Computing and AI for all: From classrooms to national dialogue in India
On 7 February 2026, we witnessed something special in Bhubaneswar: a day where classroom experience took centre stage in conversations about computing and AI education in India at our Computing and AI for All conference. At this event, we brought together educators, researchers, and system leaders for shared learning, reflection, and professional dialogue where classroom… The post Computing and AI for all: From classrooms to national dialogue in India appeared first on Raspberry Pi Foundation.
On 7 February 2026, we witnessed something special in Bhubaneswar: a day where classroom experience took centre stage in conversations about computing and AI education in India at our Computing and AI for All conference. <img fetchpriority="high" decoding="async" width="2560" height="1706" src="https://www.raspberrypi.org/app/uploads/2026/03/DSC05527-scaled.jpg" alt="" class="wp-image-92690" style="width:680px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/03/DSC05527-scaled.jpg 2560w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05527-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05527-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05527-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05527-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05527-2048x1365.jpg 2048w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05527-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05527-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05527-900x600.jpg 900w" sizes="(max-width: 2560px) 100vw, 2560px" /> At this event, we brought together educators, researchers, and system leaders for shared learning, reflection, and professional dialogue where classroom practice was placed at the heart of the discussion. An event to connect conversations Across computing and AI education in India, conversations about policy, pedagogy, research, and future readiness often take place in separate spaces. At Computing and AI for All, we wanted to connect these strands in a national forum where classroom practitioners, researchers, and system leaders could speak with each other about computing and AI education. The theme ‘Computing and AI for all: Classroom to policy’ connected: Ecosystem and policy Pedagogy and innovation AI and future readiness Research, evidence and impact Bringing these conversations together helped shape a richer dialogue than we’ve seen in many traditional education forums. Grounding in classroom experience The response from educators to the opportunity to attend was both energising and encouraging. 165 educators and 22 invited guests joined us on the day. The event also featured 35 paper and poster presentations, reflecting the breadth of classroom practice, research, and innovation that teachers were keen to share. <img decoding="async" width="800" height="450" src="https://www.raspberrypi.org/app/uploads/2026/03/DJI_20260207121348_0007_D-800x450.jpg" alt="An aerial view of the attendees of the Computing and AI for All event." class="wp-image-92706" srcset="https://www.raspberrypi.org/app/uploads/2026/03/DJI_20260207121348_0007_D-800x450.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/03/DJI_20260207121348_0007_D-300x169.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/03/DJI_20260207121348_0007_D-500x281.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/03/DJI_20260207121348_0007_D-768x432.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/03/DJI_20260207121348_0007_D-1536x864.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/03/DJI_20260207121348_0007_D-2048x1152.jpg 2048w, https://www.raspberrypi.org/app/uploads/2026/03/DJI_20260207121348_0007_D-450x253.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/03/DJI_20260207121348_0007_D-900x506.jpg 900w" sizes="(max-width: 800px) 100vw, 800px" /> The conference also brought together diverse contributors. Alongside teachers, there was representation from organisations such as Computer Science Teachers’ Associations, the University of Southampton, Quest Alliance, and Learning Links Foundation. Also part of the event were attendees from government bodies including TTWREIS – Telangana Tribal Welfare Residential Educational Institutes; PSSS – Panchasakha Shikhya Setu Sangathan (a Govt. of Odisha initiative); and IIITMK Kerala. The presence of an international delegate from Nepal further broadened the exchange of perspectives. <img decoding="async" width="1999" height="1500" src="https://www.raspberrypi.org/app/uploads/2026/03/image14.jpg" alt="A speaker on stage at the Computing and AI for All event." class="wp-image-92718" style="aspect-ratio:1.5006186587478347;width:696px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/03/image14.jpg 1999w, https://www.raspberrypi.org/app/uploads/2026/03/image14-300x225.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/03/image14-500x375.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/03/image14-768x576.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/03/image14-1536x1153.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/03/image14-800x600.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/03/image14-450x338.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/03/image14-900x675.jpg 900w" sizes="(max-width: 1999px) 100vw, 1999px" /> First and foremost, the event was rooted in classroom practice. 