, creative, and inclusive.
The cross-curricular design of each unit empowers non-specialist teachers to confidently introduce computer science in their subject and help students connect it to the world around them.
The complete Experience CS curriculum is now available.
Visit experience-cs.org to explore all 18 units and get started in your classroom.
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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.
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On 7 February 2026, we witnessed something special in Bhubaneswar: a day where classroom experience took centre stage in conversations about computing and AI education in India at our Computing and AI for All conference.
At this event, we brought together educators, researchers, and system leaders for shared learning, reflection, and professional dialogue where classroom practice was placed at the heart of the discussion.
Across computing and AI education in India, conversations about policy, pedagogy, research, and future readiness often take place in separate spaces. At Computing and AI for All, we wanted to connect these strands in a national forum where classroom practitioners, researchers, and system leaders could speak with each other about computing and AI education.
The theme ‘Computing and AI for all: Classroom to policy’ connected:
Bringing these conversations together helped shape a richer dialogue than we’ve seen in many traditional education forums.
The response from educators to the opportunity to attend was both energising and encouraging. 165 educators and 22 invited guests joined us on the day. The event also featured 35 paper and poster presentations, reflecting the breadth of classroom practice, research, and innovation that teachers were keen to share.
The conference also brought together diverse contributors. Alongside teachers, there was representation from organisations such as Computer Science Teachers’ Associations, the University of Southampton, Quest Alliance, and Learning Links Foundation. Also part of the event were attendees from government bodies including TTWREIS – Telangana Tribal Welfare Residential Educational Institutes; PSSS – Panchasakha Shikhya Setu Sangathan (a Govt. of Odisha initiative); and IIITMK Kerala. The presence of an international delegate from Nepal further broadened the exchange of perspectives.
First and foremost, the event was rooted in classroom practice. 88% of participants were ICT teachers, with strong representation from Odisha and Telangana, where we have been working on partnerships to support computing educators. A significant proportion of the educators present were attending a computing conference in person for the first time, so it was a milestone in their professional journey.
Teachers stepping onto a national stage to present their classroom experiments, research, and reflections signaled an important shift: computing and AI education in India is no longer something designed solely for teachers. It is increasingly being shaped by teachers and informed by their lived classroom realities and professional expertise.
The conference featured a full-day academic programme beginning with a formal inauguration and an introduction to our work in India.
The keynote by the Foundation’s Director of Research and Impact, Shuchi Grover — ‘Democratizing the future: Why computing and AI literacy matters for every child, everywhere”’ — set an equity-driven, future-focused tone. It reminded us that AI literacy is not optional; it’s foundational for learning and opportunity.
In the panel discussion on ‘Embedding ethical AI in everyday teaching: From principles to classroom practice’, teachers, researchers and leaders exchanged ideas about how to integrate responsible AI without losing sight of learning goals.
Across sessions, teachers and researchers shared a wide range of classroom-centred insights, including:
These sessions reflected both pedagogical creativity and deep engagement with everyday classroom challenges.
The structured poster gallery created space for peer feedback and deeper discussion around critical themes such as:
In the feedback we captured, educators described the conference as:
One participant shared: “I’m honoured to be part of [the conference]. It was inspiring to listen to educational experts sharing valuable insights on AI tools and education.”
Many participants asked us to expand the event, suggesting a two-day format, extended hands-on AI workshops, and practical demonstrations they could take back to their classrooms.
Our Computing and AI for All event formed a national, educator-led platform linking classroom innovation, AI literacy, research evidence, and policy dialogue.
What stood out most was the spirit of participation:
If AI is shaping the future of work and society, then teachers need to play a part in shaping the future of AI education. Computing and AI for All was one move in this direction that we were pleased to facilitate — and it is only the beginning.
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At the Raspberry Pi Foundation, we believe that learning to program equips young people with the knowledge and skills they need to thrive in an increasingly digital world. For many educators, teaching programming effectively can be challenging, particularly when their learners are at different stages in their programming journey. Ask learners to write code too early, and they might struggle or feel intimidated. Rely too heavily on step-by-step instructions, and you limit learners’ chances to explore ideas or develop deeper understanding.
The PRIMM framework — Predict, Run, Investigate, Modify, Make — provides educators with a structure for teaching programming. This research-informed teaching approach balances support with independence and helps learners build their understanding before they write their own code, whatever their starting point.
To help educators use this approach confidently, we have launched a new short online course, Using PRIMM to teach programming, which is available on our new Training Hub platform for free.
This practical, self-paced course gives educators the knowledge they need to use the PRIMM approach to design and adapt programming activities to suit their learners.
The course takes 1–2 hours to complete, and we have designed it for educators working in formal or non-formal learning environments around the world, using any block-based or text-based programming language. All you need is some experience of creating and adapting simple programs.
The course starts with considering the five stages of PRIMM, when and why to use each stage, and how they work together to support learning. It covers how PRIMM aligns with key teaching principles such as scaffolding, managing cognitive load, and progression, and examines how the approach supports formative assessment by making learners’ thinking — and any misunderstandings — more visible.
Although pedagogy forms the core of this course, we have deliberately avoided a theory-heavy approach. Instead, the course is designed to help you learn through hands-on activities. By reflecting, taking part in discussions with other computing educators, and completing practical tasks, you will explore how PRIMM works in real teaching contexts.
After an introduction to the core ideas of PRIMM, you will design a new programming activity, or adapt an existing one, using the PRIMM structure. This will support you to think carefully about what your learners know and can do, likely misconceptions, and how each stage of PRIMM can be used effectively, including when your learners have varied learning needs and levels of programming experience.
With its emphasis on activity design, the course will support you to develop resources you can use and keep adapting in your own setting. By the end, you will have a complete PRIMM activity designed specifically for your learners, and a clear sense of how to teach programming in a structured and supportive way.
Using PRIMM to teach programming is available on our new Training Hub, where we offer all our professional development courses for free. The Training Hub offers flexible, reflective learning experiences across a range of topics, helping you build your subject knowledge and bring research-informed teaching approaches into your day-to-day practice.
Whether you are an experienced computing teacher, a volunteer educator, or a parent looking to support their child’s learning, we invite you to join us there.
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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.
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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.
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There’s always something new to consider when teaching with technology. From the latest advancements in AI, to new software and hardware updates, it can be difficult to know which tools to use and how to incorporate it effectively into your lessons.
In today’s blog, we explore the PICRAT framework and how it can help you reflect on your use of technology in the classroom.
We also share our new PICRAT Quick Read, which you can download for free to:
Technology is constantly changing, and educators must continually decide what tools to use in their practice. To help with this challenge, researchers started developing theoretical models that teachers (especially student teachers) could use to reflect on how they integrate technology in their classrooms.
You might already be familiar with frameworks like TPACK (Technology, Pedagogy, and Content Knowledge) and SAMR (Substitution, Augmentation, Modification, Redefinition). While these models are useful, the PICRAT framework was created to address gaps in these earlier models, offering a clearer, student-focused approach. Significantly, it encourages you to treat technology as a tool to support learning, rather than the goal itself.
It asks two simple questions: “How are students experiencing the technology?” and “How does this impact your practice?”. The answers to these questions form a matrix as pictured below.
PIC (which runs along the y-axis) refers to the student’s relationship to the technology:
RAT (which runs along the x-axis) refers to how the teacher uses the technology:
First choose the lesson you’re planning to deliver. Consider what activities you’ll be running and the technologies involved. You’ll then be able to plot where they sit on the matrix using the PICRAT acronym.
For example, if you are teaching a lesson on Python loops, you might initially plan for students to watch a pre-recorded coding tutorial on their laptops. In this scenario, the student experience is Passive (receiving info via tech), and the teacher’s use is Replacement because the video simply replaces a live lecture. To move up the matrix, you could instead have students use an online IDE to complete a “Parson’s Problem” puzzle where they rearrange blocks of code to fix a loop. This shifts the activity to Interactive and Amplification, as the digital tool provides immediate debugging feedback that a paper-based exercise could not.
Next, think about how you might move your practice forwards. Although every position on the matrix has its own value, the framework is hierarchical. The overall goal is to try to move your practice towards the top right of the matrix to be Creative and Transformative.
To help you achieve this, take some time to reflect on your current lessons, activities, and the technologies you use. Ask yourself questions like:
Research highlights that technology is rarely used in ways that allow young people to be creative. By using the PICRAT matrix, teachers can identify missed opportunities and explore ways to transform their lessons, ensuring learners can be creative and thrive.
Potential benefits for educators:
You can use the PICRAT framework as part of your own reflections, or as part of a group activity. It’s a great way to spark discussion about technology integration with colleagues and improve best practices.
If you’d like to learn more about the PICRAT model, you can download our Quick Read for free via our new Pedagogy Quick Reads page.
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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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Traditionally, curriculum planning has often looked like a linear list: Topic A leads to Topic B, which leads to Topic C. However, as educators we know that learning rarely happens in such a simple, linear way. Concepts are regularly covered in different overlapping topics, and students can often take different routes to reach the same destination.
In today’s blog we’re exploring learning graphs, a helpful tool that you can use to plan your computer science curriculum. We’ll share how they can provide educators with a clear, structured way to visualise students’ non-linear progression in a subject.
We also share our new Pedagogy Quick Read about learning graphs, which you can download for free to:
A learning graph is a visual tool for curriculum planning that moves beyond simple lists. At its core, a learning graph is a network of ‘nodes’ (specific concepts and skills) and ‘links’ (the connections between them).
