A new qualification in data science and AI for students in England?
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… The post A new qualification in data science and AI for students in England? appeared first on Raspberry Pi Foundation.
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.” <img fetchpriority="high" decoding="async" width="1300" height="731" src="https://www.raspberrypi.org/app/uploads/2025/11/Ada-CS-event.png" alt="Photo of a class of students at computers, in a computer science classroom." class="wp-image-91975" style="aspect-ratio:1.7794583773563921;width:660px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2025/11/Ada-CS-event.png 1300w, https://www.raspberrypi.org/app/uploads/2025/11/Ada-CS-event-300x169.png 300w, https://www.raspberrypi.org/app/uploads/2025/11/Ada-CS-event-500x281.png 500w, https://www.raspberrypi.org/app/uploads/2025/11/Ada-CS-event-768x432.png 768w, https://www.raspberrypi.org/app/uploads/2025/11/Ada-CS-event-800x450.png 800w, https://www.raspberrypi.org/app/uploads/2025/11/Ada-CS-event-450x253.png 450w, https://www.raspberrypi.org/app/uploads/2025/11/Ada-CS-event-900x506.png 900w" sizes="(max-width: 1300px) 100vw, 1300px" /> 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. Why AI education matters 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. <img decoding="async" width="1225" height="816" src="https://www.raspberrypi.org/app/uploads/2023/03/image4.png" alt="A teenager learning computer science." class="wp-image-83499" style="aspect-ratio:1.5012566322256353;width:664px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2023/03/image4.png 1225w, https://www.raspberrypi.org/app/uploads/2023/03/image4-300x200.png 300w, https://www.raspberrypi.org/app/uploads/2023/03/image4-500x333.png 500w, https://www.raspberrypi.org/app/uploads/2023/03/image4-768x512.png 768w, https://www.raspberrypi.org/app/uploads/2023/03/image4-800x533.png 800w, https://www.raspberrypi.org/app/uploads/2023/03/image4-450x300.png 450w, https://www.raspberrypi.org/app/uploads/2023/03/image4-900x600.png 900w" sizes="(max-width: 1225px) 100vw, 1225px" /> 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. Exploring a qualification: Opportunities and challenges 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. <img decoding="async" width="1920" height="1280" src="https://www.raspberrypi.org/app/uploads/2024/02/ICS_Discovery-day-Queen-Mary_6.7.22-22.jpg" alt="A group of young people in a lecture hall." class="wp-image-86380" style="width:688px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2024/02/ICS_Discovery-day-Queen-Mary_6.7.22-22.jpg 1920w, https://www.raspberrypi.org/app/uploads/2024/02/ICS_Discovery-day-Queen-Mary_6.7.22-22-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2024/02/ICS_Discovery-day-Queen-Mary_6.7.22-22-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2024/02/ICS_Discovery-day-Queen-Mary_6.7.22-22-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2024/02/ICS_Discovery-day-Queen-Mary_6.7.22-22-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2024/02/ICS_Discovery-day-Queen-Mary_6.7.22-22-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2024/02/ICS_Discovery-day-Queen-Mary_6.7.22-22-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2024/02/ICS_Discovery-day-Queen-Mary_6.7.22-22-900x600.jpg 900w" sizes="(max-width: 1920px) 100vw, 1920px" /> 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. What knowledge and skills should young people gain from the qualification? 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. How do we ensure the qualification is accessible and inclusive? 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. How do we support teachers to deliver the qualification? 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. What form should the qualification take? 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: An A level provides structure, national recognition, and clear progression into higher education or employment. An Extended Project Qualification (EPQ) may offer more flexibility, allowing students to explore AI through research or practical investigation without requiring schools to timetable a full qualification. 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? What we’ve learned so far 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: How do we make a qualification compelling – both for students who choose it and for schools that offer it? What delivery models will genuinely support teachers to succeed? <img loading="lazy" decoding="async" width="1920" height="1280" src="https://www.raspberrypi.org/app/uploads/2024/07/ICS_Discovery-day-Queen-Mary_6.7.22-16.jpg" alt="An undergraduate student is raising his hand up during a lecture at a university." class="wp-image-87822" style="width:680px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2024/07/ICS_Discovery-day-Queen-Mary_6.7.22-16.jpg 1920w, https://www.raspberrypi.org/app/uploads/2024/07/ICS_Discovery-day-Queen-Mary_6.7.22-16-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2024/07/ICS_Discovery-day-Queen-Mary_6.7.22-16-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2024/07/ICS_Discovery-day-Queen-Mary_6.7.22-16-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2024/07/ICS_Discovery-day-Queen-Mary_6.7.22-16-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2024/07/ICS_Discovery-day-Queen-Mary_6.7.22-16-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2024/07/ICS_Discovery-day-Queen-Mary_6.7.22-16-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2024/07/ICS_Discovery-day-Queen-Mary_6.7.22-16-900x600.jpg 900w" sizes="auto, (max-width: 1920px) 100vw, 1920px" /> 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. A global call for insights 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: Do you know of any successful qualifications or programmes for 16- to 18-year-olds that centre AI or data science? What lessons should countries learn from each other? 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. The post A new qualification in data science and AI for students in England? appeared first on Raspberry Pi Foundation.
How to strategically plan your computing curriculum
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… The post How to strategically plan your computing curriculum appeared first on Raspberry Pi Foundation.
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. <img loading="lazy" decoding="async" width="500" height="261" src="https://www.raspberrypi.org/app/uploads/2026/01/image4-1-500x261.png" alt="Image displaying the Learning Graphs Quick Read." class="wp-image-92390" style="width:661px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/image4-1-500x261.png 500w, https://www.raspberrypi.org/app/uploads/2026/01/image4-1-300x157.png 300w, https://www.raspberrypi.org/app/uploads/2026/01/image4-1-768x401.png 768w, https://www.raspberrypi.org/app/uploads/2026/01/image4-1-800x418.png 800w, https://www.raspberrypi.org/app/uploads/2026/01/image4-1-450x235.png 450w, https://www.raspberrypi.org/app/uploads/2026/01/image4-1-900x470.png 900w, https://www.raspberrypi.org/app/uploads/2026/01/image4-1.png 1200w" sizes="auto, (max-width: 500px) 100vw, 500px" /> 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: Find practical tips on how you can use learning graphs to design your curriculum Read a summary of the research behind them What is a learning graph? 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). <img loading="lazy" decoding="async" width="500" height="227" src="https://www.raspberrypi.org/app/uploads/2026/01/image2-2-500x227.png" alt="Image from the Learning Graphs Quick Read that showcases an example of a learning graph." class="wp-image-92391" style="width:650px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/image2-2-500x227.png 500w, https://www.raspberrypi.org/app/uploads/2026/01/image2-2-300x136.png 300w, https://www.raspberrypi.org/app/uploads/2026/01/image2-2-768x349.png 768w, https://www.raspberrypi.org/app/uploads/2026/01/image2-2-800x364.png 800w, https://www.raspberrypi.org/app/uploads/2026/01/image2-2-450x205.png 450w, https://www.raspberrypi.org/app/uploads/2026/01/image2-2-900x409.png 900w, https://www.raspberrypi.org/app/uploads/2026/01/image2-2.png 1530w" sizes="auto, (max-width: 500px) 100vw, 500px" /> 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: Visualise and map progression Identify curriculum gaps, so educators can shape and restructure learning experiences as necessary Ensure the use of consistent terminology Sequence learning and manage cognitive load How to create a learning graph Building a learning graph is an iterative process that helps you think critically about how different parts of your curriculum relate to each other. Nodes and links 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. <img loading="lazy" decoding="async" width="500" height="333" src="https://www.raspberrypi.org/app/uploads/2026/01/image3-500x333.jpg" alt="Educators sharing ideas around a table." class="wp-image-92392" style="width:617px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/image3-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/image3-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/01/image3-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/01/image3-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/01/image3-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/01/image3-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/01/image3-900x600.jpg 900w, https://www.raspberrypi.org/app/uploads/2026/01/image3.jpg 1999w" sizes="auto, (max-width: 500px) 100vw, 500px" /> 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. Collaboration and development 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. <img loading="lazy" decoding="async" width="1311" height="874" src="https://www.raspberrypi.org/app/uploads/2026/01/image1-1-edited-1.jpg" alt="Educators collaborating using post-it notes, planning currciulum." class="wp-image-92395" style="width:683px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/image1-1-edited-1.jpg 1311w, https://www.raspberrypi.org/app/uploads/2026/01/image1-1-edited-1-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/01/image1-1-edited-1-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/image1-1-edited-1-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/01/image1-1-edited-1-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/01/image1-1-edited-1-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/01/image1-1-edited-1-900x600.jpg 900w" sizes="auto, (max-width: 1311px) 100vw, 1311px" /> 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. Learning graphs in your teaching 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. Using learning graphs to support analysis 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. <img loading="lazy" decoding="async" width="500" height="333" src="https://www.raspberrypi.org/app/uploads/2026/01/image5-500x333.jpg" alt="Photo of an educator presenting at the front of a classroom of other teachers." class="wp-image-92396" srcset="https://www.raspberrypi.org/app/uploads/2026/01/image5-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/image5-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/01/image5-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/01/image5-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/01/image5-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/01/image5-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/01/image5-900x600.jpg 900w, https://www.raspberrypi.org/app/uploads/2026/01/image5.jpg 1999w" sizes="auto, (max-width: 500px) 100vw, 500px" /> 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. Find out more about learning graphs If you’d like to find out more about learning graphs, you can download our Pedagogy Quick Read for free. Download the learning graphs Pedagogy Quick Read 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. The post How to strategically plan your computing curriculum appeared first on Raspberry Pi Foundation.
Fostering Kenya’s computing education ecosystem
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… The post Fostering Kenya’s computing education ecosystem appeared first on Raspberry Pi Foundation.