88% of participants were ICT teachers, with strong representation from Odisha and Telangana, where we have been working on partnerships to support computing educators. A significant proportion of the educators present were attending a computing conference in person for the first time, so it was a milestone in their professional journey. <img loading="lazy" decoding="async" width="800" height="533" src="https://www.raspberrypi.org/app/uploads/2026/03/DSC05753-800x533.jpg" alt="Teachers present a poster at the Computing and AI for All event." class="wp-image-92692" srcset="https://www.raspberrypi.org/app/uploads/2026/03/DSC05753-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05753-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05753-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05753-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05753-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05753-2048x1365.jpg 2048w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05753-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05753-900x600.jpg 900w" sizes="auto, (max-width: 800px) 100vw, 800px" /> Teachers stepping onto a national stage to present their classroom experiments, research, and reflections signaled an important shift: computing and AI education in India is no longer something designed solely for teachers. It is increasingly being shaped by teachers and informed by their lived classroom realities and professional expertise. Ideas, evidence, and inspiration The conference featured a full-day academic programme beginning with a formal inauguration and an introduction to our work in India. <img loading="lazy" decoding="async" width="800" height="533" src="https://www.raspberrypi.org/app/uploads/2026/03/DSC01713-1-800x533.jpg" alt="The group performing the formal inauguration of the Computing and AI for All event." class="wp-image-92702" srcset="https://www.raspberrypi.org/app/uploads/2026/03/DSC01713-1-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/03/DSC01713-1-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/03/DSC01713-1-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/03/DSC01713-1-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/03/DSC01713-1-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/03/DSC01713-1-2048x1365.jpg 2048w, https://www.raspberrypi.org/app/uploads/2026/03/DSC01713-1-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/03/DSC01713-1-900x600.jpg 900w" sizes="auto, (max-width: 800px) 100vw, 800px" /> The keynote by the Foundation’s Director of Research and Impact, Shuchi Grover — ‘Democratizing the future: Why computing and AI literacy matters for every child, everywhere”’ — set an equity-driven, future-focused tone. It reminded us that AI literacy is not optional; it’s foundational for learning and opportunity. In the panel discussion on ‘Embedding ethical AI in everyday teaching: From principles to classroom practice’, teachers, researchers and leaders exchanged ideas about how to integrate responsible AI without losing sight of learning goals. <img loading="lazy" decoding="async" width="2560" height="1706" src="https://www.raspberrypi.org/app/uploads/2026/03/DSC05631-scaled.jpg" alt="" class="wp-image-92689" style="aspect-ratio:1.5006275061985368;width:686px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/03/DSC05631-scaled.jpg 2560w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05631-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05631-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05631-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05631-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05631-2048x1365.jpg 2048w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05631-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05631-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05631-900x600.jpg 900w" sizes="auto, (max-width: 2560px) 100vw, 2560px" /> Across sessions, teachers and researchers shared a wide range of classroom-centred insights, including: Using generative AI for algebraic discovery Designing AI-supported personalised learning models Inclusive coding practices with Scratch Using AI to reduce teacher workload Low-cost computing innovations in K–12 classrooms Hands-on technical explorations, such as Kali Linux on Raspberry Pi These sessions reflected both pedagogical creativity and deep engagement with everyday classroom challenges. The structured poster gallery created space for peer feedback and deeper discussion around critical themes such as: AI for first-generation learners Classroom research evidence AI tools to support ICT engagement What educators told us In the feedback we captured, educators described the conference as: Highly educative A space for cross-state collaboration A confidence-building platform for presenting work One participant shared: “I’m honoured to be part of [the conference]. It was inspiring to listen to educational experts sharing valuable insights on AI tools and education.” <img loading="lazy" decoding="async" width="2560" height="1706" src="https://www.raspberrypi.org/app/uploads/2026/03/DSC05750-scaled.jpg" alt="" class="wp-image-92694" style="aspect-ratio:1.5006195393169182;width:722px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/03/DSC05750-scaled.jpg 2560w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05750-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05750-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05750-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05750-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05750-2048x1365.jpg 2048w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05750-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05750-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/03/DSC05750-900x600.jpg 900w" sizes="auto, (max-width: 2560px) 100vw, 2560px" /> Many participants asked us to expand the event, suggesting a two-day format, extended hands-on AI workshops, and practical demonstrations they could take back to their classrooms. Looking ahead Our Computing and AI for All event formed a national, educator-led platform linking classroom innovation, AI literacy, research evidence, and policy dialogue. What stood out most was the spirit of participation: Teachers sharing real classroom experiments Researchers listening Policy voices engaging First-time presenters stepping into a national forum If AI is shaping the future of work and society, then teachers need to play a part in shaping the future of AI education. Computing and AI for All was one move in this direction that we were pleased to facilitate — and it is only the beginning. The post Computing and AI for all: From classrooms to national dialogue in India appeared first on Raspberry Pi Foundation.
