Raspberry Pi Foundation Blog

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.

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.

An amazing 25,707 participants had their code run on the International Space Station (ISS) this year, marking the European Astro Pi Challenge’s 10th anniversary in style. Yesterday, Astro Pi teams and their mentors received their official certificates and data — the final stage of this year’s challenge. On each certificate, participants can see the exact time and the location of the ISS when their program was run. Congratulations to every student, teacher, volunteer, and parent involved.

At the Raspberry Pi Foundation, we believe that alongside learning to code, a crucial part of computing education is building AI literacy skills. Amidst the rapid pace of development and the growing impact of AI tools, it is increasingly important for educators everywhere to feel equipped to address the topic of AI with their learners, to help young people understand their world, be responsible users of AI technologies, and prepare to become the future creators of these technologies.

Hayley McKechnie is a computing lead at a primary school in the East of England, who shares in this blog how she supports her students and her fellow teachers to build confidence around artificial intelligence (AI). She developed her skills through training delivered by Parent Zone, our partner for Experience AI in the UK.

Can AI support creativity? The technology is often framed as threatening creative work either by automating it or by encouraging imitation. But Professor Rebecca Fiebrink’s work in creative machine learning suggests a more useful way to think about this relationship. In our March research seminar, she showed how machine learning can help people work with meaningful data, communicate ideas through examples, and build new kinds of creative projects.

In April, young tech creators gathered at Explorium in Dublin for Coolest Projects Ireland 2026, a lively, creative celebration of young makers and their digital projects. This year’s event was held as part of Kinia’s Creative Technology Week, and welcomed over 100 young people, showcasing more than 80 incredible tech projects. Participants included young people from Code Clubs, CoderDojos, and schools across Ireland, as well as young creators taking part individually.

We are working with partner organisations around the world to support teachers in building confidence with AI in the classroom through our Experience AI programme. In this guest post, Catarina Marques from our partner TUMO Portugal shares what the organisation is learning from delivering training to educators. Whenever we run Experience AI training sessions, we keep coming back to the same thing: teachers are not lacking interest in AI, what they are lacking is time.

Imagine places for young people to not just learn to use technology but to understand it, shape it, and build with it. That’s what you can create by partnering with the Raspberry Pi Foundation on Code Club, the global movement of computing clubs where school-age young people develop the confidence to create with digital technologies. We’re looking for organisations worldwide to join us as new Incubator Partners and bring Code Club to young people in their regions.

Artificial intelligence (AI) systems are often presented as objective. But plenty of evidence shows that AI systems can reflect and reinforce existing inequalities, from healthcare and education to scientific research itself. In the first seminar of our new research seminar series on applied AI, Thema Monroe-White from George Mason University explored how we can better understand — and challenge — these patterns.