Learning graphs build on research into ‘learning progressions’ and ‘knowledge maps’. They are a practical tool that educators can use to design and validate different curricula. For example, they can help teachers to:
Building a learning graph is an iterative process that helps you think critically about how different parts of your curriculum relate to each other.
The first step in creating a learning graph is often to identify your start and end nodes. First, you consider the key concepts and skills that your learners must acquire by the end of a series of lessons. This gives you some end nodes to work towards. Then, you think about learners’ existing knowledge, to help determine your start point. You then work backwards and forwards between these points to identify the different nodes that learners need to cover to get from the beginning to the end.
Once you have determined your nodes, you add them to your graph and connect them via ‘links’ until your graph is complete. Where knowledge of particular concepts or skills is essential for learning others, you connect the nodes with solid lines. For prior learning that is helpful but not essential, you use dotted lines.
When developing a learning graph, there isn’t a specific level of granularity that you have to work towards. Progression can be as detailed or as high-level as you need. This makes them a helpful tool in creating bespoke learning experiences and curricula for learners.
It is most effective to design learning graphs collaboratively within a small group. This allows curriculum designers to discuss their ideas and challenge each other’s thinking, which helps hone the designs.
When creating learning graphs, it can be extremely useful to use a tool that is dynamic and allows you to move elements and make changes quickly and easily. At the Raspberry Pi Foundation, our team has experimented with a range of tools, including using editable shapes in Google Slides, collaborating in Figma, and arranging sticky notes on paper. We recommend finding a tool that works for you and the educators you are working with. Although it can work, we suggest avoiding using a pen and paper if possible, as designs can quickly become messy and difficult to navigate after lots of iterations.
The process of designing learning graphs has strong links to ABC learning design and the creation of concept maps, which can also be used for curriculum planning.
Once created, learning graphs can support you to design and adapt your curricula and assess your students’ learning.
For example, to help sequence learning, you can track or predict the paths through a topic most commonly taken by learners and use this to inform your curriculum design.
If you are adapting a unit of work for a specific qualification or new context, you can prune nodes that are not relevant and add any further knowledge and skills your learners need, then use the new learning graph to guide you as you develop the unit.
Finally, you can assess which node a learner has completed, and use this to identify the next logical step in their learning, ensuring the difficulty level is always appropriate.
Another benefit of learning graphs is that they can be combined with lots of other frameworks, for example, Bloom’s taxonomy. This allows you to better assess and validate the learning journeys you have designed, and ensure that they are suitably accessible, challenging, and relevant for your learners.
There are a number of ways that you could link your learning graphs to other frameworks, such as annotating nodes with extra information, or using colour coding.
As well as working with learning graphs for specific learning experiences, you can connect multiple learning graphs together and analyse how they intersect. This can help identify inconsistencies between connected sequences of lessons. It can also help uncover broader themes of progression and highlight alternative learning pathways you might not have considered.
If you’d like to find out more about learning graphs, you can download our Pedagogy Quick Read for free.
To find out more about how we use learning graphs when planning curriculum resources at the Raspberry Pi Foundation, take a look at our teaching and learning design principles.
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In November, our first-ever Kenya Partner Showcase brought together all our partners from across the country for two days of collaboration, learning, and shared strategy. What stood out to us most from the event was not just the diversity of work that the Kenyan partners are doing in computing education, but also the clear alignment that is emerging across partners, government, and communities.
Partners used the Showcase to present sessions about their journeys implementing the Foundation’s programmes, sharing achievements and insights that strengthened the collective learning space. The conversations during our two days together reflected a maturing ecosystem and demonstrated how structured government buy-in is accelerating adoption of our localised Computing Curriculum and influencing national policy spaces.
“Through these programmes, we are equipping young people to become future-ready leaders of integrity and impact.” – Betty Oloo Anderson, National Executive Officer, Kenya Girl Guides Association
It was encouraging to see how far computing education has evolved since we began working with Kenyan partners in 2023, signalling the collective effort and commitment driving this work forward.
The sessions revealed a powerful shift already taking place in classrooms, with partners demonstrating how embedding The Computing Curriculum into Teacher Professional Development frameworks is building teacher confidence and elevating the quality of classroom instruction through stronger digital competencies and culturally relevant pedagogy. The partner presentation on Experience AI, our AI literacy programme, was especially powerful. It reframed the national dialogue from questioning whether AI might take over the classroom to recognising the real capacities and limitations of AI tools, and the central role teachers play in guiding young people to use and create AI tools in safe, ethical ways.
“Running Raspberry Pi Foundation programmes has been a transformative journey. It has challenged us to innovate, document rigorously, and continually place learners at the center of every decision. The structured support, tools, and community of practice have enabled us to deliver programmes with greater impact and accountability.” Joel Kahindi, Programme Coordinator, STEAMLabs Africa
Hands-on demonstrations, from physical computing with Raspberry Pi Pico to teacher development transitions from Scratch to Python, highlighted how practical computing is taking root in both formal and non-formal learning environments.
Partners from remote and underserved regions shared how locally contextualised computing tools and programmes we offer are reaching learners in ASAL (arid and semi-arid land) regions, strengthening literacy and digital confidence even in low-resource settings. Complementing this, curriculum-focused partners highlighted how structured computing resources are being adapted for ASAL contexts, reinforcing foundational skills at scale. Efforts around community-centred connectivity are enabling schools, especially in underserved communities, to finally access reliable high-speed internet.
“Through [our partnership with the Raspberry Pi Foundation], we are not only strengthening our programmes but also expanding our vision for what meaningful learning can look like across underserved communities.” – Joel Kahindi, Programme Coordinator, STEAMLabs Africa
A notable pattern that emerged was how existing collaborations are now opening doors to new ones, with partners building on their relationship with us to form additional partnerships for devices, connectivity, and shared learning, creating a more holistic digital ecosystem for learners.
A major anchor of the Showcase was the Code Club co-creation sprint. Implementing partners worked through real barriers and opportunities: easier onboarding for educators, continuous training, integrating clubs into school calendars, learner-led ownership, and sustainable models that last beyond donor funding. There was strong interest in forming a unified Code Club Kenya partner network to streamline communication, resource-sharing, and visibility, and our team is now exploring what it would take to bring this to life.
One message echoed across sessions: the power of telling real stories. Partners expressed a shared desire to spotlight creator projects, Code Club leader journeys, and regional experiences more consistently, through social media, case studies and impact stories, and shared platforms, to make progress visible and inspire more schools to adopt computing and digital skills.
By the close of the Showcase, the takeaway was clear: Kenya’s digital learning ecosystem is no longer a set of isolated efforts — it is a coordinated network that shares priorities, evidence-based insights, and a commitment to scaling impact together. As one partner reflected, “This Showcase created a space for joint learning and will help us scale our impact across the country.” It felt evident, too, that bringing these voices into one room is itself part of the work. For us at the Foundation, this Showcase reaffirmed the responsibility and privilege of stewarding a network that is shaping what the future of learning computing in Kenya can look like.
The Showcase marks the beginning of a new, aligned chapter for computing education in Kenya, one driven by collaboration, clarity, and a shared belief that we go further when we go together. With shared priorities for 2026, the momentum continues, and we will be continuing to highlight where this collective work is headed next.
We want to thank you to all our partners — Frontier Counties Development Council (FCDC), STEAMLabs Africa, Young Scientists Kenya, Kenya Girl Guides Association, Oasis Mathare, Futures Infinite, EmpServe Kenya, Kenya Connect, Tech Kidz Africa, Riara University, and M-Lugha — for your leadership, dedication, and unwavering belief in what is possible when we learn and build together.
This Showcase was a reflection of your work, your commitment to learners, and your vision for a digitally empowered Kenya. We are grateful for your continued partnership and excited for all that lies ahead.
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To mark the start of 2026, we’re releasing a special episode of our Hello World podcast, which reflects on the key developments in computing education during 2025 and considers the trends likely to shape the year ahead.
Hosted by James Robinson, the episode brings together a conversation between three Foundation team members — Rehana Al-Soltane, Dr Bobby Whyte, and Laura James — and perspectives from colleagues and partners in Kenya, South Africa, and Greece.
The podcast is framed around three major themes that defined 2025: data science, AI literacy, and digital literacy, all of which continue to play an increasingly important role in education systems worldwide.
In the podcast, Rehana reflects on a year characterised by research, collaboration, and community, highlighting the importance of global partnerships in developing and localising AI literacy resources for diverse educational contexts.
From a research perspective, Bobby explains that 2025 was about pulling together what we already know and making sense of it, to better understand what good data science education should look like, including curriculum design, pedagogy, and appropriate tools.
Laura focuses on resilience and creativity in computing education, as well as the growing presence of more personalised forms of artificial intelligence, which present both significant opportunities and complex ethical challenges.
A key concern raised throughout the episode is the risk of cognitive offloading, whereby learners rely on AI tools to bypass critical thinking processes. The speakers emphasise the need for learning experiences and assessments that value process, reasoning, and reflection rather than solely final outputs.
The episode also examines barriers to the adoption of computing and AI education, including teacher confidence, limited access to devices, restrictive school IT policies, and the need for translated and localised resources.
Contributions from our colleagues around the world highlight stark contrasts in educational contexts, with challenges such as funding constraints, connectivity issues, and teacher training needs, alongside examples of innovation where educators are adequately supported.
Looking ahead to 2026, Rehana outlines the potential of interdisciplinary approaches to AI literacy, integrating AI concepts into subjects such as geography, history, languages, and the arts to increase relevance and engagement (look out for our upcoming research seminar series on the topic).