Do you have some rope? Then let’s teach about AI concepts
Teaching about AI concepts in schools is a tricky business as there are complicated ideas to be taught. To teach complex concepts, in computer science, we often use an instructional approach called ‘unplugged’. We use the unplugged approach to teach computing concepts without a computer. Often unplugged activities include using an everyday analogy or a… The post Do you have some rope? Then let’s teach about AI concepts appeared first on Raspberry Pi Foundation.
Teaching about AI concepts in schools is a tricky business as there are complicated ideas to be taught. To teach complex concepts, in computer science, we often use an instructional approach called ‘unplugged’. We use the unplugged approach to teach computing concepts without a computer. Often unplugged activities include using an everyday analogy or a physical fun activity. For example, to teach about algorithms, students might learn how to make a jam sandwich where the recipe and following instructions accurately are similar to an algorithm and the steps within it used to write a program. The jam sandwich activity has now become a popular and key teaching experience for young students across the world, as it teaches a complex but fundamental idea in a simple and fun way. <img loading="lazy" decoding="async" width="1999" height="1333" src="https://www.raspberrypi.org/app/uploads/2026/03/image3.jpg" alt="Salomey Afua Addo is a third-year PhD student at the Raspberry Pi Computing Education Research Centre, University of Cambridge." class="wp-image-92646" style="aspect-ratio:1.4996652532916759;width:732px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/03/image3.jpg 1999w, https://www.raspberrypi.org/app/uploads/2026/03/image3-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/03/image3-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/03/image3-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/03/image3-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/03/image3-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/03/image3-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/03/image3-900x600.jpg 900w" sizes="auto, (max-width: 1999px) 100vw, 1999px" /> Salomey Afua Addo, third-year PhD student at the Raspberry Pi Computing Education Research Centre, University of Cambridge At the January 2026 Raspberry Pi Foundation Research Seminar, Salomey Afua Addo, a researcher at the University of Cambridge, presented her work about how to teach about AI. She has specifically looked at this in the context of high school students in Ghana, where AI is now part of the mandatory curriculum. In Ghana, most schools do not have access to computers, therefore an unplugged approach to teach about AI is a good idea. Therefore, Salomey developed a set of unplugged activities to teach about a range of AI concepts. Here, I focus on one of the activities that she presented — one that I think will become another ‘jam sandwich’ experience for students. So if you might teach about AI at some point, then read on. Neural networks and rope: An unplugged activity Salomey has designed an unplugged role-play activity to teach about neural networks and how they are trained to solve a problem. She focused on finding a familiar problem context for Ghanaian teachers and their students, and selected farming and crop disease. Students are asked to figure out what features about a farm are relevant for detecting diseases on cocoa trees. To solve the problem, students are given data about the farms (see Table 1). Giving students data, rather than preconceived rules about the context is key to the learning activity. Neural networks are data-driven — they provide a way to model given data so that we can make predictions. Here the features of farms, and importantly whether disease is or is not found in their cocoa trees, is the data that is used to train a model. The model is used to make predictions, which can then be used to improve farming by reducing crop disease. <img loading="lazy" decoding="async" width="1999" height="421" src="https://www.raspberrypi.org/app/uploads/2026/03/image4-1.png" alt="Students using ropes to signify the strength of connections between nodes." class="wp-image-92653" srcset="https://www.raspberrypi.org/app/uploads/2026/03/image4-1.png 1999w, https://www.raspberrypi.org/app/uploads/2026/03/image4-1-300x63.png 300w, https://www.raspberrypi.org/app/uploads/2026/03/image4-1-500x105.png 500w, https://www.raspberrypi.org/app/uploads/2026/03/image4-1-768x162.png 768w, https://www.raspberrypi.org/app/uploads/2026/03/image4-1-1536x323.png 1536w, https://www.raspberrypi.org/app/uploads/2026/03/image4-1-800x168.png 800w, https://www.raspberrypi.org/app/uploads/2026/03/image4-1-450x95.png 450w, https://www.raspberrypi.org/app/uploads/2026/03/image4-1-900x190.png 900w" sizes="auto, (max-width: 1999px) 100vw, 1999px" /> Students using ropes to signify the strength of connections between nodes. Using farm