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. <img loading="lazy" decoding="async" width="2000" height="1333" src="https://www.raspberrypi.org/app/uploads/2026/01/IMG_0742.jpg" alt="A speaker at the Raspberry Pi Foundation's Kenya Partner Showcase 2025, smiling and holding a microphone." class="wp-image-92360" style="aspect-ratio:1.500418643594753;width:756px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/IMG_0742.jpg 2000w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0742-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0742-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0742-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0742-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0742-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0742-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0742-900x600.jpg 900w" sizes="auto, (max-width: 2000px) 100vw, 2000px" /> 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. Partner-led sessions to reflect on what works 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. <img loading="lazy" decoding="async" width="800" height="534" src="https://www.raspberrypi.org/app/uploads/2026/01/Kenya_Partner_Showcase_2025-800x534.jpg" alt="Attendees have a conversation at the Raspberry Pi Foundation's Kenya Partner Showcase 2025." class="wp-image-92377" srcset="https://www.raspberrypi.org/app/uploads/2026/01/Kenya_Partner_Showcase_2025-800x534.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/01/Kenya_Partner_Showcase_2025-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/01/Kenya_Partner_Showcase_2025-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/Kenya_Partner_Showcase_2025-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/01/Kenya_Partner_Showcase_2025-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/01/Kenya_Partner_Showcase_2025-2048x1366.jpg 2048w, https://www.raspberrypi.org/app/uploads/2026/01/Kenya_Partner_Showcase_2025-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/01/Kenya_Partner_Showcase_2025-900x600.jpg 900w" sizes="auto, (max-width: 800px) 100vw, 800px" /> 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 <img loading="lazy" decoding="async" width="2000" height="1333" src="https://www.raspberrypi.org/app/uploads/2026/01/IMG_0822.jpg" alt="A speaker at the Raspberry Pi Foundation's Kenya Partner Showcase 2025, smiling and holding a microphone." class="wp-image-92361" style="aspect-ratio:1.500418643594753;width:734px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/IMG_0822.jpg 2000w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0822-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0822-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0822-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0822-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0822-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0822-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0822-900x600.jpg 900w" sizes="auto, (max-width: 2000px) 100vw, 2000px" /> 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. Solving shared problems, sharing real stories 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. <img loading="lazy" decoding="async" width="2000" height="1333" src="https://www.raspberrypi.org/app/uploads/2026/01/IMG_0720.jpg" alt="A speaker at the Raspberry Pi Foundation's Kenya Partner Showcase 2025, smiling and holding a microphone." class="wp-image-92356" style="aspect-ratio:1.500418643594753;width:700px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/IMG_0720.jpg 2000w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0720-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0720-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0720-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0720-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0720-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0720-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0720-900x600.jpg 900w" sizes="auto, (max-width: 2000px) 100vw, 2000px" /> 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. From isolation to coordination 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. <img loading="lazy" decoding="async" width="2000" height="1333" src="https://www.raspberrypi.org/app/uploads/2026/01/IMG_0908.jpg" alt="Two attendees of the Raspberry Pi Foundation's Kenya Partner Showcase 2025 smile at the camera." class="wp-image-92364" style="aspect-ratio:1.500418643594753;width:708px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/IMG_0908.jpg 2000w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0908-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0908-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0908-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0908-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0908-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0908-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0908-900x600.jpg 900w" sizes="auto, (max-width: 2000px) 100vw, 2000px" /> 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. Thank you to all partners 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. <img loading="lazy" decoding="async" width="2000" height="1333" src="https://www.raspberrypi.org/app/uploads/2026/01/IMG_0931.jpg" alt="Attendees mingle among tables and chairs at the Raspberry Pi Foundation's Kenya Partner Showcase 2025." class="wp-image-92367" style="aspect-ratio:1.500418643594753;width:753px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/IMG_0931.jpg 2000w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0931-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0931-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0931-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0931-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0931-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0931-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_0931-900x600.jpg 900w" sizes="auto, (max-width: 2000px) 100vw, 2000px" /> 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. The post Fostering Kenya’s computing education ecosystem appeared first on Raspberry Pi Foundation.
How to put data first in K–12 AI education by using data case studies
In Germany, as in many countries, AI topics are rapidly entering formal computer science education. Yet, this haste often risks us focusing on fleeting technological developments rather than fundamental concepts. As computer science educator Viktoriya Olari, from Free University of Berlin, discovered in her research, the fundamental role of data, which powers most modern AI… The post How to put data first in K–12 AI education by using data case studies appeared first on Raspberry Pi Foundation.
In Germany, as in many countries, AI topics are rapidly entering formal computer science education. Yet, this haste often risks us focusing on fleeting technological developments rather than fundamental concepts. As computer science educator Viktoriya Olari, from Free University of Berlin, discovered in her research, the fundamental role of data, which powers most modern AI systems, is critically underestimated in many existing frameworks. If students are to become responsible designers of such systems, they can’t afford to treat AI as an opaque box. Rather, they must first master the messy, human process that begins with the data itself. <img loading="lazy" decoding="async" width="1600" height="1067" src="https://www.raspberrypi.org/app/uploads/2026/01/image3.png" alt="Viktoriya Olari, from Free University of Berlin." class="wp-image-92325" style="aspect-ratio:1.4995397617918607;width:672px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/image3.png 1600w, https://www.raspberrypi.org/app/uploads/2026/01/image3-300x200.png 300w, https://www.raspberrypi.org/app/uploads/2026/01/image3-500x333.png 500w, https://www.raspberrypi.org/app/uploads/2026/01/image3-768x512.png 768w, https://www.raspberrypi.org/app/uploads/2026/01/image3-1536x1024.png 1536w, https://www.raspberrypi.org/app/uploads/2026/01/image3-800x534.png 800w, https://www.raspberrypi.org/app/uploads/2026/01/image3-450x300.png 450w, https://www.raspberrypi.org/app/uploads/2026/01/image3-900x600.png 900w" sizes="auto, (max-width: 1600px) 100vw, 1600px" /> Viktoriya Olari In our October research seminar, Viktoriya shared the results of her work over the last four years on how schools can shift the focus from the latest technologies to the underlying data. Her research offers a clear structure for what young people should learn about data and how teachers can make it work inside ordinary classrooms. Why begin with data? Viktoriya’s analysis of existing AI education frameworks found the data domain is underrepresented, with essentials such as data cleaning often not addressed at all. She argues that, because modern AI systems are data driven, students need both language and routines for working with data: being able to name concepts like training vs test data, data quality, and bias, and to explain practices such as collection, cleaning, and pre-processing. That’s the rationale for teaching data concepts and practices first, and then placing modelling inside an explicit, staged lifecycle. <img loading="lazy" decoding="async" width="1906" height="1070" src="https://www.raspberrypi.org/app/uploads/2026/01/image4.png" alt="Word clouds for two foundational components: data concepts and data practices." class="wp-image-92328" style="aspect-ratio:1.7813121272365806;width:696px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/image4.png 1906w, https://www.raspberrypi.org/app/uploads/2026/01/image4-300x168.png 300w, https://www.raspberrypi.org/app/uploads/2026/01/image4-500x281.png 500w, https://www.raspberrypi.org/app/uploads/2026/01/image4-768x431.png 768w, https://www.raspberrypi.org/app/uploads/2026/01/image4-1536x862.png 1536w, https://www.raspberrypi.org/app/uploads/2026/01/image4-800x449.png 800w, https://www.raspberrypi.org/app/uploads/2026/01/image4-450x253.png 450w, https://www.raspberrypi.org/app/uploads/2026/01/image4-900x505.png 900w" sizes="auto, (max-width: 1906px) 100vw, 1906px" /> A slide from Viktoriya’s presentation. Click to enlarge. Her talk presented this argument in the German school context, where AI topics are entering state curricula quickly. Her critique targets how existing frameworks fail to address data and how that gap undermines responsible evaluation and design. The proposed model centres data by pairing an eight-stage, data-driven lifecycle with a curated set of key concepts and practices, and by making “data-based judgment skills” a key outcome. Viktoriya’s work organises this understanding into two foundational components: data concepts (the vocabulary, e.g. training/test data, data quality, overfitting) and data practices (the actions, e.g. collect, clean, train, evaluate). A lifecycle for learning Viktoriya’s framework is built around an eight-stage data lifecycle, stretching from defining a task through