Bobby anticipates a gradual shift towards more data-informed approaches to computing education, with greater emphasis on classroom-based trials and research that directly informs practice.
Laura offers a strong call to renew focus on cybersecurity education, arguing that security and safety must remain central as digital systems and AI technologies continue to evolve.
In a series of concise predictions, the speakers point to increased attention on explainable AI, wider integration of AI literacy across the curriculum, and renewed concern for digital safety and security.
You can subscribe to Hello World and listen to the full podcast episodes from wherever you get your podcasts. Or you can find this and previous Hello World podcasts on our podcast page.
Also check out Hello World magazine, our free digital and print magazine from computing educators for computing educators.
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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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This blog is the third and final in our mini-series about the things we’ve learnt from adapting The Computing Curriculum resources, and from training teachers to use them in schools. In the first two blogs, we wrote about our experiences in Kenya and Odisha, India. Here, we focus on our work in Telangana, India.
This blog was written by Jaskaran Singh, Impact Manager, and Mamta Manaktala, Senior Learning Manager.
Every country and region has unique opportunities, challenges, and needs. In a vast country like India, every state is different — what works in Odisha may not work in other locations. Thus, to meet the needs of students in the state of Telangana, we’ve been working on adapting The Computing Curriculum specifically for them.
Our work in Telangana began in 2023, when we kickstarted a five-year partnership with the Telangana Social Welfare Residential Educational Institutions Society (TGSWREIS), a society under the Government of Telangana. Through the partnership, we’ve developed an adapted curriculum, along with training for educators working in educational institutions with limited resources. The adapted curriculum includes localised examples and activities, and teaching approaches to make the learning experience feel relevant and meaningful for students in Telangana, while keeping the core learning outcomes aligned with global standards.
Since the start of the partnership, we’ve been testing the curriculum at the Coding Academy School, a co-educational school at Moinabad, and the Coding Academy College, a degree college for women in Shamirpet.
Our work delivering the curriculum in Telangana was our first time using a direct-to-learners model. The Coding Academy School and College gave us unique opportunities to work with students directly and observe first-hand the difference the programme made in their learning journeys.
During the first year of implementation, we gathered useful feedback from students and teachers. Check out one of our earlier blogs where we share some of the findings. We used these inputs to further develop the curriculum.
This updated version of the curriculum was implemented in the 2024/25 academic year. At the school, our educators worked with 210 students in grades 7–9, while at the college, our educators worked with 382 undergraduate students. As in the first year, we used data from assessments, lesson observations, educator interviews, student surveys, and student focus groups to understand what’s working well and what could be improved. So what did we learn?
Our evaluation findings show that the updated curriculum worked well and positive outcomes are being achieved for most students. Educators felt prepared to teach the curriculum in this second year and found the ongoing support and spaces for discussion really useful. Moreover, we found that there are potential positive ripple effects beyond the school as well.
In surveys, 91% of students in the school and 96% of students in the college responded that the lessons helped them get better at computing and coding. Students feel they are not just learning new skills but also finding the content enjoyable: 88% of students in the school and 98% of students in the college responded that they are enjoying their classes. Educators and observers also reported that students were engaged during lessons, and often completed activities without needing any support.
Students’ assessment scores further confirmed positive learning outcomes. 4 out of every 5 scores in the school and 9 out of every 10 scores in the college were 60% or above, which was higher than in the first year of the adapted curriculum’s implementation.
The changes we made to the curriculum included:
Educators were highly positive about the updates to the curriculum.
“The students are able to [better] understand the examples because we updated [to] the India context examples.” — Educator, Coding Academy School
“Students are receiving it very well because we have modified the content this year, and [that includes] the placements of the unit and the connectivity of the lessons and units.” — Educator, Coding Academy School
Additionally, for the college curriculum, we aligned the content more closely with the learning objectives set by Osmania University — with which the college is affiliated. We also included more advanced topics for students specialising in data science. During interviews, educators reported that the content was now much better aligned to student expectations.
“[The curriculum] we have designed is based as per [the] Osmania University curriculum. [The lessons] are definitely meeting the students’ needs because whatever discussions we are taking in classes, they are [successfully] participating in those discussions and they are doing whatever activities we give them.” — Educator, Coding Academy College
Outside of knowledge and skills in computing, the curriculum is also helping students develop wider life skills. In our survey, college students shared that working on projects gives them a sense of accomplishment and the confidence to solve real-world problems. Many students also reported that through the curriculum they are developing higher-order thinking skills, which will support their future careers.
“The thrill lies the creativity and problem-solving aspects. I get to turn ideas into reality pieces, and there is something incredible satisfying about debugging code and watching it run flawlessly. It’s like slow, challenging puzzles, frustrating at times but rewarding when everything clicks.” — Student, Coding Academy College
“My favourite thing [about] the computing and coding classes [is the] Scratch programme. I have learnt it [for the] first time. By learning I have enjoyed a lot. During the coding process, it trains our brain to think deeply, identify trouble, and break things up and put pieces together [as] a solution.” — Student, Coding Academy College
Students are showing high interest in applying their skills outside of their classes. Almost all students — 100% in the school and 99% in the college — reported that they would like to participate in coding-related competitions.
Educators also told us that many students are exploring future job opportunities in the computing and digital technology fields, and are curious about topics outside the curriculum. Interestingly, 93% of the college students who were studying courses not traditionally associated with jobs in computing and digital technology reported that they would like to pursue a job in computing.
We have also learnt that a high-quality computing education for young people has potentially wider benefits for the community. One educator described how students are helping their families, many of whom have limited experiences, engage more confidently with digital technologies.
“Families don’t know how to use smartphones and laptop computers, but our students know very well so I can say they do teach to their elders how to use these platforms.” — Educator, Coding Academy School
To help educators feel confident and prepared, individualised learning resources were provided throughout the year. These were well received by educators. Educators also found the weekly meetings with our India-based team members useful to discuss ongoing challenges regarding delivery and assessments.
There were improvements this year in the availability of equipment, and the use of Wi-Fi dongles addressed internet connectivity issues to some degree. However, educators still faced some challenges. For example, educators in the school faced issues accessing printed worksheets and educators in the college faced issues accessing projectors during their lessons. We are working closely with our delivery partner to address these issues for the new academic year.
With regard to the content, educators felt the curriculum could benefit from some further amendments. For the school curriculum, these include easing the transition from block-based to text-based coding. For the college curriculum, there were suggestions for more focus on real-world applications of coding and including advanced topics, like machine learning, for undergraduates specialising in computing-related subjects. We have considered all these suggestions and made necessary revisions to the curriculum.
With the success of the pilot, we’re excited to announce that the adapted curriculum will now be implemented at over 350 schools and junior colleges in the state of Telangana. A majority of schools will be with the same partner, TGSWREIS, while some schools and junior colleges will be with other partners. The Coding Academy School will become our hub for trialling new curriculum content and strategies, and conducting research studies and teacher training and support. Additionally, the school will also host inter-school events.
The progress we’ve seen so far in Telangana is very encouraging. We look forward to continuing these partnerships and helping more young people realise their potential through the power of computing and digital technologies.
From our work in Telangana, Odisha, and Kenya, we’ve learnt that a curriculum isn’t a one-size-fits-all product. The local context, culture, and educational provisions are important considerations when adapting learning resources for different regions. We’ve also learnt that building long-term partnerships with organisations who have local expertise is key to understanding these considerations and effectively reaching communities where we can make the biggest difference. Finally, we’ve learnt that adaptation isn’t a one-time activity. It’s a cycle of continuous refinement; listening closely to feedback from the ground is important to ensure that our support for educators and learning experiences for young people have the best possible impact.
You can access our free Computing Curriculum resources on our website — we are currently working to make the materials for India and Kenya downloadable there.
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Throughout our lives, we’re constantly learning from others. Whether we’re interacting with teachers or trainers, or observing friends or strangers, we’re learning either deliberately or inadvertently. This process is known as ‘social learning’.
In today’s blog, you’ll dive into what social learning is and how you can use it to create more engaging and effective learning experiences in your computing classroom.
You’ll also find our latest Pedagogy Quick Read, which explores social learning. It’s free to download and includes:
Social learning is simply any learning that involves other people. It can take any form, from watching a video, to taking part in a classroom discussion. It can take place in person or online, and it can happen without people realising they’re learning something.
Social learning is based on modelling and involves people observing and imitating the behaviours that others model. Albert Bandura, the acknowledged originator of social learning theory, suggested that social learning is guided by four related processes:
There’s lots of ways you can involve social learning in your computing classroom, including through other teaching approaches and frameworks.
To help your learners get the most out of social learning, it’s best to:
Among our pedagogy resources, you’ll find lots of practical advice for teaching approaches that promote social learning. The approaches we recommend for the pedagogy principles ‘Work together’ and ‘Model everything’ are especially suitable.
Work together:
Model everything:
Using a PRIMM (PDF) approach for structuring programming lessons, and encouraging students to talk about code as part of these, also works well for social learning.
Let’s look at pair programming as an example. In this activity, pairs of learners work together to create a computer program, taking on distinct roles that they swap regularly. One learner acts as the ‘driver’, writing the code, while the other is the ‘navigator’, guiding the process, reviewing the code, and identifying potential issues.
As they work, each learner is able to observe the other person’s approach, learning with and from their partner throughout the activity. This constant interaction and shared problem solving can help them to understand programming concepts better and to build stronger teamwork skills.