data, students can learn how neural networks work, and they can do this through an unplugged role play — using ropes! Here’s how Salomey’s classroom activity works. Sets of students act out the processes of training a neural network, including forward propagation, evaluation, and backpropagation. They take on the “roles’’ of some of the concepts of a neural network. One student acts as the supervisor, six students act as the input layer, two as the hidden layer, and one as the output layer. Keeping it simple: Concepts and data Key concepts are simplified for students: Forward propagation: The hidden layer players randomly select a set of farms (three of the six sets of input values), which reflects how weights are often set to random values at the start. Evaluation: The student acting as the output layer compares the prediction (whether crop disease is present or not) to the actual value for the farm to assess the error, similar to a loss function. Backpropagation: Inspired by MIT’s RAISE curriculum, this stage is modelled on establishing trust. Players in the hidden and output layers modify their trust in the previous layers (by adding or removing ropes) based on the accuracy of the prediction (if the farm has disease). Simple numerical data about the features of the problem are given to the students, such as whether the “Temperature” is suitable (0=No, 1=Yes), if there are “Spots” on the plant (0=No, 1=Yes), if “Fertilizer’’ has been used, whether the “Leaf colour’’ is green or not (see Table 1). Importantly, each of the six features given are represented by the six “input layer” students. So each student can ‘process’ each feature as the data for a given farm is used to train the model. Cards are used to represent the data values passed between layers. And this is where the ropes come into play, as they are used to represent the connections between the nodes in the layers. <img loading="lazy" decoding="async" width="1999" height="980" src="https://www.raspberrypi.org/app/uploads/2026/03/image5.png" alt="Table 1: This data table was given to the student assigned the “Supervisor” role in each group and contains both relevant and irrelevant data to “train” their neural network." class="wp-image-92649" srcset="https://www.raspberrypi.org/app/uploads/2026/03/image5.png 1999w, https://www.raspberrypi.org/app/uploads/2026/03/image5-300x147.png 300w, https://www.raspberrypi.org/app/uploads/2026/03/image5-500x245.png 500w, https://www.raspberrypi.org/app/uploads/2026/03/image5-768x377.png 768w, https://www.raspberrypi.org/app/uploads/2026/03/image5-1536x753.png 1536w, https://www.raspberrypi.org/app/uploads/2026/03/image5-800x392.png 800w, https://www.raspberrypi.org/app/uploads/2026/03/image5-450x221.png 450w, https://www.raspberrypi.org/app/uploads/2026/03/image5-900x441.png 900w" sizes="auto, (max-width: 1999px) 100vw, 1999px" /> Table 1: This data table was given to the student assigned the “Supervisor” role in each group and contains both relevant and irrelevant data to “train” their neural network. Instructions for each role Written role-specific instructions are provided for the students to follow, for example, the Supervisor is given three steps to follow for the forward propagation stage, and the Input Layer students receive a different set of instructions and so on. The detail of the role play is shown in the instruction sheets (see Figure 1). <img loading="lazy" decoding="async" width="1019" height="1221" src="https://www.raspberrypi.org/app/uploads/2026/03/image1.png" alt="" class="wp-image-92650" style="aspect-ratio:0.834575260804769;width:762px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/03/image1.png 1019w, https://www.raspberrypi.org/app/uploads/2026/03/image1-209x250.png 209w, https://www.raspberrypi.org/app/uploads/2026/03/image1-500x599.png 500w, https://www.raspberrypi.org/app/uploads/2026/03/image1-768x920.png 768w, https://www.raspberrypi.org/app/uploads/2026/03/image1-800x959.png 800w, https://www.raspberrypi.org/app/uploads/2026/03/image1-450x539.png 450w, https://www.raspberrypi.org/app/uploads/2026/03/image1-900x1078.png 900w, https://www.raspberrypi.org/app/uploads/2026/03/image1-168x202.png 168w" sizes="auto, (max-width: 1019px) 100vw, 1019px" /> Figure 1: Detailed explanation of the eight steps of the role-play activity that Salomey developed. Click to enlarge. Why the ropes are important Using ropes to connect the nodes becomes most important at the reverse propagation stage. The clever part of this is that we can show an increase or decrease in the strength of connection by adding or removing ropes. For me, this is the ‘jam sandwich’ effect. This, I think, is probably the most significant learning point. Here, the number of ropes that connect the nodes in the layers are changed based on the strength of evidence that a particular feature is indicated, by the data, to be relevant to the output. In this case, whether “Temperature”, for example, has an implied effect on cocoa