gathering, preparing, modeling, evaluating, and finally sharing or archiving results. Inside that backbone she has identified two layers of learning targets: Data concepts – roughly a hundred ideas that give teachers and students a common language, from “training vs. test data” and “bias” to “features”, “labels”, and “provenance”. Data practices – 28 kinds of hands-on work (and 69 subpractices) that materialise those ideas: for instance collecting, cleaning, splitting datasets, checking quality, training and evaluating models, and handling privacy and deletion responsibly. More details are available in her work on data-related concepts and practice. <img loading="lazy" decoding="async" width="1914" height="1072" src="https://www.raspberrypi.org/app/uploads/2026/01/image1-2.png" alt="Viktoriya’s 8-stage process model of the data-driven lifecycle." class="wp-image-92331" style="aspect-ratio:1.785453337761541;width:760px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/image1-2.png 1914w, https://www.raspberrypi.org/app/uploads/2026/01/image1-2-300x168.png 300w, https://www.raspberrypi.org/app/uploads/2026/01/image1-2-500x280.png 500w, https://www.raspberrypi.org/app/uploads/2026/01/image1-2-768x430.png 768w, https://www.raspberrypi.org/app/uploads/2026/01/image1-2-1536x860.png 1536w, https://www.raspberrypi.org/app/uploads/2026/01/image1-2-800x448.png 800w, https://www.raspberrypi.org/app/uploads/2026/01/image1-2-450x252.png 450w, https://www.raspberrypi.org/app/uploads/2026/01/image1-2-900x504.png 900w" sizes="auto, (max-width: 1914px) 100vw, 1914px" /> A slide from Viktoriya’s presentation. Click to enlarge. Viktoriya’s 8-stage process model of the data-driven lifecycle. It serves as a guide for curriculum developers and teachers, outlining 28 key data-related practices and providing 69 examples of subpractices for use in K–12 computer science education. <img loading="lazy" decoding="async" width="1906" height="1074" src="https://www.raspberrypi.org/app/uploads/2026/01/image2-1.png" alt="A collection of 133 key data-related concepts." class="wp-image-92334" style="aspect-ratio:1.774726000264096;width:762px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/image2-1.png 1906w, https://www.raspberrypi.org/app/uploads/2026/01/image2-1-300x169.png 300w, https://www.raspberrypi.org/app/uploads/2026/01/image2-1-500x282.png 500w, https://www.raspberrypi.org/app/uploads/2026/01/image2-1-768x433.png 768w, https://www.raspberrypi.org/app/uploads/2026/01/image2-1-1536x866.png 1536w, https://www.raspberrypi.org/app/uploads/2026/01/image2-1-800x451.png 800w, https://www.raspberrypi.org/app/uploads/2026/01/image2-1-450x254.png 450w, https://www.raspberrypi.org/app/uploads/2026/01/image2-1-900x507.png 900w" sizes="auto, (max-width: 1906px) 100vw, 1906px" /> A slide from Viktoriya’s presentation. Click to enlarge. A collection of 133 key data-related concepts. These concepts are organised according to the eight stages of the data-driven lifecycle and provide the foundational vocabulary for teaching AI education. Making it teachable Viktoriya’s team set out to redesign the format so that real data work could happen within ordinary lessons. They ended up with three “Data Case Study” architectures, each using authentic datasets and domain questions. The materials are supported by Orange 3, an unplugged machine learning and data visualisations tool familiar to the teachers participating. Variants emerged across three design cycles to address specific challenges, but teachers choose among them based on learning objectives and class context. Bottom-up: Students create a workflow step by step (e.g. import, inspect, clean, transform, split, train, evaluate). This approach is excellent for procedural fluency, but teachers reported an over-emphasis on operating Orange and too little reflection on the lifecycle unless explicit reflection is added. Top-down: Students start from a prepared workflow, read plots, infer the role of each branch, identify issues in the data/practices, and justify changes. This architecture directly counters the reflection gap seen in bottom-up and leans into reasoning rather than routine. Puzzle-like: Using “widgets,” visualisations of data tables, that stand for parts of a data pipeline, students rebuild a valid flow collaboratively. This encourages discussion, works without devices, and makes thinking visible. <img loading="lazy" decoding="async" width="1904" height="1070" src="https://www.raspberrypi.org/app/uploads/2026/01/image5.png" alt="The school-specific data case study" class="wp-image-92335" style="aspect-ratio:1.7794842938002715;width:698px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/image5.png 1904w, https://www.raspberrypi.org/app/uploads/2026/01/image5-300x169.png 300w, https://www.raspberrypi.org/app/uploads/2026/01/image5-500x281.png 500w, https://www.raspberrypi.org/app/uploads/2026/01/image5-768x432.png 768w, https://www.raspberrypi.org/app/uploads/2026/01/image5-1536x863.png 1536w, https://www.raspberrypi.org/app/uploads/2026/01/image5-800x450.png 800w, https://www.raspberrypi.org/app/uploads/2026/01/image5-450x253.png 450w, https://www.raspberrypi.org/app/uploads/2026/01/image5-900x506.png 900w" sizes="auto, (max-width: 1904px) 100vw, 1904px" /> A slide from Viktoriya’s presentation. The data case study method uses real-world data and context to help students achieve three key learning outcomes: go through the data-driven lifecycle, reflect on data practices and concepts in a criteria-guided manner, and develop data-based problem-solving and judgment skills. What happened in the German classrooms Viktoriya’s team ran three design cycles with small groups in Germany, with students aged 14 to 15. Each cycle lasted around 48 hours of teaching. Because participating teachers already knew Orange 3, the emphasis was on pedagogy rather than software training. The projects drew on manageable real-world data: spreadsheets, time-series sets, a few geographical samples. Two examples are: Forecasting Berlin air quality – Students explored how data quality, feature choice, and evaluation metrics shape predictions, then argued which model best answered the civic question. Classifying Tasmanian abalone – A deceptively simple dataset that invites talk about imbalance, feature engineering, and what counts as “good enough” accuracy. Some groups experimented with collecting their own sensor data, a plan that occasionally failed when the hardware didn’t cooperate. However, even that became part of the lesson: reliability, risk, and missing data are real features of data science, not mistakes to hide. <img loading="lazy" decoding="async" width="1132" height="755" src="https://www.raspberrypi.org/app/uploads/2017/05/IMG_1058.jpg" alt="A computing classroom filled with learners" class="wp-image-32214" style="aspect-ratio:1.4993724724585134;width:738px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2017/05/IMG_1058.jpg 1132w, https://www.raspberrypi.org/app/uploads/2017/05/IMG_1058-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2017/05/IMG_1058-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2017/05/IMG_1058-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2017/05/IMG_1058-800x534.jpg 800w, https://www.raspberrypi.org/app/uploads/2017/05/IMG_1058-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2017/05/IMG_1058-900x600.jpg 900w" sizes="auto, (max-width: 1132px) 100vw, 1132px" /> Student work reflected the three architectures. In the bottom-up groups, guided builds produced complete workflows and concise reflections, while top-down groups submitted annotated screenshots and critiques, and the puzzle-based lessons ended with posters and verbal presentations. Across them all, assessment focused on reasoning: not whether the “right” model appeared, but whether students could explain the stage they were in and justify their choices. Teaching resources Everything Viktoriya described is open and classroom-ready (currently in German). The computingeducation.de/proj-datacases hub hosts teacher guides, student tasks, and sample Orange 3 files. The growing library of data cases covers topics from climate data to air quality analytics. Why it matters now In the UK, a curriculum review has been recently released and along with the Government’s response. Across Europe and beyond, education systems are racing to add AI content to their curricula. Tools will come and go, and benchmarks will keep moving. What endures is the capacity to reason about data: to know what stage of work you’re in, what evidence supports your decisions, and what trade-offs you’re making. That is why Viktoriya’s contribution is unique — it gives teachers a map, a shared vocabulary, and practical ways to make data visible and the focus of discussion in schools. You can read this blog to see how we’ve used Viktoriya’s framework in our work designing a data science curriculum for schools. Join our next seminar Join us at our seminar on Tuesday 27 January from 17:00 to 18:30 GMT to hear Salomey Afua Addo talk about how to teach about neural networks in Junior High Schools in Ghana. To sign up and take part, click the button below. We’ll then send you information about joining. I want to sign up for the next seminar We hope to see you there. This will be the final seminar in our series on teaching about AI and data science — the next series focuses on how to teach about applied AI across subjects and disciplines. You can view the schedule of our upcoming seminars, and catch up on past seminars on our previous seminars page. Teachers in England, take part in our new data science study WKS2 teachers, participate in our new study! We’re launching a new study to explore how to teach learners aged 9 to 11 about data-driven computing. The study will take place in collaboration with KS2 teachers (Y4/Y5/Y6) in England, Scotland and Wales and look at: What key ideas pupils need to understand How teachers currently approach topics related to data-driven computing How pupils make sense of data and probability Our goal is to find practical ways to help teachers build children’s confidence in working with data in computing lessons. The study will be collaborative, with two workshops held throughout 2026, and we’re inviting KS2 teachers (Y4/Y5/Y6) to take part. You can express your interest in participating by filling in this form: rpf.io/data-science-study-blog The post How to put data first in K–12 AI education by using data case studies appeared first on Raspberry Pi Foundation.
Coolest Projects 2026: Opens for entries in January!
Coolest Projects is our global technology showcase for young people up to age 18. It’s a place where young creators share the brilliant things they’ve made using digital technology — from first-ever Scratch projects and coding for fun experiments to ambitious robotics builds — with a global audience. Everyone who takes part receives certificates and… The post Coolest Projects 2026: Opens for entries in January! appeared first on Raspberry Pi Foundation.