Another example is setting your class the task to create shared digital resources on several topics everyone needs to learn about. In this activity, you split learners into small groups or pairs, and assign them a topic to later explain to the whole group. Grouped learners work together to create a resource explaining their topic. As the facilitator, you can either provide the information they need, or let them conduct their own research. At the end of the activity, each group presents their resource to the wider class.
An activity like this helps learners develop their knowledge through working together and talking to each other, and also provides the class with resources they can keep using.
Potential benefits for teachers:
Potential benefits for students:
In a world filled with complex challenges, there’s more need than ever for people to work together. By using social learning approaches in your classroom, you help your students to engage more deeply with your teaching and to develop the skills to succeed in collaboration with others. In this way, you’ll prepare them for navigating technological change as well as for shaping a common future where everyone can thrive.
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New research by the Alan Turing Institute, published in June, shows that in the UK nearly one in four children aged 8 to 12 is using artificial intelligence (AI) tools like ChatGPT. With learners exploring these tools at such young ages, it’s more urgent than ever to develop teaching resources that help them understand how AI works.
One of the biggest challenges with AI is that it’s hard to tell how these tools function. With a chatbot, you enter a prompt and the tool returns a response, but what happens in between is invisible. For educators, that’s a problem: how can we help young people become thoughtful, creative users of AI tools if the technology feels like a closed box?
In our May research seminar, we welcomed Matti Tedre and Henriikka Vartiainen from the University of Eastern Finland. They’ve been working on how to teach about AI for years and were frustrated with existing educational platforms: many of these are either too complex, raise privacy concerns, or rely on coding skills that not all students (or teachers) have. In response, they created an award-winning, classroom-friendly tool designed to make AI technology more transparent and more hands-on.
Matti and Henriikka began by discussing the unique challenges of teaching young learners about AI technology. Many students start with “folk theories”, for example, thinking that computers understand language like humans do. These misconceptions can be surprisingly hard to shake.
They also pointed out broader issues:
To address these challenges, Matti and Henriikka have spent three years co-designing tools and approaches with local schools, teachers, and over 200 students. Their approach is grounded in educational theory and a set of core AI learning principles:
Frustrated by existing platforms that require programming skills, raise privacy concerns, or don’t allow collaboration, Matti and Henriikka’s team developed GenAI Teachable Machine: a no-code, browser-based tool designed to make key AI concepts tangible. In the central research study, the team used the tool with Finnish students in grade 4–7 (10 to 14 years of age). It’s a great introductory tool that could also be used with younger and older students.
Matti demonstrated how the tool addresses their core AI learning principles with a simple, creative project. Using hand puppets, he trained a model to recognise four distinct classes: a background, a bunny, a calm wolf, and an angry wolf. For each class, he assigned a specific action: a sound, an image, or both. This hands-on process gives students a direct line of sight from the data they might create (visuals of hand puppets) to the final behaviour of the model (outputted sounds and images). They learn about classifiers, training, and confidence levels not as abstract definitions, but as creative tools they can control.
But the learning really starts when things don’t work as expected. Students can easily open their models on their phones with an automatically created QR code and then move around the classroom to test their models. At this point, they quickly notice how fragile AI technology can be. For example, in the case of a simple recognition model trained on specific colours or features, with a change of lighting or a different shirt, the model might fail. These “failures” turn into powerful lessons. For instance, a face-recognition app trained mostly on students with blonde hair might not work well for someone with brown hair — sparking immediate conversations about bias. As Matti put it, students very quickly start asking deep questions.
GenAI Teachable Machine also allows students to apply their AI models to the physical world. By connecting to a simple, low-cost robotics kit, students can use their models to control motors, lights, and other actuators. This step into the physical world teaches fundamental concepts that are difficult to grasp in a purely virtual environment. Students learn about causality, as their model’s classifications trigger real, physical actions. They also learn about the need for a world model — an understanding of how the physical world works — and see that they must take responsibility for what happens when their abstract models have real-world consequences.
By combining a no-code platform with practical, well-designed learning experiences, Matti, Henriikka, and their team are showing what AI education can look like: hands-on, accessible, and grounded in real understanding. Their work is helping students see inside the box, and giving them the tools to think critically about the AI technologies that are becoming part of their everyday lives.
If you’re an educator interested in including the topic of AI in your teaching, you can try GenAI Teachable Machine on its official website.
You may also be interested in our own Experience AI resources, which are designed to help you and your learners navigate the fast-moving world of AI and machine learning technologies.
In September, Matti and his team are returning to discuss other ways to teach young people about AI technologies.
To sign up and take part in the Matti’s seminar on Tues 9 Sept at 17:00–18:30 BST, 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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Today’s blog is the second in a mini-series of three sharing our experiences of adapting computing curriculum resources for different contexts, and of training teachers to use them in schools. Last month we wrote about our collaboration with partners in Kenya. Here we discuss our work in Odisha, India.
This article has been written by Fiona Coventry, Impact Manager, and Mamta Manaktala, Senior Learning Manager.
We know that building long-term partnerships with organisations that have local expertise is key to making a real impact for young people. This fact was echoed by people involved in education initiatives worldwide who spoke at the What Works Hub for Global Education 2024 annual conference, which Fiona followed online. Our work in Odisha is an example of this.
We have now been working with our government partner in Odisha, Panchasakha Shikhya Setu (formerly Mo School Abhiyan), for four years. Our journey began in 2021, when we worked together to establish a network of Code Clubs in government and government-aided schools in the state. In 2023, our focus shifted to developing a formal computing curriculum for students in grades 9 and 10 (known locally as the Kaushali curriculum), in collaboration with two other partners.
Adaptation is a crucial aspect of how we ensure our computing resources are accessible to as many young people as possible. For our work in Odisha, we adapted content from The Computing Curriculum and then localised it to fit the requirement of the students.
In Odisha’s June 2024 to April 2025 academic year, we rolled out adapted computing curriculum content for grade 10 students, for students who had already learned with adapted grade 9 content in 2023/24. We worked with our partners to develop the curriculum content and trained 310 master teachers from across Odisha, along with 30 State Resource Groups (SRGs) to support them. Before the end of 2024, the 310 master teachers subsequently trained 8109 teachers, who would reach an estimated 880,000 students with the grade 9 and 10 curriculum content. We had an ongoing responsibility to support 1846 of these teachers in our allocated districts, with an estimated reach to around 205,000 students.
In early 2025 we issued a follow-up survey about student learning, content, and training to a sample of teachers in our allocated districts, and 310 teachers responded. (We used a stratified sampling approach designed to ensure the survey results were representative of all teachers.)
At least 87% of teachers agreed that students achieved the outcomes we asked about, e.g. regarding coding skills, staying safe online, and use of data in machine intelligence.
Moreover, responses related to our grade 9 curriculum remained similarly high compared to 2024 survey responses.
Teachers also expressed their appreciation for the computing curriculum resources and training in free-text comments and interviews, for example:
“IT and coding is essential nowadays. So a good initiative, adding this to schools’ curriculum.” – Teacher in Odisha
“The training was quite informative, interesting and helpful.” – Teacher in Odisha
“It is very useful training for me. It boosts my knowledge and helps me for classroom transaction.” – Teacher in Odisha
An ongoing challenge in Odisha has been supporting those teachers who lack experience with computing and/or with our recommended teaching approaches for computing. We have been working hard to help these teachers develop the knowledge, skills, and confidence to effectively deliver the curriculum content in the limited time they have alongside their other professional commitments.
In the 2023/2024 academic year, many teachers had told us they needed further training and support. For this reason, we offered longer training in the 2024/25 academic year. We also adapted our training approach based on learning from earlier phases, such as including activities teachers could complete on their smartphones, enabling more hands-on learning while reducing dependence on available IT equipment. The outcome of this was positive: in the follow-up survey, fewer teachers felt they needed additional training to deliver the lessons, and most teachers we interviewed felt this year’s training was an improvement on the previous year’s.
Our team also ran weekly webinars to support teachers and address their queries. These were very well received by teachers. Of the responses received to feedback form available after each webinar:
This was supported by comments from teachers, for example:
“All questions were answered. The webinar was good. Gained a lot. Thank you very much.” – Teacher in Odisha
“I learned many unknown things about Scratch, it will help my classroom teaching.” – Teacher in Odisha
In this year’s follow-up survey, teachers also less frequently indicated they felt they needed “additional content to support students”. They provided useful feedback and suggestions regarding the curriculum content, e.g. further simplifying and localising it, which we will incorporate into future resource development.
Another persistent challenge has been limited access to IT equipment and the internet in schools, and what this means for student-device ratios and how teachers are able to deliver the content. For future resources we are developing, we are therefore adapting the amount of content to be delivered over a series of lessons.
In 2025, we are working with the same partners to implement a curriculum for grades 6 to 8, initially in around 460 schools. We and our partners have developed the curriculum content and are currently in the process of training teachers in preparation for classroom delivery.
We are also continuing to support the teachers previously trained on the grade 9 and 10 curriculum through webinars and school visits.
You can access our free Computing Curriculum resources on our website — we are currently working to make the materials for India, and for Kenya, downloadable there.
Look out for the final blog in this mini-series next month, which will focus on our computer science curriculum in Telangana, India.
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At this time of year, many educators are considering ways to update their content ahead of the new school term. Whether you’re a teaching assistant or head of department, it’s important to ensure that the content you’re updating — or even designing anew — is relevant and high quality, and meet learners’ needs. In today’s blog we’re highlighting ‘ABC learning design’, and how it can be used to rapidly design and improve learning experiences.