disease or not — based on the data, not on any preconceived rule. Simply put, if a farm did have disease then a rope is added, if a farm did not then a rope is removed. Or at a more abstracted level, if a particular neuron contributes towards the correct prediction, a rope is added, otherwise a rope is removed. In a real neural network, backpropagation involves complex maths, such as calculus that would not be accessible to students of this age. Therefore, the rope is an analogy that replaces something that would be impossible for these students to grasp if it was taught using the real-world implementation. Problem to be solved in the unplugged activity: Identify features that are relevant for detecting diseases At the end of the activity, features (temperature, leaf color, family farm, etc.) with many rope connections are considered to be relevant for crop disease detection on the farm, whereas features with fewer rope connections are considered to be irrelevant for crop disease detection. The more ropes attached to a particular feature, e.g., temperature, represent its higher relevance in identifying crop disease on the farm. Activity design, follow-on and evaluation As part of the design of this activity, Salomey has simplified technical language so that throughout the role play students use everyday terms and she has chosen a context that is relatable for the students. For example, she uses the language of trust, and the new thickness of a rope connection, rather than using technical terms such as weight, loss function, and the error of the network. Salomey also designed a follow-on activity that uses pen and paper. In this version of the activity, which she calls a board game, the students draw lines to connect the nodes in the layers. The thickness of the lines connecting the nodes represent the strength of the trust (see Figure 2). <img loading="lazy" decoding="async" width="1330" height="1024" src="https://www.raspberrypi.org/app/uploads/2026/03/image2.png" alt="" class="wp-image-92657" style="aspect-ratio:1.2988644600144963;width:692px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/03/image2.png 1330w, https://www.raspberrypi.org/app/uploads/2026/03/image2-300x231.png 300w, https://www.raspberrypi.org/app/uploads/2026/03/image2-500x385.png 500w, https://www.raspberrypi.org/app/uploads/2026/03/image2-768x591.png 768w, https://www.raspberrypi.org/app/uploads/2026/03/image2-800x616.png 800w, https://www.raspberrypi.org/app/uploads/2026/03/image2-450x346.png 450w, https://www.raspberrypi.org/app/uploads/2026/03/image2-900x693.png 900w" sizes="auto, (max-width: 1330px) 100vw, 1330px" /> Figure 2: An example of how students use the board game that Salomey designed to teach about neural networks, where the thickness of the lines between nodes represents the strength of trust. Salomey also shared her evaluation of the resources. She conducted pre‑ and post‑intervention surveys with 39 teachers as part of the professional development on the AI teaching materials, and ten of those teachers implemented the unplugged activities in their classrooms. She reported that the teachers found the role-play activity was effective to demonstrate neural networks, that children worked independently to learn, and that some students who did not take part usually in class were engaged. As well as sharing about her unplugged neural network activity, Salomey also talked about a set of AI stories that she has developed to teach about other aspects of AI applications. For example, the importance of fact-checking is demonstrated through a story about a young girl who fact-checked information she received from her friends about life in a city. If you would like to find out more about Salomey’s work, you can find related materials on our seminar website. Join our next seminar Join us at our next seminar on Tuesday 17 March from 17:00 to 18:30 GMT to hear Rebecca Fiebrink (University of the Arts London) speak about teaching AI for creative practitioners. This will be the second seminar in our new series on how to teach about AI across disciplines. We hope to see you there! To sign up and take part in our research seminars, click below: I want to join the next seminar You can also view the schedule of our upcoming seminars, and catch up on past seminars on our previous seminars page. The post Do you have some rope? Then let’s teach about AI concepts appeared first on Raspberry Pi Foundation.
‘Using PRIMM to teach programming’: A new short course for educators
At the Raspberry Pi Foundation, we believe that learning to program equips young people with the knowledge and skills they need to thrive in an increasingly digital world. For many educators, teaching programming effectively can be challenging, particularly when their learners are at different stages in their programming journey. Ask learners to write code too… The post ‘Using PRIMM to teach programming’: A new short course for educators appeared first on Raspberry Pi Foundation.