Coolest Projects is our global technology showcase for young people up to age 18. It’s a place where young creators share the brilliant things they’ve made using digital technology — from first-ever Scratch projects and coding for fun experiments to ambitious robotics builds — with a global audience. Everyone who takes part receives certificates and rewards to celebrate their achievements. What you need to know about Coolest Projects 2026 Coolest Projects is open to any young person, anywhere in the world. Creators can submit their tech projects to our online showcase, explore the global project gallery, and join our special celebration livestream. There are in-person events in some countries for local creators, too (find out more below). <img loading="lazy" decoding="async" width="799" height="533" src="https://www.raspberrypi.org/app/uploads/2026/01/54576484212_4227041373_c.jpg" alt="Young learner at Coolest Projects UK" class="wp-image-92302" style="aspect-ratio:1.4990765442232712;width:771px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/54576484212_4227041373_c.jpg 799w, https://www.raspberrypi.org/app/uploads/2026/01/54576484212_4227041373_c-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/01/54576484212_4227041373_c-500x334.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/54576484212_4227041373_c-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/01/54576484212_4227041373_c-450x300.jpg 450w" sizes="auto, (max-width: 799px) 100vw, 799px" /> By taking part in Coolest Projects, young people can: Join an international community of digital makers Represent their country on a global stage Receive feedback on their creations Earn certificates to recognise their achievements Celebrate their progress at our livestream event on 24 June 2026 It is completely free to take part in Coolest Projects, and we welcome all kinds of digital technology projects — from first attempts to an ambitious STEM project or advanced build. Projects don’t need to be finished to be submitted. Creators can enter a project into one of seven categories: Scratch, Games, Hardware, Web, Mobile Apps, Advanced Programming, and the emergent category of AI. How Coolest Projects makes a difference The findings from the Coolest Projects 2025 Impact report offer clear, evidence-based insights into how Coolest Projects builds confidence, creativity, and a sense of belonging for young people around the world. In 2025, almost 12,000 young people from 41 countries took part in Coolest Projects online, a 57% increase on the previous year. This year, we’re excited to welcome even more creators to the Coolest Projects community. <img loading="lazy" decoding="async" width="799" height="533" src="https://www.raspberrypi.org/app/uploads/2026/01/54577531709_a53e2a634e_c.jpg" alt="Young learner with her project at Coolest Projects UK" class="wp-image-92306" style="aspect-ratio:1.4990765442232712;width:739px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/54577531709_a53e2a634e_c.jpg 799w, https://www.raspberrypi.org/app/uploads/2026/01/54577531709_a53e2a634e_c-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/01/54577531709_a53e2a634e_c-500x334.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/54577531709_a53e2a634e_c-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/01/54577531709_a53e2a634e_c-450x300.jpg 450w" sizes="auto, (max-width: 799px) 100vw, 799px" /> Our annual evaluation shows that Coolest Projects continues to have a powerful impact on young digital creators worldwide. In 2025: 100% of mentors and 72% of young people taking part in the online showcase said their confidence in digital making increased 83% of young people felt inspired to continue learning and participating in computing after taking part 74% of young creators and 100% of mentors said Coolest Projects helped them or their team feel a sense of belonging in computing 80% of surveyed young people said seeing projects from around the world made them feel part of a larger community of makers Creators told us how proud they felt sharing their work: “It feels like I’m being noticed!” – Young creator, UK “I enjoyed presenting my project and seeing so many creative ideas from others.” – Young creator, India With many early-stage makers joining for the first time, including 55% of young people at in-person events, Coolest Projects continues to be an accessible entry point into the world of digital creativity. <img loading="lazy" decoding="async" width="600" height="800" src="https://www.raspberrypi.org/app/uploads/2026/01/54804957735_acc5a69e3e_c.jpg" alt="Young learner at Coolest Projects Ireland" class="wp-image-92309" style="aspect-ratio:0.7496427483777263;width:398px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/54804957735_acc5a69e3e_c.jpg 600w, https://www.raspberrypi.org/app/uploads/2026/01/54804957735_acc5a69e3e_c-188x250.jpg 188w, https://www.raspberrypi.org/app/uploads/2026/01/54804957735_acc5a69e3e_c-500x667.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/54804957735_acc5a69e3e_c-450x600.jpg 450w" sizes="auto, (max-width: 600px) 100vw, 600px" /> We’re also proud to see growing participation from girls who code, who tell us they value the chance to share their ideas and be inspired by others. At in-person events, girls made up 63% of creators in India, 50% in the US, 39% in Ireland, and 37% in the UK. Find out more about the incredible creativity and collaboration from mentors and makers worldwide in our 2025 Impact report: rpf.io/cp-impact-2025 How to take part in Coolest Projects Come up with an idea — or choose something you’ve already made for school or a science fair project. Whether it started as a small classroom task or a personal coding challenge, any tech creation can be part of Coolest Projects. Our projects site offers hundreds of free, step-by-step guides — ideal for beginners, mentors, and anyone looking for engaging STEM activities for kids. The 2025 online gallery is also a great place to find inspiration for your next cool coding project. Select your category. Our category pages break down each of the seven categories, including our AI category. Work on the project individually or with friends. Young people can take part individually or in teams of up to five. Submit it via the Coolest Projects website by 27 May 2026. We encourage creators to take part in both the online showcase and their local in-person event — find the dates below. See your project showcased in our online gallery. Join the celebration livestream to discover creations from around the world on 24 June 2026. I want to take part <img loading="lazy" decoding="async" width="799" height="533" src="https://www.raspberrypi.org/app/uploads/2026/01/54576484207_5fdff50913_c.jpg" alt="Presenter: Greg Foot interviewing a young learner at Coolest Projects" class="wp-image-92310" style="aspect-ratio:1.4990765442232712;width:705px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/54576484207_5fdff50913_c.jpg 799w, https://www.raspberrypi.org/app/uploads/2026/01/54576484207_5fdff50913_c-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/01/54576484207_5fdff50913_c-500x334.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/54576484207_5fdff50913_c-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/01/54576484207_5fdff50913_c-450x300.jpg 450w" sizes="auto, (max-width: 799px) 100vw, 799px" /> Group codes for mentors entering multiple projects online If you’re entering several projects on behalf of your group, you can create a group code to link young people’s projects to your account. Here’s a quick reminder of how it works: Sign up or log in: Use your Raspberry Pi account or create a new one. Create a group: Add your group details, including name and country. Share your group code: Young people enter the code on their project submission form. Review and submit: View all linked projects in your group dashboard. There is no limit to the number of young people who can submit through one group code. If you’d like a full walkthrough, watch our step-by-step group code ‘how-to’ video. Whether your coders are beginners or experienced digital makers, there are lots of resources to support them. Visit the Coolest Projects guidance page for mentor guides, session plans, and more. Coolest Projects in-person events in 2026 Alongside the global online showcase, Coolest Projects events will take place in several countries again this year. We encourage creators to take part in both their local in-person event and the global showcase. <img loading="lazy" decoding="async" width="799" height="533" src="https://www.raspberrypi.org/app/uploads/2026/01/54577678590_bc123bedc2_c.jpg" alt="Two young learner testing out a project at Coolest Projects UK" class="wp-image-92313" style="aspect-ratio:1.4990765442232712;width:741px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/54577678590_bc123bedc2_c.jpg 799w, https://www.raspberrypi.org/app/uploads/2026/01/54577678590_bc123bedc2_c-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/01/54577678590_bc123bedc2_c-500x334.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/54577678590_bc123bedc2_c-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/01/54577678590_bc123bedc2_c-450x300.jpg 450w" sizes="auto, (max-width: 799px) 100vw, 799px" /> Save the date for: Coolest Projects Belgium – 14 March Coolest Projects Japan – 29 March Coolest Projects USA (Minnesota) – 11 April Coolest Projects Ireland – 25 April Coolest Projects USA (Georgia) – 2 May Coolest Projects UK – 16 May Coolest Projects Sudan (held in Egypt) – 12 August Coolest Projects Nigeria – 29 August More dates coming soon for: Coolest Projects Canada Coolest Projects India Coolest Projects Indonesia Coolest Projects Malaysia Coolest Projects South Africa Coolest Projects Sri Lanka Sign up to the Coolest Projects newsletter to be the first to hear about events near you. We can’t wait to see what your young people create Whether your young people already have something they’re excited to share or they’re ready to start something new, Coolest Projects is the perfect place to celebrate their creativity, problem solving, and imagination. We can’t wait to see the incredible projects they bring to Coolest Projects 2026. I want to take part The post Coolest Projects 2026: Opens for entries in January! appeared first on Raspberry Pi Foundation.
Learning from Code Clubs around the world: How approaches differ but values are shared
Every week, young people around the world gather in libraries, classrooms, community centres, and makerspaces to create with code. From Gujarat to Glasgow, Nairobi to New Jersey, the settings may differ, but the energy is unmistakable. We set out to learn from the wealth of experiences within the global Code Club community: how clubs adapt… The post Learning from Code Clubs around the world: How approaches differ but values are shared appeared first on Raspberry Pi Foundation.