We also share our new ABC-focused Pedagogy Quick Read, which you can download for free to:
ABC learning design is a rapid, hands-on approach to design and develop blended learning experiences. The framework has traditionally been used at undergrad level, and had a lot of success when used in response to the global pandemic in 2020, when learning experiences had to quickly transition from being delivered in-person to being accessible remotely.
The model is centred around six learning types:
Before continuing, it’s important to distinguish between ‘learning types’ and the widely discredited concept of ‘learning styles’. Whilst learning styles refer to fixed characteristics or preferences of learners, learning types refer to different kinds of learning activities and pedagogical approaches that can be designed into a course.
These learning types are representative simplifications of pre-existing learning theories. For each learning type, educators can use different activities to deliver that type of learning. The activities will depend on your context and what’s right and applicable for your students.
How can I apply ABC learning design?
ABC learning design is often done in a team-based workshop setting (you can do it by yourself as well). Firstly, you analyse your existing content. Consider the goal of your current learning sequence, and assess how your learners are going to reach that goal with the different learning types.
By analysing existing content and activities, you can then identify what’s missing from your sequence. This allows you to build on existing gaps and consider different types of activities you could implement. You then create a set of learning cards, which help you to storyboard and plan your new learning sequence.
Learning cards are typically postcard-sized and colour-coded to one of the six learning types. Colour coding helps you to tell the cards apart, and to easily see which learning types are or aren’t included in your sequence.
Each card has the name and a short description of the learning type on the front, with examples of associated digital or in-person learning activities on the back. The learning cards:
ABC design can be contextualised to your classroom, practices, and school, and to the technologies you have available. For example, on the back of each learning card you could include a set of activities that have been tried and tested in, or approved by, your school. Alternatively, you could link to other frameworks or teaching approaches that work for you and your students.
Learning cards can also be used to collect other insights about teaching and learning within your context, and used as reminders of pedagogies to implement, as well as practical concerns. They can also help you to consider if there are opportunities for cross-curricular links within your learning sequence.
In a computing department, ABC learning cards can become a shared resource that give fellow educators an understanding of what’s possible. The cards can be used again and again to help plan future learning experiences.
By running an ABC workshop and creating these learning cards, you and your team will put together a contextualised learning sequence toolkit specific to your school and learners.
In our blog How to build young people’s agency through accessible learning, we explored the universal design for learning (UDL) framework. UDL aims to support educators to reduce barriers for learners. It helps educators to create learning environments that are accessible and effective for all learners by providing multiple means of engagement, representation, and action and expression.
Gormley et al. (2022) described an initiative to integrate UDL within ABC learning design. They developed adapted ABC learning cards where the reverse side included specific UDL prompts, in addition to the usual example activities. For example:
By including these UDL considerations directly on the ABC cards, the design team ensured that accessibility and inclusivity were central to learning design conversations.
Adding UDL prompts to your learning cards is a fantastic way to help you design accessible learning sequences.
In 2020, during the pandemic, the Computing at School (CAS) Research Working Group worked with classroom teachers to apply ABC learning design in their own contexts.
Following some training, teachers analysed their existing classroom activities and then developed a range of suitable alternatives for remote learning, categorising them into low-tech, mid-tech, and high-tech options.
The different activity options were then added to their own sets of ABC learning cards and used to help adapt lessons for remote teaching. You can read more about the project and view example cards on the CAS website.
Potential benefits for educators:
Potential benefits for students:
If you’d like to find out more about ABC learning design, you can download our Quick Read for free.
You can also listen to a thought-provoking discussion on the topic between James Robinson, Carrie Anne Philbin, Jane Waite, and Matthew Wimpenny-Smith in season 1, episode 6 of the Hello World podcast: Could curriculum design be as simple as ABC?
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The Hello World podcast is back, accompanying the latest issue of Hello World magazine. This new three-part miniseries explores some of the topics from issue 27 of Hello World, which focuses on integrating computing education across the curriculum.
Whether you’re a seasoned educator or just starting your journey with computing education, this podcast audio and video series is full of insights, inspiration, and practical tips from educators and experts around the world.
If you’re already subscribed, the new episodes will appear automatically in your favourite podcast app every Tuesday.
In our first episode, Raspberry Pi Foundation CEO Philip Colligan, CBE, sits down with teacher Janine Kirk to discuss why, in the age of AI, it’s more important than ever for young people to learn to code. Their conversation draws on ideas from our downloadable position paper, which is also featured in issue 27 of Hello World magazine.
Next week, we’ll bring you the buzz from the Computer Science Teachers Association’s annual conference in Cleveland, USA. We’re speaking with educators at the conference to hear how they’re integrating computer science across subjects, and you’ll hear their top classroom tips for teaching CS in context.
The miniseries wraps up with an in-depth discussion about AI education around the world. Hosted by Ben Garside, Senior Learning Manager for Experience AI, the conversation features Leonida Soi, Learning Manager in Kenya; Monika Katkute-Gelzine, CEO of Vedliai in Lithuania; and Aimy Lee, COO of Penang Science Cluster in Malaysia. Monika and Aimy work with us in our global Experience AI partner programme.
Each of these three podcast episode builds on the themes in the latest Hello World issue, where you’ll find inspiration and practical tips from educators who are integrating CS across a variety of subjects and for all school ages.
Subscribe to the Hello World podcast wherever you get your podcasts to never miss an episode, and to help us reach more teachers. If you’re subscribe to Hello World magazine (it’s free), we’ll also let you know when new podcast episodes are available.
And, don’t forget to share this new podcast series with your fellow educators.
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We think computing or computer science (CS) needs to be accessible to all learners, and we know that teachers work hard towards this. Traditional CS approaches can lack flexibility, creating barriers to learning and excluding some young people. In today’s blog, we’re highlighting the ‘Universal design for learning’ (UDL) framework and how you can use it to make computing education more accessible to all your learners.
We also share our new UDL-focused Pedagogy Quick Read, which you can download for free to:
Everyone is different and has their own way of learning. What works for one young person may not work for the next. So why should we expect learners to be taught the same material in the same way?
Todd Rose, a contributor to the UDL framework, highlights the factors involved with a young person’s ability to engage and participate in learning. These include cognitive, social-emotional, family background and academic factors. He dispels the idea of an “average” learner, and instead suggests the concept of learner variability.
As educators, it’s important to consider that students will likely be at different stages of understanding, and a one-size-fits-all approach isn’t suitable. The UDL framework avoids this mindset and provides teachers with structured guidelines to design accessible lessons from the beginning.
The UDL framework encourages educators to provide flexibility for learners in three areas:
Two things are key while you are planning how to apply the UDL framework with your learners:
Multiple means of engagement — show learners different reasons for engaging in programming. For example:
Multiple means of representation — teach programming concepts in multiple ways. For example:
Multiple means of action and expression — teach with accessibility in mind. For example:
The UDL framework aligns closely with several key research-supported pedagogies that you can use for effective instruction in computational thinking and programming. For example, the pedagogy approach ‘Use-Modify-Create’ (UMC) can be paired with the UDL categories. The new Quick Read explores these connections in more detail.
Potential benefits for teachers:
Potential benefits for learners:
Our new Quick Read shares tips on how to best use the framework in your teaching.
We know there is a lack of representation within the field of CS. Our recent position paper ‘Why kids still need to learn to code in the age of AI’ and an episode of the Hello World podcast, ‘How can we empower girls in computing’ touched on this. Both highlight why it’s important that learners from all backgrounds are empowered to contribute their perspectives and experiences and shape the future with computing.
“The reality is that access to the opportunities to learn about computer science, programming, and coding has remained deeply unequal, both within and between countries. That has helped create a technology sector that doesn’t reflect the broad diversity of human backgrounds, perspectives, and experiences. And we are all living with the consequences.” – Philip Colligan, Mark Griffiths, Veronica Cucuiat
“If we don’t have a diverse range of people designing and implementing that tech, then we are going to come across issues.” – Becky Patel, Tech She Can, Hello World podcast”
By embracing the principles of ‘Universal design for learning’ and similar approaches, we can create a more inclusive and equitable learning environment in computer science for everyone.
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While in some countries, such as England, computing is taught as a standalone subject, in others, like the USA, computing concepts are integrated across the school curriculum. In our brand-new issue of Hello World, out today for free, educators share ways to integrate computer science into your classroom.
The argument for making computing and computer science (CS) standalone has often been about quality. We’ve heard educators say that teaching CS as part of other subjects can be hard, especially if you don’t have a CS background. On the other hand, integrating computer science into other subjects can offer a more accessible entry point for young people, broadening participation in CS education. And the critical thinking and problem-solving skills young people gain through computer science can enhance their learning of any subject.
As digital technology increasingly shapes our world, it may be that thoughtful cross-curricular CS education is the most effective way to empower all young people to become confident and critical technology users.
Issue 27 of Hello World features a range of practical articles with ideas for integrating CS over a variety of subjects at the primary, elementary, and high-school levels.
For example:
Also in this issue:
And much, much more.
Jake Baskin, Executive Director of the Computer Science Teachers Association, says in this issue of Hello World: “If you’re a teacher who is implementing CS principles in your classroom, you are a computer science teacher.”
Whether CS is your specialist subject or not, Hello World is full of ideas from your fellow educators on how to inspire your students.
The Hello World podcast is also back, with a miniseries in audio and video focused on integrated CS. If you’re subscribed via a podcast app, the three new episodes will show up in your feed on Tuesdays. Or you can already listen to the new episodes today via the exclusive links inside issue 27.
We hope you enjoy this issue of Hello World. Please get in touch with your article ideas or what you would like to see in the magazine.