At the Raspberry Pi Foundation, we believe that learning to program equips young people with the knowledge and skills they need to thrive in an increasingly digital world. For many educators, teaching programming effectively can be challenging, particularly when their learners are at different stages in their programming journey. Ask learners to write code too early, and they might struggle or feel intimidated. Rely too heavily on step-by-step instructions, and you limit learners’ chances to explore ideas or develop deeper understanding. <img loading="lazy" decoding="async" width="600" height="338" src="https://www.raspberrypi.org/app/uploads/2026/02/11038_OC_PRIMM_1.png" alt="Using PRIMM to teach programming artwork" class="wp-image-92630" style="aspect-ratio:1.7752067341565618;width:784px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/11038_OC_PRIMM_1.png 600w, https://www.raspberrypi.org/app/uploads/2026/02/11038_OC_PRIMM_1-300x169.png 300w, https://www.raspberrypi.org/app/uploads/2026/02/11038_OC_PRIMM_1-500x282.png 500w, https://www.raspberrypi.org/app/uploads/2026/02/11038_OC_PRIMM_1-450x254.png 450w" sizes="auto, (max-width: 600px) 100vw, 600px" /> The PRIMM framework — Predict, Run, Investigate, Modify, Make — provides educators with a structure for teaching programming. This research-informed teaching approach balances support with independence and helps learners build their understanding before they write their own code, whatever their starting point. To help educators use this approach confidently, we have launched a new short online course, Using PRIMM to teach programming, which is available on our new Training Hub platform for free. What is the course about? This practical, self-paced course gives educators the knowledge they need to use the PRIMM approach to design and adapt programming activities to suit their learners. The course takes 1–2 hours to complete, and we have designed it for educators working in formal or non-formal learning environments around the world, using any block-based or text-based programming language. All you need is some experience of creating and adapting simple programs. The course starts with considering the five stages of PRIMM, when and why to use each stage, and how they work together to support learning. It covers how PRIMM aligns with key teaching principles such as scaffolding, managing cognitive load, and progression, and examines how the approach supports formative assessment by making learners’ thinking — and any misunderstandings — more visible. Active, social learning Although pedagogy forms the core of this course, we have deliberately avoided a theory-heavy approach. Instead, the course is designed to help you learn through hands-on activities. By reflecting, taking part in discussions with other computing educators, and completing practical tasks, you will explore how PRIMM works in real teaching contexts. <img loading="lazy" decoding="async" width="800" height="533" src="https://www.raspberrypi.org/app/uploads/2023/06/Spencer_10-1-800x533.jpg" alt="A computer science teacher sits with students at computers in a classroom." class="wp-image-84333" srcset="https://www.raspberrypi.org/app/uploads/2023/06/Spencer_10-1-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2023/06/Spencer_10-1-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2023/06/Spencer_10-1-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2023/06/Spencer_10-1-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2023/06/Spencer_10-1-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2023/06/Spencer_10-1-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2023/06/Spencer_10-1-900x600.jpg 900w, https://www.raspberrypi.org/app/uploads/2023/06/Spencer_10-1.jpg 2000w" sizes="auto, (max-width: 800px) 100vw, 800px" /> After an introduction to the core ideas of PRIMM, you will design a new programming activity, or adapt an existing one, using the PRIMM structure. This will support you to think carefully about what your learners know and can do, likely misconceptions, and how each stage of PRIMM can be used effectively, including when your learners have varied learning needs and levels of programming experience. With its emphasis on activity design, the course will support you to develop resources you can use and keep adapting in your own setting. By the end, you will have a complete PRIMM activity designed specifically for your learners, and a clear sense of how to teach programming in a structured and supportive way. Join the course on the Training Hub Using PRIMM to teach programming is available on our new Training Hub, where we offer all our professional development courses for free. The Training Hub offers flexible, reflective learning experiences across a range of topics, helping you build your subject knowledge and bring research-informed teaching approaches into your day-to-day practice. I want to join the course Whether you are an experienced computing teacher, a volunteer educator, or a parent looking to support their child’s learning, we invite you to join us there. The post ‘Using PRIMM to teach programming’: A new short course for educators appeared first on Raspberry Pi Foundation.
Measuring student understanding in computing education is not an easy task. As AI literacy becomes an important pillar in computing education, defining and accurately measuring students’ understanding of concepts and their skills is an even greater challenge. In a recent seminar in our series on teaching about AI and data science, researcher Jesús Moreno-León (Universidad… The post The challenges of measuring AI literacy appeared first on Raspberry Pi Foundation.