Every week, young people around the world gather in libraries, classrooms, community centres, and makerspaces to create with code. From Gujarat to Glasgow, Nairobi to New Jersey, the settings may differ, but the energy is unmistakable. <img loading="lazy" decoding="async" width="1333" height="889" src="https://www.raspberrypi.org/app/uploads/2026/01/IMG_9862.jpg" alt="Code Club meeting at the shared hub at AEF Reuben in Kenya" class="wp-image-92271" style="aspect-ratio:1.4994561124592085;width:734px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/IMG_9862.jpg 1333w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_9862-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_9862-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_9862-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_9862-800x534.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_9862-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_9862-900x600.jpg 900w" sizes="auto, (max-width: 1333px) 100vw, 1333px" /> Code Club meeting at the shared hub at AEF Reuben in Kenya We set out to learn from the wealth of experiences within the global Code Club community: how clubs adapt to local needs, and which practices consistently support young people’s learning. Although we found small differences in how clubs make Code Club work locally, what stands out far more are the shared principles that make it work everywhere. Here we share stories from across our network that collectively paint a vibrant picture of what makes this movement work. Inspiration from the people who make Code Club thrive One theme runs through every story: Code Club is powered by people who really understand the needs of their community. During a visit to a set of Code Clubs in India, our team met a group of girls who once faced barriers to attending school. Now, they are confidently creating Scratch projects and exploring new technologies. Their club leader explained how a simple change — allowing girls to attend school wearing traditional attire — opened doors for families. Seeing these young creators code with pride is a vivid reminder of how opportunity can reshape futures. <img loading="lazy" decoding="async" width="2000" height="924" src="https://www.raspberrypi.org/app/uploads/2026/01/1ebbd9e4-b93b-4e76-9d7a-df510524e986.jpg" alt="Welspun Vapi Code Club in Gujarat" class="wp-image-92249" style="aspect-ratio:2.1645997745208567;width:730px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/1ebbd9e4-b93b-4e76-9d7a-df510524e986.jpg 2000w, https://www.raspberrypi.org/app/uploads/2026/01/1ebbd9e4-b93b-4e76-9d7a-df510524e986-300x139.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/01/1ebbd9e4-b93b-4e76-9d7a-df510524e986-500x231.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/1ebbd9e4-b93b-4e76-9d7a-df510524e986-768x355.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/01/1ebbd9e4-b93b-4e76-9d7a-df510524e986-1536x710.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/01/1ebbd9e4-b93b-4e76-9d7a-df510524e986-800x370.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/01/1ebbd9e4-b93b-4e76-9d7a-df510524e986-450x208.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/01/1ebbd9e4-b93b-4e76-9d7a-df510524e986-900x416.jpg 900w" sizes="auto, (max-width: 2000px) 100vw, 2000px" /> Welspun Vapi Code Club in Gujarat A club leader at Better Juniors Digital Club at Better Life Primary and Junior School in Kenya described how their programme began with just one laptop. Rather than letting that limit what learners could do, he found solutions everywhere: applying for grants, borrowing digital space from a nearby hub, and setting up equipment so children could work on projects together. His determination effectively created a bridge between schools and resources, opening up real opportunities for every child to learn. We also heard from educators whose clubs have become long-standing pillars of their communities. At Rhiwbina Library in Wales, leaders have been running Code Club for over a decade, creating a space where older creators naturally guide new ones. When asked about club rules, one child replied: “There’s only one and that’s ‘respect’.” That simple principle continues to shape a joyful, collaborative atmosphere. <img loading="lazy" decoding="async" width="600" height="450" src="https://www.raspberrypi.org/app/uploads/2026/01/IMG_20250930_125643.jpg" alt="Fiona Lindsay and pupils at Hillside Primary School in Scotland " class="wp-image-92268" style="width:708px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/IMG_20250930_125643.jpg 600w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_20250930_125643-300x225.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_20250930_125643-500x375.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/IMG_20250930_125643-450x338.jpg 450w" sizes="auto, (max-width: 600px) 100vw, 600px" /> Fiona Lindsay and pupils at Hillside Primary School in Scotland And sometimes the inspiration comes from the young people themselves. At Hillside Primary in Scotland, an enthusiastic creator took it upon himself to run taster sessions and codealongs for new members, helping them discover whether Code Club was right for them. His enthusiasm and leadership were infectious, and that spirit of young people lifting up their peers is something we’ve seen in clubs all over the world. Moments of joyful learning capture the spirit of Code Club In one club that meets across three different venues in Pennsylvania, USA — a creative arts centre, a coffee shop, and a library — the excitement became contagious. The librarian, Miss Sandy, was so inspired by the learners’ projects — including the moment they added “Shredder Cat”, the library’s pet mascot, into their digital creations — that she has begun learning to code alongside them. <img loading="lazy" decoding="async" width="1200" height="671" src="https://www.raspberrypi.org/app/uploads/2026/01/Screenshot-2026-01-12-at-10.21.06-1.png" alt="Miss Sandy, Ruth, and her Code Club in Pennsylvania " class="wp-image-92266" style="aspect-ratio:1.7884231536926147;width:780px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/Screenshot-2026-01-12-at-10.21.06-1.png 1200w, https://www.raspberrypi.org/app/uploads/2026/01/Screenshot-2026-01-12-at-10.21.06-1-300x168.png 300w, https://www.raspberrypi.org/app/uploads/2026/01/Screenshot-2026-01-12-at-10.21.06-1-500x280.png 500w, https://www.raspberrypi.org/app/uploads/2026/01/Screenshot-2026-01-12-at-10.21.06-1-768x429.png 768w, https://www.raspberrypi.org/app/uploads/2026/01/Screenshot-2026-01-12-at-10.21.06-1-800x447.png 800w, https://www.raspberrypi.org/app/uploads/2026/01/Screenshot-2026-01-12-at-10.21.06-1-450x252.png 450w, https://www.raspberrypi.org/app/uploads/2026/01/Screenshot-2026-01-12-at-10.21.06-1-900x503.png 900w" sizes="auto, (max-width: 1200px) 100vw, 1200px" /> Miss Sandy, Ruth, and her Code Club in Pennsylvania At one showcase event in India, learners proudly demonstrated text-to-speech and video-sensing projects — remarkable achievements for many of them in their first year of coding. They explained their ideas with confidence and clarity, sharing the logic behind their work as parents, teachers, and mentors looked on with pride. In the UK, we experienced a beautiful moment at Fakenham Academy where the room filled with a chorus of squarks, clicking, tapping, and squeaking sounds as learners adapted the Grow a Dragonfly project in their own creative ways. From applause erupting whenever a project is finished to a room buzzing with micro:bits or young people debugging together on a shared laptop, these snapshots show Code Club at its best. All about community: Belonging, identity, and a resourceful spirit Whatever the context or setting, Code Club leaders are resourceful. They are not waiting for others to solve their challenges; together with their creators, they are finding local solutions that work. Communities share equipment, mentor each other, offer space, and build continuity for learners in imaginative ways. Young people are gaining far more than digital skills: they are developing belonging, confidence, and a clear sense that they are part of something bigger. For example, in a club in Kenya, two groups learnt side by side in a shared space. Younger learners were welcomed by older peers who acted as mentors, creating a real sense of community — collaborative, vibrant, and full of pride in each other’s achievements. <img loading="lazy" decoding="async" width="500" height="625" src="https://www.raspberrypi.org/app/uploads/2024/04/1-1-500x625.png" alt="Learners at a Code Club at Oasis Mathare in Kenya " class="wp-image-86862" srcset="https://www.raspberrypi.org/app/uploads/2024/04/1-1-500x625.png 500w, https://www.raspberrypi.org/app/uploads/2024/04/1-1-200x250.png 200w, https://www.raspberrypi.org/app/uploads/2024/04/1-1-768x960.png 768w, https://www.raspberrypi.org/app/uploads/2024/04/1-1-800x1000.png 800w, https://www.raspberrypi.org/app/uploads/2024/04/1-1-450x562.png 450w, https://www.raspberrypi.org/app/uploads/2024/04/1-1-900x1125.png 900w, https://www.raspberrypi.org/app/uploads/2024/04/1-1.png 945w" sizes="auto, (max-width: 500px) 100vw, 500px" /> Code Club at Oasis Mathare in Kenya We also saw how deeply this work is woven into people’s identities. One team member visiting three clubs in Malvern, UK, wrote about Bob Bilsland, a Code Club champion for 13 years, describing how naturally he connected with learners at their own level and how fully he embodied the role of mentor and champion: “Seeing his versatility as he mentored, sparked excitement and connected with creators at their own level was a thing to behold… he truly walks the talk.” The impact can sometimes show up in unexpected ways. Holly, a leader from Illinois, USA, shared that her learners wear their Code Club t-shirts to school as “spirit gear” on Fridays. She told us how much this meant to her students: “They absolutely love their shirts and are thrilled to be able to wear them… It makes them feel like a team.” A reminder that belonging matters just as much as skills. Across every example, we saw resilience, creativity, and generosity in action because Code Clubs grow from strong communities. Locally rooted but globally informed These stories underscore something essential: Code Club grows not because of any one model, but because communities everywhere make it their own. Sandra Keeru, Programme Coordinator in Kenya, put it beautifully when she reflected that: “Code Club is locally rooted but globally informed.” Each club reflects the needs, culture, and creativity of its community, yet everywhere the same shared values shine through: curiosity, inclusion, and the belief that young people can achieve remarkable things. Code Club is more than just learning to code; it’s about creating opportunities, encouraging confidence, and building a global network of digital creators. Whether you’re a mentor, educator, or young digital maker, there’s a place for you in the community. Start your Code Club journey today and join a global community of digital creators. The post Learning from Code Clubs around the world: How approaches differ but values are shared appeared first on Raspberry Pi Foundation.
How can we teach about AI in the arts, humanities and sciences? Research seminar series 2026
For the last five years, once a month, we have hosted an online seminar sharing computing education research. Seminars are organised as usually year-long series with changing themes. In 2025, for example, our theme was ‘Teaching about AI and data science’. In 2024, it was ‘Teaching programming (with or without AI)’. It is not surprising… The post How can we teach about AI in the arts, humanities and sciences? Research seminar series 2026 appeared first on Raspberry Pi Foundation.