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Experience CS is a brand-new, free, integrated computer science curriculum for elementary and middle school educators and anyone working with students aged 8 to 14. A key design principle for Experience CS is that any educator can use it. You don’t need a computer science qualification or previous experience in teaching computer science classes to deliver engaging and creative learning experiences for your students. That’s why, as US Executive Director, I’m especially pleased to announce the launch of the first six units in the curriculum today.
Read on to explore the new learning materials available and how you can start using them in your school.
Experience CS enables educators to teach computer science through a curriculum that integrates CS concepts and knowledge into core subjects such as math, science, and social studies. Ashly Tritch, computer science immersion specialist at Olson Middle School in Bloomington, MN, USA, said, “Cross-curricular computer science is important because it shows students how coding and tech skills can be used in other subjects like math, science, or even art. It helps make learning more interesting and helps kids understand how computer science connects to real life. The lessons that the Raspberry Pi Foundation is creating will be super engaging, with fun and creative activities that keep students curious and excited to learn.”
Six integrated computer science units are available to access, with more on the way. The units have been released in beta, and we would love to hear your feedback as we continue to make updates to the lesson materials. Each of the units includes an overview with a summary of the topics covered and a series of six to eight lessons, including lesson plans, slide decks, student-facing materials, and starter projects within our Code Editor for Education.
We have designed the units to be cross-curricular, so students can learn about computer science concepts while deepening their understanding of related subject area content. For example, in “The me project,” grade 4 students (ages 9–10) explore the basics of Scratch, personalise sprites, and develop programs to create an animation that tells a story all about them. The project could be integrated into language arts lessons, enabling young learners to explore visual representation and write their own unique stories. In the “Smart communities” unit, students in grade 6 (ages 11–12) explore ways in which computing and technology can be used to create environments that are responsive to the needs of community members; this could be included within science or technology lessons.
Initially, the curriculum and resources have been mapped to national and local standards in the US and Canada, including the K–12 Computer Science Teachers Association Standards for Students, but they are available for teachers and students anywhere in the world to use.
You can register for a free Raspberry Pi Foundation account to start downloading the learning materials, including lesson plans, slide decks, student activity sheets and assessment criteria.
Experience CS has been built from the ground up to support safe, confident computing lessons in real classrooms. It includes self-directed creative projects using the popular programming language Scratch. We have built a version of Scratch that is especially for schools. That means it doesn’t have the community and sharing features that are central to the full Scratch platform. Instead, everything runs in a closed, classroom-ready environment that supports safeguarding policies and fits with school filtering systems. Simple and intuitive learning management features enable teachers to create accounts, set assignments, and review progress.
On the “Getting Started” page, teachers will find everything they need, including helpful videos and tutorials. The next webinar takes place on 16th July, where we will walk you through all six units available at launch and show you how easy it is to get started with the learning materials. Whether you’re a CS teacher, general education teacher, administrator, or someone who works with school-aged young people, this session will give you the practical tools and guidance you need to bring Experience CS to life in your classroom or program.
No matter your experience or skill level, the Experience CS content has been designed to be easy to use. However, we also provide professional development (PD) opportunities to help build confidence in teaching computer science.
Teachers anywhere in the world can access free online courses offering flexible, self-paced learning to help you confidently teach block-based programming with effective, inclusive computing pedagogy. Our new course will develop your understanding of semantic waves while highlighting research-backed activities and examples directly from Experience CS units.
Experience CS is supported by Google and builds on the fantastic work they have done to support educators and students through CS First. The team behind Experience CS includes educators with significant experience in teaching CS in elementary and middle school settings, and it is based on extensive classroom testing and research. We will continue to develop and improve the curriculum and resources in response to feedback from teachers and students. If you would like to help shape the future of Experience CS by testing new features and providing valuable feedback to improve the programme, sign up for the mailing list.
We can’t wait for you to explore Experience CS. We will continue to release more curriculum units as well as make the materials available in French and Spanish. Get a head start ready for the next school year by registering for a free Raspberry Pi Foundation account, which will allow you immediate access to all the lesson materials, and then create your school account to begin creating classes, add a scratch project to a class, manage student accounts and view student work.
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Young people everywhere deserve a high-quality computing education. But what a high-quality computing education looks like differs depending on a learner’s culture, context, and the existing provision in the country they live in. Therefore, adapting our educational resources for a range of contexts is a key part of our work at the Raspberry Pi Foundation, for example when we collaborate with partners to localise our Computing Curriculum resources.
In this blog post, we share our experiences of adapting curriculum resources with our partners in Kenya, and the impact of this work. This is the first post in a mini-series of three — look out for the upcoming ones about our partnerships in the Indian states of Odisha and Telangana.
Last year, we embarked on partnerships in two areas of Kenya and aimed to roll out computing curriculum resources to students in grades 4 to 9 in Kenyan schools:
During the 2024 academic year, we trained 39 local community trainers, who subsequently trained 453 teachers. We also adapted The Computing Curriculum materials to develop resources — lesson plans, presentation slides, and supporting activities — that are relevant and engaging in the schools our partners work with.
We estimate that around 55,000 students were reached by our Kenya computing curriculum resources in 2024. Most teachers who had used our resources felt the lessons had improved their students’ knowledge and skills. Of those who responded to our follow-up survey:
This was supported by conversations with teachers and students. In student focus groups, students were able to list topics they had learned about and skills they had developed.
“….The lessons have had a significant positive impact on the students. They now demonstrate greater confidence in using technology, particularly with tasks involving programming in Scratch. This has improved their problem-solving skills and made them more engaged in learning.” – Teacher, Mombasa
“In my computing lessons, I’ve learned how to use a computer safely and properly. I learned how to type, use the mouse, and open programs. We also learned about coding, which is really fun because we can make things happen on the screen by giving the computer instructions. I’ve also learned how to create a simple document using software like Excel sheets. I really enjoy using the computer to solve problems and make things work.” – Learner, Arid and Semi Arid Lands
While teachers tended to agree that students’ skills and knowledge had increased, fewer felt that most students had achieved the specific learning objectives identified in the resources. This was often due to the content being only partially delivered, for example, due to limited availability of computing equipment in schools. However, many students lacked prior experience with the topics covered in the lessons, suggesting a large improvement in their skills compared to a low baseline.
Similarly, some training sessions were affected by challenges with the equipment, infrastructure, and learning environment available. Teachers were appreciative of the training and many have begun to deliver the computing lessons, but often lacked prior experience with computing and hence requested additional support.
In response to feedback from partners and teachers, we made some updates to our Computing Curriculum and training resources in preparation for the 2025 academic year. For example, we increased the alignment to Kenya’s national curriculum, prepared a more comprehensive teacher guide, and incorporated time for teachers to discuss solutions to common delivery challenges during training.
In 2025, we are working with partners to upskill even more teachers and broaden the reach of our computing lessons to a further four counties. Our partners have now begun upskilling both new and existing teachers on the updated resources, and we will continue to work with them to monitor and evaluate their programme’s success in the coming months.
You can access our free Computing Curriculum resources on our website — we are currently working to make the materials for Kenya, and for India, downloadable there.
Look out for the next blog in this mini-series in July!
The post Adapting our computing curriculum resources for Kenya — the journey so far appeared first on Raspberry Pi Foundation.
Are you a teacher who is interested in data science education for key stage 5 (age 16 to 18)? Then we invite you to join our free, in-person workshop exploring the topic, taking place in Cambridge, UK on 10 July 2025.
You will be among the very first educators to see some of our first test activities for teacher training to build data science concepts, and your contributions will feed into our future work. Sign up by 20 June to take part.
Current artificial intelligence (AI) methods, especially machine learning (ML), rely heavily on data. While young people learn mathematics, and some statistics, at school, data science concepts are not commonly taught.
To complement our work on AI literacy, we have been investigating what data science teaching resources and education research are currently available.
Our goals for this work are:
If you are interested in data science education for young people, and maybe even have experience of teaching it to learners aged 16 to 18 in your school (in any subject, including computer science, social sciences, mathematics, statistics, and ethics), please join our free workshop on Thursday 10 July in our office in Cambridge. We are able to reimburse some travel expenses.
At the workshop:
You’ll be the first group of working teachers we will share these activities with — your feedback will be invaluable, and you’ll have the chance to shape our work going forward.
If you are interested, please fill in this form by Friday 20 June:
You will then receive more information from us by 27 June. Spaces in the workshop are limited, so please do not book any travel until we confirm your space.
We’re looking forward to shaping the future of data science education with you.
PS In our current seminar series, researchers from around the world are presenting their latest work on teaching about AI and data science. You can catch up on past sessions and sign up for upcoming ones on our website.
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Today we’re publishing a position paper setting out five arguments for why we think that kids still need to learn to code in the age of artificial intelligence.
Just like every wave of technological innovation that has come before, the advances in artificial intelligence (AI) are raising profound questions about the future of human work. History teaches us that technology has the potential to both automate and augment human effort, destroying some jobs and creating new ones. The only thing we know for sure is that it is impossible to predict the precise nature and pace of the changes that are coming.
One of the fastest-moving applications of generative AI technologies are the systems that can generate code. What started as the coding equivalent of autocomplete has quickly progressed to tools that can generate increasingly complex code from natural language prompts.
This has given birth to the notion of “vibe-coding” and led some commentators to predict the end of the software development industry as we know it. It shouldn’t be a surprise then that there is a vigorous debate about whether kids still need to learn to code.
In the position paper we put forward five arguments for why we think the answer is an unequivocal yes.