Measuring student understanding in computing education is not an easy task. As AI literacy becomes an important pillar in computing education, defining and accurately measuring students’ understanding of concepts and their skills is an even greater challenge. <img loading="lazy" decoding="async" width="500" height="324" src="https://www.raspberrypi.org/app/uploads/2020/11/BARROWHILL_CC4576IT031319-500x324.jpg" alt="A girl doing Scratch coding in a Code Club classroom" class="wp-image-65202" srcset="https://www.raspberrypi.org/app/uploads/2020/11/BARROWHILL_CC4576IT031319-500x324.jpg 500w, https://www.raspberrypi.org/app/uploads/2020/11/BARROWHILL_CC4576IT031319-300x194.jpg 300w, https://www.raspberrypi.org/app/uploads/2020/11/BARROWHILL_CC4576IT031319-768x497.jpg 768w, https://www.raspberrypi.org/app/uploads/2020/11/BARROWHILL_CC4576IT031319-1536x994.jpg 1536w, https://www.raspberrypi.org/app/uploads/2020/11/BARROWHILL_CC4576IT031319-2048x1325.jpg 2048w, https://www.raspberrypi.org/app/uploads/2020/11/BARROWHILL_CC4576IT031319-800x518.jpg 800w, https://www.raspberrypi.org/app/uploads/2020/11/BARROWHILL_CC4576IT031319-scaled-450x291.jpg 450w, https://www.raspberrypi.org/app/uploads/2020/11/BARROWHILL_CC4576IT031319-scaled-900x583.jpg 900w" sizes="auto, (max-width: 500px) 100vw, 500px" /> In a recent seminar in our series on teaching about AI and data science, researcher Jesús Moreno-León (Universidad de Sevilla) talked about his work in developing assessment tools for computational thinking (CT) and AI literacy. Jesús is also co-founder of Programamos, a non-profit organisation that promotes the development of computational thinking, supporting teachers through training and sharing resources. <img loading="lazy" decoding="async" width="608" height="628" src="https://www.raspberrypi.org/app/uploads/2026/02/image4.jpg" alt="Jesús Moreno-León (Universidad de Sevilla/Programamos)." class="wp-image-92600" style="width:446px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/image4.jpg 608w, https://www.raspberrypi.org/app/uploads/2026/02/image4-242x250.jpg 242w, https://www.raspberrypi.org/app/uploads/2026/02/image4-500x516.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/02/image4-450x465.jpg 450w" sizes="auto, (max-width: 608px) 100vw, 608px" /> Jesús Moreno-León (Universidad de Sevilla/Programamos) Developing assessment tools in computer science Jesús began by discussing the recent development of computer science assessment tools. Together with Gregorio Robles (Universidad Rey Juan Carlos), they created Dr Scratch, a web-based tool to assess the quality of Scratch projects and detect errors and bad programming habits (e.g. dead code). Projects are scored on the use of computational thinking concepts (e.g. parallelism, conditional logic) and the use of desirable programming practices (e.g. naming sprites, removing duplicate scripts) in order to give feedback to students and teachers to iteratively improve their Scratch projects. <img loading="lazy" decoding="async" width="600" height="351" src="https://www.raspberrypi.org/app/uploads/2026/02/image3-1.png" alt="Dr Scratch tool." class="wp-image-92601" style="aspect-ratio:1.7094343222360011;width:770px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/image3-1.png 600w, https://www.raspberrypi.org/app/uploads/2026/02/image3-1-300x176.png 300w, https://www.raspberrypi.org/app/uploads/2026/02/image3-1-500x293.png 500w, https://www.raspberrypi.org/app/uploads/2026/02/image3-1-450x263.png 450w" sizes="auto, (max-width: 600px) 100vw, 600px" /> Dr Scratch tool. Alongside measuring students’ programming skills, Jesús also shared work by Marcos Román-González (Universidad Nacional de Educación a Distancia) to develop the Computational Thinking test (CTt), a 28-item assessment tool designed to measure the computational thinking skills of students aged 10 to 16 years old. Two collaborators, María Zapata and Estafanía Martín (Universidad Rey Juan Carlos) further adapted these items to create the Beginners Computational Thinking test (or BCTt), an unplugged assessment suitable for younger learners aged 5 to 10 years old. Teaching about AI in Spain Jesús also described his more recent work at the Ministry of Education and Vocational Training in Spain to promote computer science at all educational levels. One initiative, La Escuela de Pensamiento Computacional e Inteligencia Artificial (or the School of Computational Thinking and Artificial Intelligence), supported Spanish teachers through training and resources to introduce CT and AI into the classroom. Over 400 teachers and 7000 teachers took part across Spain through unplugged activities and tools such as Machine Learning for Kids and LearningML, allowing students to classify text and images using machine learning. Older students created apps using the MIT App Inventor. When evaluating the design of the curriculum, they found