For the last five years, once a month, we have hosted an online seminar sharing computing education research. Seminars are organised as usually year-long series with changing themes. In 2025, for example, our theme was ‘Teaching about AI and data science’. In 2024, it was ‘Teaching programming (with or without AI)’. <img loading="lazy" decoding="async" width="500" height="333" src="https://www.raspberrypi.org/app/uploads/2025/07/AI-research-summit-27-500x333.jpg" alt="Three people look at sticky notes on a whiteboard." class="wp-image-91148" srcset="https://www.raspberrypi.org/app/uploads/2025/07/AI-research-summit-27-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2025/07/AI-research-summit-27-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2025/07/AI-research-summit-27-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2025/07/AI-research-summit-27-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2025/07/AI-research-summit-27-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2025/07/AI-research-summit-27-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2025/07/AI-research-summit-27-900x600.jpg 900w, https://www.raspberrypi.org/app/uploads/2025/07/AI-research-summit-27.jpg 1890w" sizes="auto, (max-width: 500px) 100vw, 500px" /> It is not surprising that for the last few years our focus has been on AI technology, and for 2026 we will continue this. But we will shift from showcasing how computing education research is changing teaching and learning in computing lessons, to showcasing how computing education research in other disciplines, such as art or geography, is starting to include teaching about AI. For example, art lessons may change so that learners find out how professional artists are using AI tools to create arts. Or geography lessons may change so that learners discover how professional geographers are using AI to make predictions about physical or human aspects of geography, such as volcanic activity and global warming. Our series for 2026 is called ‘Applied AI’. This title recognises that AI technology is applied across contexts, across careers, across disciplines, and this means what we teach across school subjects will change. Encouraging a pull from disciplines, rather than a push from computer science The majority of resources and professional development material related to teaching about AI have been developed by the computer science community. For example, we have developed the popular Experience AI resources in collaboration with Google DeepMind. In these resources, the contexts were carefully selected to represent real-world examples across disciplines, and to to enable the teaching of particular technical or social and ethical concepts. This could be described as “a push” of content from computing towards other disciplines. For example, to enable teaching about the ethical issues around plagiarism, an art context is used in the Experience AI resources; to enable teaching about the potential benefits of using AI tools, an ecological geography context is used. <img loading="lazy" decoding="async" width="1999" height="1121" src="https://www.raspberrypi.org/app/uploads/2026/01/image1-1.png" alt="Example activity from the Experience AI resources, focused on ecology" class="wp-image-92204" style="aspect-ratio:1.783262016121007;width:754px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/image1-1.png 1999w, https://www.raspberrypi.org/app/uploads/2026/01/image1-1-300x168.png 300w, https://www.raspberrypi.org/app/uploads/2026/01/image1-1-500x280.png 500w, https://www.raspberrypi.org/app/uploads/2026/01/image1-1-768x431.png 768w, https://www.raspberrypi.org/app/uploads/2026/01/image1-1-1536x861.png 1536w, https://www.raspberrypi.org/app/uploads/2026/01/image1-1-800x449.png 800w, https://www.raspberrypi.org/app/uploads/2026/01/image1-1-450x252.png 450w, https://www.raspberrypi.org/app/uploads/2026/01/image1-1-900x505.png 900w" sizes="auto, (max-width: 1999px) 100vw, 1999px" /> Example activity from the Experience AI resources, focused on ecology AI applications are always situated within a particular topic. Most current AI applications are data-driven: vast amounts of data are collected and processed to produce models that can then either be used to generate outputs or make predictions. For example, data about artworks can be collected and used to train a model for generating outputs similar to the artworks; this is an application of AI in the art discipline. Or data on wild fires can be collected and used to train a model for making predictions about current or prospective fires; this is an application of AI in the geography discipline. <img loading="lazy" decoding="async" width="1999" height="769" src="https://www.raspberrypi.org/app/uploads/2026/01/image2.png" alt="Example activity from the Experience AI resources, focused on meteorology " class="wp-image-92207" srcset="https://www.raspberrypi.org/app/uploads/2026/01/image2.png 1999w, https://www.raspberrypi.org/app/uploads/2026/01/image2-300x115.png 300w, https://www.raspberrypi.org/app/uploads/2026/01/image2-500x192.png 500w, https://www.raspberrypi.org/app/uploads/2026/01/image2-768x295.png 768w, https://www.raspberrypi.org/app/uploads/2026/01/image2-1536x591.png 1536w, https://www.raspberrypi.org/app/uploads/2026/01/image2-800x308.png 800w, https://www.raspberrypi.org/app/uploads/2026/01/image2-450x173.png 450w, https://www.raspberrypi.org/app/uploads/2026/01/image2-900x346.png 900w" sizes="auto, (max-width: 1999px) 100vw, 1999px" /> Example activity from the Experience AI resources, focused on meteorology In reality, the best people to recognise how AI technology is being applied in a discipline and what students in that discipline should be taught about these applications are the people working in the discipline, for example the art and geography teachers. Computer science educators can work to build the technical understanding and the general social and ethical understanding that is common across applications. But the detail of how AI technology is changing a discipline can only truly be understood by the respective community, by the artists and art educators, by the geographers and the geography educators. An emerging focus At present, though, most educators are grappling with how they can use AI tools for productivity, such as creating lesson plans, or answering emails. Or they are looking at how they can use AI for general teaching and learning, for example for personalisation, say for students with additional needs. The idea that their underpinning discipline is changing is, perhaps, not yet on teachers’ radar. But at universities, such as in undergraduate courses, and in the world of work, education and training are changing. Data science courses are now being offered across faculties, including science, geography, language, and art faculties. These changes will start to filter down to school-based education via curriculum change. While some resources and professional development materials addressing this shift are already becoming available, change is still fragile and patchy. Raising awareness, building community and a common language The aims of our Applied AI research seminar series in 2026 are to start to: Raise awareness of the forthcoming changes that applying AI will bring to disciplines Build a cross-discipline community Think about a common language that could be used across disciplines If we can start to agree on what common concepts could be taught in the arts, sciences and humanities, it gives us a better chance to: Understand how to use AI as it is applied in different disciplines Help students to build useful mental models and develop the agency and critical thinking skills they need to evaluate these applications and decide when and how to use them and how far to trust them We need your help To make our 2026 series a success, we need to spread the word about our seminars to groups of educators, researchers, industry and policy makers across the arts, sciences, and humanities. <img loading="lazy" decoding="async" width="2560" height="1707" src="https://www.raspberrypi.org/app/uploads/2025/02/AI-research-summit-32-scaled.jpg" alt="" class="wp-image-89521" style="aspect-ratio:1.4995397617918607;width:712px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2025/02/AI-research-summit-32-scaled.jpg 2560w, https://www.raspberrypi.org/app/uploads/2025/02/AI-research-summit-32-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2025/02/AI-research-summit-32-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2025/02/AI-research-summit-32-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2025/02/AI-research-summit-32-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2025/02/AI-research-summit-32-2048x1366.jpg 2048w, https://www.raspberrypi.org/app/uploads/2025/02/AI-research-summit-32-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2025/02/AI-research-summit-32-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2025/02/AI-research-summit-32-900x600.jpg 900w" sizes="auto, (max-width: 2560px) 100vw, 2560px" /> Please tell those you know in these groups about the seminar series, and share it through your social media and other networks. If you have ideas for subject associations we could connect with or publications where we can write about our series, please let us know. Join our ‘Applied AI’ seminar series We have already arranged the following seminars across 2026 and will add more speakers for the remaining monthly slots soon. Seminars always take place online on Tuesdays at 17:00 to 18:30 UK time. 10 February: Social studies, public policy, economics and AI — Thema Monroe-White (George Mason University, USA) 17 March: Arts and AI — Rebecca Fiebrink (University of the Arts London, UK) 14 April: Healthcare and AI — Kathryn Jessen Eller (Data Science, AI & You (DSAIY) in Healthcare, USA) 14 July: Literacy and AI — Dan Verständig (Goethe University Frankfurt, Germany) 8 September: History and AI — Jie Chao (The Concord Consortium) 6 October: Robotics and AI — Eleni Petraki & Damith Herath (University of Canberra, Australia) 10 November: Geography and AI — Doreen Boyd (University of Nottingham, UK) To sign up and take part, click the button below. We’ll then send you information about joining. We hope to see you there. I want to join the next seminar You can view the schedule and details of our upcoming seminars on this page, and catch up on past seminars on our previous seminars page. PS If you are teaching upper primary school learners in England, you can currently register your interest in our upcoming collaborative study on data science education. You’ll find out more about some of the research we’ve done in this area in this blog post. The post How can we teach about AI in the arts, humanities and sciences? Research seminar series 2026 appeared first on Raspberry Pi Foundation.
What shaped computing education in 2025 — and what comes next
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,… The post What shaped computing education in 2025 — and what comes next appeared first on Raspberry Pi Foundation.
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. <img loading="lazy" decoding="async" width="1999" height="1500" src="https://www.raspberrypi.org/app/uploads/2026/01/image1.jpg" alt="The Hello World Podcast team " class="wp-image-92155" style="width:754px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/image1.jpg 1999w, https://www.raspberrypi.org/app/uploads/2026/01/image1-300x225.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/01/image1-500x375.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/image1-768x576.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/01/image1-1536x1153.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/01/image1-800x600.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/01/image1-450x338.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/01/image1-900x675.jpg 900w" sizes="auto, (max-width: 1999px) 100vw, 1999px" /> 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. Looking back at 2025 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. <img loading="lazy" decoding="async" width="1500" height="1999" src="https://www.raspberrypi.org/app/uploads/2026/01/image2.jpg" alt="The new set!" class="wp-image-92157" style="width:500px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/image2.jpg 1500w, https://www.raspberrypi.org/app/uploads/2026/01/image2-188x250.jpg 188w, https://www.raspberrypi.org/app/uploads/2026/01/image2-500x666.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/image2-768x1023.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/01/image2-1153x1536.jpg 1153w, https://www.raspberrypi.org/app/uploads/2026/01/image2-800x1066.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/01/image2-450x600.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/01/image2-900x1199.jpg 900w" sizes="auto, (max-width: 1500px) 100vw, 1500px" /> 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. What’s ahead 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). <img loading="lazy" decoding="async" width="1333" height="884" src="https://www.raspberrypi.org/app/uploads/2026/01/HW_28.5-2.jpg" alt="The cast on set" class="wp-image-92170" style="width:720px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/01/HW_28.5-2.jpg 1333w, https://www.raspberrypi.org/app/uploads/2026/01/HW_28.5-2-300x199.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/01/HW_28.5-2-500x332.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/01/HW_28.5-2-768x509.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/01/HW_28.5-2-800x531.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/01/HW_28.5-2-450x298.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/01/HW_28.5-2-900x597.jpg 900w" sizes="auto, (max-width: 1333px) 100vw, 1333px" /> 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. More from Hello World 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. The post What shaped computing education in 2025 — and what comes next appeared first on Raspberry Pi Foundation.
A research-led framework for teaching about models in AI and data science
Research indicates that teaching learners to use and create with data-driven technologies such as AI and machine learning (ML) requires an entirely different approach for solving problems compared to traditional programming activities. In this blog, we share the new data paradigms framework that we have developed through research and used to help improve our understanding… The post A research-led framework for teaching about models in AI and data science appeared first on Raspberry Pi Foundation.