First, we argue that even in a world where AI can generate code, we need skilled human programmers who can think critically, solve problems, and make ethical decisions. The large language models that underpin these tools are probabilistic systems designed to provide statistically acceptable outputs and, as any skilled software engineer will tell you, simply writing more code faster isn’t necessarily a good thing.
Learning to code is the most effective way we know for a young person to develop the mental models and fluency to become a skilled human programmer. The hard cognitive work of reading, modifying, writing, explaining, and testing code is precisely how young people develop a deep understanding of programming and computational thinking.
While there’s no doubt that AI is going to reshape the labour market, the evidence from history suggests that it will increase the reach of programming and computational approaches across the economy and into new domains, creating demand for humans who are skilled programmers. We also argue that coding is no longer just for software engineers, it’s becoming a core skill that enables people to work effectively and think critically in a world shaped by intelligent machines. From healthcare to agriculture, we are already seeing demand for people who can combine programming with domain-specific skills and craft knowledge.
Alongside the arguments for coding as a route to opening up economic opportunities, we argue that coding and programming gives young people a way to express themselves, to learn, and to make sense of the world.
And perhaps most importantly, that learning to code is about power. Providing young people with a solid grounding in computational literacy, developed through coding, helps ensure that they have agency. Without it, they risk being manipulated by systems they don’t understand. As Rushkoff said: “Program, or be programmed”.
Finally, we argue that the power to create with technology is already concentrated in too small and homogenous a group of people. We need to open up the opportunity to learn to code to all young people because it will help us mobilise the full potential of human talent, will lead to more inclusive and effective digital solutions to the big global challenges we face, and will help ensure that everyone can share in the societal and economic benefits of technological progress.
We end the paper with a call to action for all of us working in education. We need to challenge the false narrative that AI is removing the need for kids to learn to code, and redouble our efforts to ensure that all young people are equipped to take advantage of the opportunities in a world where AI is ubiquitous.
You can read the full paper here:
The cartoon image for this blog was created using ChatGPT-4o, which was prompted to produce a “whimsical cartoon that expresses some of the key ideas in the position paper”. It took several iterations.
The post Why kids still need to learn to code in the age of AI appeared first on Raspberry Pi Foundation.
Experience CS is our new free curriculum that helps elementary and middle school educators (working with students aged 8 to 14) teach computer science with confidence through creative, cross-curricular lessons and projects. Designed for teachers, by teachers, Experience CS is built to be easy to use in classrooms, with everything you need integrated into one safe, school-friendly platform.
In this blog post, we will share more about the safety features of Experience CS, and the steps we’ve taken to make the platform a great fit for your school.
Experience CS supports young people to develop their understanding of computer science through engaging, interactive projects using the programming language Scratch. Scratch is a popular block-based language that helps young people get started with coding, and Experience CS includes a version of Scratch that we have built especially for schools. With our version, which is fully integrated into the Experience CS platform, students can explore coding in a teacher-managed, closed environment that aligns with schools’ safeguarding policies and gives you full control over what your students see and do.
Student safety and privacy are at the forefront in Experience CS, which means:
We have designed every part of the Experience CS platform with school environments in mind, making it easier for teachers to manage and for students to use.
Here’s how:
Experience CS gives you the tools and peace of mind to deliver creative, engaging computer science lessons and activities in a way that works for your school. You will be able to effortlessly manage students’ work, with everything you and your students need provided within a simple, intuitive interface.
Experience CS is launching soon, and we can’t wait to see what you and your students create with it.
If you would like early access, want to stay up to date, or are interested in trying Experience CS out in your classroom, sign up for updates and we’ll keep you in the loop.
We’re also planning ahead: in the coming months, we’ll make our version of Scratch available to all schools and clubs via our Code Editor. That means whether or not you use the Experience CS curriculum, you’ll be able to run safe, creative coding sessions using Scratch in a school-friendly environment.
The post Experience CS: A safe, creative way to teach computing appeared first on Raspberry Pi Foundation.
’Digital literacy’ is a term that seems to pop up everywhere. In the early 2000s, it was the next big thing; some even suggested it might replace traditional literacy and numeracy. But, like many educational trends, it soon faded from the spotlight, and became something that schools ‘should’ do, or something left to the lone teacher who had been handed the role of IT coordinator.
For many teachers, at least in the UK, digital literacy meant booking a set of laptops (and hoping the last class had remembered to charge them) and ticking off history learning objectives by making a PowerPoint about Henry VIII’s wives. It became a bit of an afterthought.
More recently, digital literacy seems to have been rebranded as ‘digital skills’, often framed as the capabilities young people need for the workplace of tomorrow. But I don’t think that tells the full story.
Digital literacy isn’t just about employability; it’s about fairness and access. It’s about more than just learning to use spreadsheets (though my love for Excel remains strong); it’s about ensuring that all young people have the knowledge and confidence to navigate the digital world we live in today.
Digital literacy is about understanding the digital tools we rely on every day, securely accessing online services, making informed decisions about sharing personal information, and critically evaluating the endless stream of news and misinformation online.
It’s also about artificial intelligence: not just playing with the latest tools, but understanding how they work, the biases built into them, and the ways they shape our lives.
True digital literacy empowers young people to engage with technology thoughtfully, critically, and confidently. And that’s something worth making space for. To truly ensure that young people have fair access to the digitally enabled world we live in, we must equip them with the skills to understand and use technology effectively. This means making space for digital literacy within the curriculum and ensuring that all teachers feel confident in delivering it.
Every teacher has a role to play in helping students develop these essential skills. This requires high-quality curriculum resources that integrate digital tools meaningfully into different subjects, as well as comprehensive teacher training to ensure every educator feels empowered to teach digital literacy as part of their everyday practice.
So, let’s not treat digital literacy like that forgotten box of tangled charging cables in the staffroom (important, but nobody is quite sure what to do with it). Instead, let’s make it a core part of teaching, just like reading, writing, and knowing how to keep a straight face when a student asks if they really need to save their work.
If we get this right, we’re not just preparing young people for the jobs of tomorrow, we’re making sure they can navigate today’s digital world safely, confidently, and with the critical thinking skills to tell fact from fiction (because let’s face it, the internet isn’t exactly short on absolute nonsense).
Now, who’s up for making a PowerPoint about Henry VIII’s wives?
You can discover our free teacher training and classroom resources, and read about how we’ve integrated digital literacy in The Computing Curriculum.
A version of this article appears in the newest issue of Hello World magazine, which is all about digital literacy. Explore issue 26 and download your free PDF copy today.
You can also listen to our recent Hello World podcast episode exploring three teachers’ digital literacy tips for the classroom.
The post What do we even mean by digital literacy? appeared first on Raspberry Pi Foundation.
What does outstanding computing education look like in the age of AI? We’ve just released a new series of Pedagogy Quick Reads exploring this vital question. Focusing on three aspects of AI in computing education, these short guides offer practical insights and new strategies for your classroom practice.
Each Pedagogy Quick Read is designed to help educators explore, understand, and apply one area of research evidence.
You get:
This Quick Read explores how the concept of computational thinking is evolving, particularly in the context of AI. It offers guidance on how to teach computational thinking skills that are relevant to and enhanced by AI technologies.
“Without CT2.0, today’s learners will remain passive consumers rather than informed participants in a world increasingly shaped by data-driven AI technologies.”
As AI becomes widely used, it’s important to consider how students understand and view these technologies. This Quick Read discusses anthropomorphism (attributing human-like qualities to AI) and provides strategies for teaching about AI in a way that avoids common misconceptions.
“If young people see this technology as innately human-like, we run the risk of impacting their…sense of agency…safety…social connection…curiosity.”
Effective feedback is important for student learning, especially in a rapidly changing field like AI. This Quick Read examines how to develop “feedback literacy” in both educators and students, enabling them to give, receive, and use feedback more effectively.
“How do we ensure that all students get the most out of AI system-produced feedback? Feedback literacy is a theory-driven framework that can help…answer this question.”
The new AI-themed reads join our bank of other Pedagogy Quick Reads, which cover a wide range of topics related to computing education. You can find these resources and more on our pedagogy page, all organised around our 12 pedagogy principles for computing education.
As well as our Pedagogy Quick Reads, we also offer lots of other resources to support computing educators:
The post Pedagogy Quick Reads: turning abstract ideas into classroom practice appeared first on Raspberry Pi Foundation.
A key part of digital literacy is ensuring that all learners, regardless of their background or gender, have equal opportunities to develop computing skills and confidence in using technology.
However, the Department for Education in the UK recently released a report highlighting the growing gender gap in maths and science education. With International Girls in ICT Day just around the corner, it’s a timely reminder of the need to empower more girls and women within the computing sector.
In the latest episode of the Hello World podcast, Rachel Arthur, Chief Learning Officer at the Raspberry Pi Foundation, is joined by Dr. Jessica Hamer from King’s College London and Becky Patel from Tech She Can. Together, they explore the current state of girls’ engagement in computing and what we can do to support change.
This episode is the second in our three-part mini series on digital literacy. Each episode continues the conversations featured in the latest issue of the Hello World magazine, offering expert perspectives and practical examples from educators making a difference.
The gender gap in computing refers to the disparity between the proportion of female and male students who choose computing as a subject or career. This is problematic because:
To address these issues in detail, Rachel is joined by two guests who have dedicated years to understanding and addressing the gender gap.
Dr. Jessica Hamer is a Research Associate at King’s College London. She transitioned to academic research after working for a decade as a science teacher in secondary education. With a focus on projects like the SCARI project, she investigates factors affecting girls’ performance and participation in secondary-level computing. Her work includes a key report published last year on this topic.