Research indicates that teaching learners to use and create with data-driven technologies such as AI and machine learning (ML) requires an entirely different approach for solving problems compared to traditional programming activities. <img loading="lazy" decoding="async" width="500" height="333" src="https://www.raspberrypi.org/app/uploads/2023/11/NCCE-Enfeild-Primary-37-500x333.jpg" alt="Learner in a computing classroom." class="wp-image-85484" srcset="https://www.raspberrypi.org/app/uploads/2023/11/NCCE-Enfeild-Primary-37-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2023/11/NCCE-Enfeild-Primary-37-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2023/11/NCCE-Enfeild-Primary-37-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2023/11/NCCE-Enfeild-Primary-37-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2023/11/NCCE-Enfeild-Primary-37-2048x1365.jpg 2048w, https://www.raspberrypi.org/app/uploads/2023/11/NCCE-Enfeild-Primary-37-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2023/11/NCCE-Enfeild-Primary-37-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2023/11/NCCE-Enfeild-Primary-37-900x600.jpg 900w" sizes="auto, (max-width: 500px) 100vw, 500px" /> In this blog, we share the new data paradigms framework that we have developed through research and used to help improve our understanding about how to teach and learn about AI and data science. We also invite you to register your interest in participating in our next collaborative study on the topic. Knowledge-based approaches to systems design Let’s start by highlighting an important distinction between different approaches to designing systems. In a knowledge-based approach to system design, a set of rules (e.g., if-then statements) are written for the system to execute. Every rule is explicitly defined. This approach is called ‘rule-based’, ‘symbolic’, or ‘logic-based’. For example, a developer could create a program that simulates dialogue by writing specific lines of code to handle a greeting, such as “IF user says “Hello” THEN output “Hi!”. If the user types “Greetings!” instead, the program fails because it has no rule for that specific word. <img loading="lazy" decoding="async" width="1000" height="651" src="https://www.raspberrypi.org/app/uploads/2025/03/ATHENS_CODE_CLUB_025.jpg" alt="An educator helps students with a coding task." class="wp-image-89708" style="width:700px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2025/03/ATHENS_CODE_CLUB_025.jpg 1000w, https://www.raspberrypi.org/app/uploads/2025/03/ATHENS_CODE_CLUB_025-300x195.jpg 300w, https://www.raspberrypi.org/app/uploads/2025/03/ATHENS_CODE_CLUB_025-500x326.jpg 500w, https://www.raspberrypi.org/app/uploads/2025/03/ATHENS_CODE_CLUB_025-768x500.jpg 768w, https://www.raspberrypi.org/app/uploads/2025/03/ATHENS_CODE_CLUB_025-800x521.jpg 800w, https://www.raspberrypi.org/app/uploads/2025/03/ATHENS_CODE_CLUB_025-450x293.jpg 450w, https://www.raspberrypi.org/app/uploads/2025/03/ATHENS_CODE_CLUB_025-900x586.jpg 900w" sizes="auto, (max-width: 1000px) 100vw, 1000px" /> Knowledge-based models are often said to be explainable by design. This means the logic is accessible and interpretable and developers can trace the exact steps taken to produce an output. For example, if developers manually classify restaurant reviews as positive or negative using a pre-defined set of criteria, the rules their restaurant classifying system follows are entirely explicit, and the path from input to output is clear and explainable. Data-driven approaches to systems design By contrast, in a data-driven approach to system design developers do not write specific rules. Instead, they collect lots of data and train a model. In the dialogue simulator example, they would collect hundreds of examples of greetings and train a model to the pattern of a greeting. If the user types “Greetings!”, the system generates a response based on the patterns in its training data. <img loading="lazy" decoding="async" width="1080" height="1080" src="https://www.raspberrypi.org/app/uploads/2025/01/Untitled-design-36.png" alt="Photo focused on a young person working on a computer in a classroom." class="wp-image-89304" style="width:577px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2025/01/Untitled-design-36.png 1080w, https://www.raspberrypi.org/app/uploads/2025/01/Untitled-design-36-250x250.png 250w, https://www.raspberrypi.org/app/uploads/2025/01/Untitled-design-36-500x500.png 500w, https://www.raspberrypi.org/app/uploads/2025/01/Untitled-design-36-150x150.png 150w, https://www.raspberrypi.org/app/uploads/2025/01/Untitled-design-36-768x768.png 768w, https://www.raspberrypi.org/app/uploads/2025/01/Untitled-design-36-800x800.png 800w, https://www.raspberrypi.org/app/uploads/2025/01/Untitled-design-36-100x100.png 100w, https://www.raspberrypi.org/app/uploads/2025/01/Untitled-design-36-300x300.png 300w, https://www.raspberrypi.org/app/uploads/2025/01/Untitled-design-36-450x450.png 450w, https://www.raspberrypi.org/app/uploads/2025/01/Untitled-design-36-900x900.png 900w, https://www.raspberrypi.org/app/uploads/2025/01/Untitled-design-36-400x400.png 400w, https://www.raspberrypi.org/app/uploads/2025/01/Untitled-design-36-600x600.png 600w" sizes="auto, (max-width: 1080px) 100vw, 1080px" /> Data-driven models are often opaque. In other words, the internal workings of these ML models are hidden. While we can see our input and the system’s output, the internal mathematical process is so complex — often involving layers of calculations and abstractions — that we cannot simply “explain” why a specific output was produced. For example, developers can create a classification model by training a neural network using thousands of images. Due to the large quantity of data used to train the model, and complex internal parameters and hidden layers, developers and users of the system cannot understand or explain the logic or features that lead to a specific output. These kinds of models are often referred to as a “black box” (as opposed to a “glass” or “clear” box). Comparing knowledge-based and data-driven approaches Researchers have argued that the move from knowledge-based (or rule-based) programming to data-driven system design represents a paradigm shift and creates unique challenges for educators. The challenge is helping students shift from the expectation that a system produces a single ‘right’ answer — characteristic of traditional rule-based programming — toward an understanding that systems trained on large quantities of data produce outcomes that aren’t always fixed or explainable. If the current instruction in the classroom still relies heavily on traditional rule-based programming approaches, we might be setting students up for misconceptions. Data paradigms: A framework for analysing data science education approaches In our research work on AI and data science at the Raspberry Pi Computing Education Research Centre, we analysed 84 research studies about the teaching and learning of data science. We categorised learning activities used in the studies to understand whether they were (i) knowledge-based or data-driven, and (ii) the extent to which the underlying models used were transparent or opaque. This led us to define four distinct data paradigms: <img loading="lazy" decoding="async" width="1999" height="1031" src="https://www.raspberrypi.org/app/uploads/2026/01/image1.png" alt="The data paradigms framework" class="wp-image-92176" srcset="https://www.raspberrypi.org/app/uploads/2026/01/image1.png 1999w, https://www.raspberrypi.org/app/uploads/2026/01/image1-300x155.png 300w, https://www.raspberrypi.org/app/uploads/2026/01/image1-500x258.png 500w, https://www.raspberrypi.org/app/uploads/2026/01/image1-768x396.png 768w, https://www.raspberrypi.org/app/uploads/2026/01/image1-1536x792.png 1536w, https://www.raspberrypi.org/app/uploads/2026/01/image1-800x413.png 800w, https://www.raspberrypi.org/app/uploads/2026/01/image1-450x232.png 450w, https://www.raspberrypi.org/app/uploads/2026/01/image1-900x464.png 900w" sizes="auto, (max-width: 1999px) 100vw, 1999px" /> The data paradigms framework Knowledge-based and transparent (KB + T): Activities in this paradigm are ones where students write rules for systems, or work with systems that use rules, where the logic is fully explainable by design. For example, if students manually classify data (e.g. creating simple ‘if-then’ statements to predict an outcome), the path from input to output is clear. Data-driven + Transparent (DD + T): In this paradigm, activities involve students working with models trained on data, but the trained model’s logic remains explainable and interpretable. For example these could be models using k-nearest neighbors (KNN) algorithm to group data points based on proximity, or using linear regression to predict a trend. Even though the model produces an output, the student can look at the inner workings of the model and see how the decision is made. Data-driven + Opaque (DD + O): This paradigm’s activities require students to work with data-driven ML models where the models’ internal logic is hidden, for example an image classification model using a type of neural network (e.g. CNN). The model produces an output (e.g. classifying an image as ‘This is a dog’), but the student cannot inspect the system to find a rule or clear path explaining why that specific output was produced. To understand these systems, it’s necessary to use additional testing and evaluation tools. Knowledge-based + Opaque (KB + O): Activities in this paradigm would involve systems with human-written rules that are not explainable. In our review of K–12 activities, we found no examples of activities within this paradigm. The data paradigms framework helps us to distinguish between different kinds of modeling activities students take part in and how instructional approaches could be classified across one or more paradigms. For instance, we found that most data-driven activities were also opaque (DD + O), usually meaning that students collected and used data to train a model, but how the system worked was opaque. This pattern, where the data is visible but the model is not explainable, risks students forming misconceptions about the capabilities and limitations of data-driven systems. Without understanding how outputs are generated, students may expect data-driven ML systems to operate like fully explainable (or transparent) ones. <img loading="lazy" decoding="async" width="560" height="373" src="https://www.raspberrypi.org/app/uploads/2023/07/Hills-Road-Dojo_Low-res_22-01-23-24.jpg" alt="Learners at a Code Club." class="wp-image-84606" style="width:698px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2023/07/Hills-Road-Dojo_Low-res_22-01-23-24.jpg 560w, https://www.raspberrypi.org/app/uploads/2023/07/Hills-Road-Dojo_Low-res_22-01-23-24-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2023/07/Hills-Road-Dojo_Low-res_22-01-23-24-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2023/07/Hills-Road-Dojo_Low-res_22-01-23-24-450x300.jpg 450w" sizes="auto, (max-width: 560px) 100vw, 560px" /> We think that lessons are needed in the data-driven opaque (DD + O) quadrant to explicitly teach students about how data-driven systems work and the role they play in everyday contexts. However, when teaching data-driven opaque (DD + O) activities, learners’ attention needs to be directed to concepts such as model confidence, data quality, and model evaluation. Since an ML model is not inherently explainable, we need to teach students to use post-hoc explanation methods, such as testing different inputs to see how a system’s output changes. To prepare students for this learning experience, we think that first introducing activities about rule-based systems (knowledge-based + transparent; KB + T) or simple data exploration, such as linear regression or data visualisation (data-driven + transparent; DD + T) may serve as a ‘bridge’ to understanding data-driven modeling by helping students to distinguish between systems built from specific logical rules and systems trained on data. We believe the idea of data paradigms can serve as a way of framing teaching activities about data science and help educators and students to consider the transition between different paradigms when engaging with the systems we interact with every day. Teachers in England, participate in our new study KS2 teachers, participate in our new study! We’re launching a new study to explore how to teach learners aged 9 to 11 about data-driven computing. The study will take place in collaboration with KS2 teachers (Y4/Y5/Y6) in England, Scotland and Wales and look at: What key ideas pupils need to understand How teachers currently approach topics related to data-driven computing How pupils make sense of data and probability Our goal is to find practical ways to help teachers build children’s confidence in working with data in computing lessons. The study will be collaborative, with two workshops held throughout 2026, and we’re inviting KS2 teachers (Y4/Y5/Y6) to take part. You can express your interest in participating by filling in this form: Register your interest The post A research-led framework for teaching about models in AI and data science appeared first on Raspberry Pi Foundation.