In the podcast, Dr. Hamer reveals concerning findings about stereotypes in computing and how early they form. She explains that beliefs portraying girls as less interested in computing emerge as early as ages five or six, which significantly hinders girls’ engagement and involvement in the field.
Further, when students were asked to name famous figures in computing, most cited white, male tech entrepreneurs who are frequently in the news. This revealed a limited view of the kinds of people involved in computing, as well as a narrow conception of tech careers, with roles in areas like climate change and social justice being overlooked. However, Jessica highlights a key insight: “We found that girls were three times more likely to suggest a female famous face”, emphasising the vital importance of female representation and the need for relatable role models.
Becky Patel is the Head of Education and Learning at Tech She Can, a UK charity focused on increasing women’s representation in technology. She has a decade of teaching experience in both primary and secondary education and now develops the ‘Tech WE Can’ educational resources, which aim to inspire all children, particularly girls, to pursue careers in technology.
In the podcast, Becky emphasises the importance of early intervention to address gender stereotypes. She advocates for introducing children to a wide range of STEM careers, and to do this not just in computing lessons but across the whole curriculum.
Another key strategy Becky highlights is showing children that they can explore their hobbies and interests further through technology, even when at first glance it might seem that technology can’t play a role. She explains:
“There are so many tech roles which you can do working behind the scenes, training the athlete, helping them with health and physiotherapy, working on goal-line technology… There’s so many cool things and when you show children this, you see the attention. You see them sit up like, hold on, she’s talking about me and the thing I love. And she’s also talking about computing.”
To hear more about how to empower girls into computing, listen to or watch the full episode.
We hope this episode inspires you and helps you to engage your students more in computing. In the comments section below, we’d love to hear your thoughts, your feedback, and any of your own tips for empowering girls and young women.
Enjoy the conversation!
Next week, in the final podcast in our mini series, we’ll be looking at the notion of the ‘digital native’. Are young people truly tech-savvy, or are they simply reliant on digital tools without understanding the underlying mechanics?
James Robinson, Senior Learning Manager at the Raspberry Pi Foundation, will be joined by fellow colleague Sway Gratham and two guest speakers, Anna Lahtinen from Haaga-Helia University and Nicole O’Connor from Digi Know This, as they debate the complexities of digital skills, access, and education. Discover how the label ‘digital native’ can mask crucial issues and learn what real digital literacy looks like today.
You can watch, or listen, to each episode of our podcast on YouTube, or listen via your preferred audio streaming service, whether that’s Apple Podcasts, Spotify, or Amazon Music.
Don’t miss out on this discussion — subscribe to Hello World today to ensure you never miss a podcast episode or issue of the magazine.
The post How can we empower girls in computing? An important conversation on the Hello World podcast appeared first on Raspberry Pi Foundation.
We are thrilled to announce that the Raspberry Pi Foundation (RPF) has been accepted as a member of UNESCO’s Global Education Coalition (GEC).
Initiated during the COVID-19 pandemic, when 1.6 billion learners were shut out of the classroom, the GEC aimed to provide continuity of education in times of crisis. Since then, the Coalition has grown into a global multistakeholder network, and we are proud to help drive education transformation and accelerate the path to achieving UNESCO’s Sustainable Development Goal 4 (SDG 4 – Quality Education).
UNESCO’s vision to transform education for the world’s most underserved aligns with our mission at the Raspberry Pi Foundation. Being part of the Coalition enables us to work together to achieve this shared aim.
In addition to being part of the GEC, we have been invited to join the Digital Transformation Collaborative (DTC), a tech-focused subgroup that empowers educators and education leaders to include emerging technologies in their teaching practices and decision-making through capacity building and training.
We’re joining a coalition that has already achieved a lot. Having attended the GEC annual conference last week in Paris, Ms Stefania Giannini, UNESCO Assistant Director-General for Education, stated that to date the GTC has:
Source: https://www.unesco.org/en/global-education-coalition
Whilst these are amazing achievements to celebrate, there is still more work to do, with Ms Giannini also highlighting that there are currently 251 million children and youth out of school and that 44 million more teachers are needed for universal primary and secondary education by 2030.
Our commitment to the Coalition
The Digital Transformation Collaborative (DTC), which the Foundation has committed to support, aims to play a crucial role in shaping the future of education through technology. The group has established a framework structured around six core pillars:
Through our work at the Foundation, we believe we have the expertise to provide meaningful support through the sharing of our expertise across these issues. Many of these are challenges we work to overcome through the delivery of our programmes.
Joining UNESCO’s Global Education Coalition marks a significant milestone for the Raspberry Pi Foundation. Our mission to empower the underserved aligns with the Coalition’s goals. We are excited to contribute our expertise and resources to this global effort, driving forward the agenda for inclusive and equitable quality education for all.
I’m looking forward to writing more on our projects and initiatives within the GEC as we move forward and work together to transform global education.
The post Raspberry Pi Foundation joins UNESCO’s Global Education Coalition appeared first on Raspberry Pi Foundation.
We often believe we understand the meaning of ‘digital literacy’, but it can be a misleading term. Do we mean digital skills? Online safety? Where does AI fit in? As computer science education evolves to meet the needs of our increasingly digital world, we believe that true digital literacy empowers young people to engage with technology thoughtfully, critically, and confidently.
In this issue of Hello World, out today for free, we discuss what digital literacy means, how it is taught in different countries around the world, and how educators are rethinking digital literacy for their students and themselves.
As the use of digital technology grows, a broader view of digital literacy is necessary. Digital literacy is more than knowing how to use software. It’s the ability to use digital technologies effectively, safely, and responsibly.
In Issue 26 of Hello World, we explore this topic in detail and hear insights from educators across the world, including:
This issue also includes inspiring articles from the world of computer science education:
And there is lots more for you to discover in issue 26.
We’re also pleased to announce that the Hello World podcast has returned alongside the magazine with a miniseries also focused on digital literacy.
We asked for your thoughts on the podcast in our previous annual survey, and you kindly sent us lots of helpful feedback. Based on that, we’re trialling new episode formats, welcoming additional hosts, and bringing in more expert voices from around the world.
On Tuesday 15 April we’ll be releasing our first teacher tips episode, a shorter podcast with 3 teachers sharing practical, actionable tips for improving digital literacy in the classroom.
The week after, Tuesday 22 April, you’ll be able to hear a brilliant conversation between the Raspberry Pi Foundation’s Chief Learning Officer, Rachel Arthur, and two special guests: Dr Jessica Hamer from King’s College London’s School of Education, Communication and Society, and Becky Patel from Tech She Can. They’ll be discussing the current state of girls’ engagement in computing — a wide-reaching and important conversation exploring how we can empower more girls in computing through school, university and their careers.
Then on Tuesday 29 April the final episode in the miniseries will be a panel debate about “digital natives” where we’ll be asking, ‘Are young people who grew up with technology around them truly tech-savvy, or are they dependent on digital tools without understanding how they work?’ James Robinson — Senior Learning Manager here at the Raspberry Pi Foundation and regular host of the podcast — will lead this discussion as global educators debate the myth of the “digital native,” uncover how it could overlook complex issues of access, skills, and education, and consider what it really takes to be tech-smart in the modern world.
More information and links to listen can be found inside the magazine.
We hope you enjoy this issue of Hello World, and please get in touch with your article ideas or what you would like to see in the magazine.
Subscribe to Hello World for free to never miss an issue.
The post Hello World #26 out now: Digital Literacy appeared first on Raspberry Pi Foundation.
The Raspberry Pi Foundation has been working in India since 2018 to enable young people to realise their potential through the power of computing and digital technologies.
We’ve supported Code Clubs, partnered with government organisations, and designed and delivered a complete computing curriculum for students in grades 6 to 12 and at the undergraduate level. Our curriculum is tailored to the Indian context, and we provide extensive support to help teachers deliver it effectively.
In another recent blog, we shared in detail how we’ve created an impactful curriculum for India. We’re now excited to share our new report evaluating how our curriculum is being taught in Telangana and Odisha. This report demonstrates the impact we’ve had so far, highlighting our successes and the key lessons we have learnt.
Our evaluations of how the curriculum is being taught show that teachers are well-equipped to deliver the curriculum and provide high-quality and accessible learning experiences that develop students’ computing knowledge and skills.
In Telangana, we partnered with the Telangana Social Welfare Residential Educational Institutions Society (TGSWREIS) to introduce our curriculum at the Coding Academy School and Coding Academy College. Our report found that all school and college teachers we trained agreed they felt confident teaching students using the resources provided. Students were very positive about the classes, and their assessment scores demonstrated strong learning outcomes: 77% of school students and 70% of college students achieved at least 60% of available marks.
In Odisha, we worked with Learning Links Foundation and Quest Alliance, in partnership with Panchasakha Sikhya Setu (PSS) Abhiyan, to deliver the IT and Coding Curriculum (Kaushali) to students in grades 9 and 10. Our findings were also very positive:
The report also highlights how students better understood how computing and coding are used in the world and developed an increased interest in pursuing careers in these fields.
Our evaluations show the importance of several factors when launching a computing curriculum:
We are committed to always improving our approach to ensure that all young people in India have the opportunity to learn about computing.
You can read our new report here. If you are interested in partnering with us or want to learn more about our mission, please contact india@raspberrypi.org.
The post Empowering India’s digital future: Our computing curriculum’s impact appeared first on Raspberry Pi Foundation.
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