Introducing our new ‘Programming with AI’ unit
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… The post Introducing our new ‘Programming with AI’ unit appeared first on Raspberry Pi Foundation.
In the age of AI, we believe kids still need to learn to code. We’re also testing how AI tools can be used to help with teaching and learning programming. This blog dives into our new classroom unit ‘Programming with AI’, which we’ve developed for teachers to introduce students to learning to code using tools built on large language models (LLMs). We share an overview of the unit, and the early excitement of educators and learners with whom we’re exploring this new approach to teaching and learning programming. <img loading="lazy" decoding="async" width="1999" height="1125" src="https://www.raspberrypi.org/app/uploads/2025/12/image4-1.jpg" alt="Students completing activities from lesson 1" class="wp-image-92116" style="width:756px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2025/12/image4-1.jpg 1999w, https://www.raspberrypi.org/app/uploads/2025/12/image4-1-300x169.jpg 300w, https://www.raspberrypi.org/app/uploads/2025/12/image4-1-500x281.jpg 500w, https://www.raspberrypi.org/app/uploads/2025/12/image4-1-768x432.jpg 768w, https://www.raspberrypi.org/app/uploads/2025/12/image4-1-1536x864.jpg 1536w, https://www.raspberrypi.org/app/uploads/2025/12/image4-1-800x450.jpg 800w, https://www.raspberrypi.org/app/uploads/2025/12/image4-1-450x253.jpg 450w, https://www.raspberrypi.org/app/uploads/2025/12/image4-1-900x507.jpg 900w" sizes="auto, (max-width: 1999px) 100vw, 1999px" /> Students completing activities from lesson 1 Bridging the gap in programming education 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. <img loading="lazy" decoding="async" width="500" height="333" src="https://www.raspberrypi.org/app/uploads/2023/10/YP7_1955-500x333.jpg" alt="A group of male students at the Coding Academy in Telangana." class="wp-image-85231" srcset="https://www.raspberrypi.org/app/uploads/2023/10/YP7_1955-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2023/10/YP7_1955-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2023/10/YP7_1955-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2023/10/YP7_1955-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2023/10/YP7_1955-2048x1365.jpg 2048w, https://www.raspberrypi.org/app/uploads/2023/10/YP7_1955-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2023/10/YP7_1955-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2023/10/YP7_1955-900x600.jpg 900w" sizes="auto, (max-width: 500px) 100vw, 500px" /> 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. Key design decisions during unit development During the design phase of the unit, we identified three main challenges we wanted to address: The first was the transition from block-based to text-based coding. The students we were designing for are familiar with block-based programming like Scratch, but Python needs precisely typed code (syntax). This is a huge shift, and we wanted to offer a way to bridge the gap. We also had to figure out how to teach prompting LLM tools to students who are learning English as their second language. Finally, LLMs are non-deterministic. This means that LLM tools don’t always produce the same output, even when given the same prompt. They also do not generate correct code consistently. 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. <img loading="lazy" decoding="async" width="1608" height="350" src="https://www.raspberrypi.org/app/uploads/2025/12/image6.png" alt="Comparison of Python syntax with equivalent Scratch block" class="wp-image-92117" srcset="https://www.raspberrypi.org/app/uploads/2025/12/image6.png 1608w, https://www.raspberrypi.org/app/uploads/2025/12/image6-300x65.png 300w, https://www.raspberrypi.org/app/uploads/2025/12/image6-500x109.png 500w, https://www.raspberrypi.org/app/uploads/2025/12/image6-768x167.png 768w, https://www.raspberrypi.org/app/uploads/2025/12/image6-1536x334.png 1536w, https://www.raspberrypi.org/app/uploads/2025/12/image6-800x174.png 800w, https://www.raspberrypi.org/app/uploads/2025/12/image6-450x98.png 450w, https://www.raspberrypi.org/app/uploads/2025/12/image6-900x196.png 900w" sizes="auto, (max-width: 1608px) 100vw, 1608px" /> Comparison of Python syntax with equivalent Scratch block 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. <img loading="lazy" decoding="async" width="637" height="371" src="https://www.raspberrypi.org/app/uploads/2025/12/image5.png" alt="Illustration showing the progression of Python and generative AI concepts covered in the unit" class="wp-image-92118" style="width:707px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2025/12/image5.png 637w, https://www.raspberrypi.org/app/uploads/2025/12/image5-300x175.png 300w, https://www.raspberrypi.org/app/uploads/2025/12/image5-500x291.png 500w, https://www.raspberrypi.org/app/uploads/2025/12/image5-450x262.png 450w" sizes="auto, (max-width: 637px) 100vw, 637px" /> The progression of Python and generative AI concepts covered in the unit 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. Preparing the educators 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. <img loading="lazy" decoding="async" width="1182" height="665" src="https://www.raspberrypi.org/app/uploads/2025/12/image2-1.jpg" alt="STEM and ICT teachers participated in the training" class="wp-image-92120" style="width:750px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2025/12/image2-1.jpg 1182w, https://www.raspberrypi.org/app/uploads/2025/12/image2-1-300x169.jpg 300w, https://www.raspberrypi.org/app/uploads/2025/12/image2-1-500x281.jpg 500w, https://www.raspberrypi.org/app/uploads/2025/12/image2-1-768x432.jpg 768w, https://www.raspberrypi.org/app/uploads/2025/12/image2-1-800x450.jpg 800w, https://www.raspberrypi.org/app/uploads/2025/12/image2-1-450x253.jpg 450w, https://www.raspberrypi.org/app/uploads/2025/12/image2-1-900x506.jpg 900w" sizes="auto, (max-width: 1182px) 100vw, 1182px" /> STEM and ICT teachers participated in the training 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: Hands-on activities to ensure comfort with the Python programming language Exploring the capabilities and limitations of LLM tools Effective prompt writing, which teachers would in turn model for their students to use for code debugging and idea generation Reviewing lesson flow and learning objectives to understand the structure of a lesson Adapting specific pedagogies to integrate an LLM tool in teaching the Python programming language 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. Initial student reactions: Excitement, familiarity with LLM tools, and seamless transition 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. <img loading="lazy" decoding="async" width="1182" height="665" src="https://www.raspberrypi.org/app/uploads/2025/12/image1-1.jpg" alt="" class="wp-image-92123" srcset="https://www.raspberrypi.org/app/uploads/2025/12/image1-1.jpg 1182w, https://www.raspberrypi.org/app/uploads/2025/12/image1-1-300x169.jpg 300w, https://www.raspberrypi.org/app/uploads/2025/12/image1-1-500x281.jpg 500w, https://www.raspberrypi.org/app/uploads/2025/12/image1-1-768x432.jpg 768w, https://www.raspberrypi.org/app/uploads/2025/12/image1-1-800x450.jpg 800w, https://www.raspberrypi.org/app/uploads/2025/12/image1-1-450x253.jpg 450w, https://www.raspberrypi.org/app/uploads/2025/12/image1-1-900x506.jpg 900w" sizes="auto, (max-width: 1182px) 100vw, 1182px" /> 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. Looking ahead and collecting impact data 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. <img loading="lazy" decoding="async" width="1182" height="665" src="https://www.raspberrypi.org/app/uploads/2025/12/image3-2.jpg" alt="STEM and ICT teachers participated in the training" class="wp-image-92122" style="width:760px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2025/12/image3-2.jpg 1182w, https://www.raspberrypi.org/app/uploads/2025/12/image3-2-300x169.jpg 300w, https://www.raspberrypi.org/app/uploads/2025/12/image3-2-500x281.jpg 500w, https://www.raspberrypi.org/app/uploads/2025/12/image3-2-768x432.jpg 768w, https://www.raspberrypi.org/app/uploads/2025/12/image3-2-800x450.jpg 800w, https://www.raspberrypi.org/app/uploads/2025/12/image3-2-450x253.jpg 450w, https://www.raspberrypi.org/app/uploads/2025/12/image3-2-900x506.jpg 900w" sizes="auto, (max-width: 1182px) 100vw, 1182px" /> The teachers and team at our ‘Programming with AI’ training. 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. The post Introducing our new ‘Programming with AI’ unit appeared first on Raspberry Pi Foundation.