they had strong instruments to measure the development of CT — such as the assessment tools described above — yet nothing to measure AI literacy. <img loading="lazy" decoding="async" width="600" height="337" src="https://www.raspberrypi.org/app/uploads/2026/02/image2-1.png" alt="" class="wp-image-92602" style="width:728px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/image2-1.png 600w, https://www.raspberrypi.org/app/uploads/2026/02/image2-1-300x169.png 300w, https://www.raspberrypi.org/app/uploads/2026/02/image2-1-500x281.png 500w, https://www.raspberrypi.org/app/uploads/2026/02/image2-1-450x253.png 450w" sizes="auto, (max-width: 600px) 100vw, 600px" /> The School of Computational Thinking and Artificial Intelligence curriculum. A tool for measuring AI literacy The lack of valid AI literacy assessment tools led the team to develop the AI Knowledge Test (or AIKT), a 14-item survey consisting of multiple-choice questions designed to measure students’ understanding of AI. The instrument was inspired by previous work in the field and relevant research (e.g. the AI4K12 framework). An example from the AI Knowledge Test An example of one of these items is presented below. Can you solve it? The answer is at the bottom of this article. Q1. Which of the following strategies would be most appropriate for teaching a computer to recognise photos of apples? Train the computer with photos of dogs Train the computer with several photos of different apples, taken in different places and contexts Train the computer with several similar photos of the same apple, taken in the same place Train the computer with several identical copies of the same photo of an apple Testing the test In a study on the impact of programming activities on computational thinking and AI literacy in Spanish schools, the authors tested these knowledge-based items with over 2000 students to assess the reliability (e.g. internal consistency), or a measure of the quality of a survey or test. They found one item (“As a user, the legal regulation that is approved regarding AI systems will affect my life”) did not correlate with the other items. This left a total of 13 items which were found to have sufficient internal consistency — meaning how well each item correlated with one another to measure an underlying construct (i.e. “AI knowledge”). They concluded that the assessment tool needed a higher ceiling and needed to address common misconceptions. The authors also learned that teachers needed free and open-source tools with low barriers for entry, such as not needing registration, and were suitable for classroom use, such as limiting data sent to the cloud. AI literacy in the generative era With the rise of generative AI tools like ChatGPT or Google’s Gemini, Jesús and his colleagues felt their AI literacy assessment tool needed to focus on the capabilities of generative AI tools. They also felt they needed to take a broader view of AI and focus on additional dimensions, such as the social and ethical implications of AI tools. They are, therefore, currently revising their assessment items to align with several common frameworks, including the SEAME framework and AI Learning Priorities for All K–12 Students. An example from the revised AI Knowledge Test One of the revised items is presented below. Can you solve it? The answer is revealed below. Q2. You have asked your students to design a decision tree to classify different fruits based on three characteristics: color, size, and shape. To check whether the following proposed solution is correct, you are going to test it. As what fruit does the decision tree classify a small, round, yellow apple? Apple Watermelon Lemon Banana <img loading="lazy" decoding="async" width="1424" height="974" src="https://www.raspberrypi.org/app/uploads/2026/02/image1.png" alt="A decision tree to classify fruit." class="wp-image-92604" style="aspect-ratio:1.4620614631493065;width:652px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/image1.png 1424w, https://www.raspberrypi.org/app/uploads/2026/02/image1-300x205.png 300w, https://www.raspberrypi.org/app/uploads/2026/02/image1-500x342.png 500w, https://www.raspberrypi.org/app/uploads/2026/02/image1-768x525.png 768w, https://www.raspberrypi.org/app/uploads/2026/02/image1-800x547.png 800w, https://www.raspberrypi.org/app/uploads/2026/02/image1-450x308.png 450w, https://www.raspberrypi.org/app/uploads/2026/02/image1-900x616.png 900w" sizes="auto, (max-width: 1424px) 100vw, 1424px" /> Learn more about this work Jesús concluded the seminar by describing his intentions to collaborate with others to test the revised AI literacy instrument with students in early 2026. We look forward to hearing about their results! You can watch Jesús’s whole seminar here: If you are interested to learn more about Jesús and his work, you can read about his development of the AI Knowledge Test (or AIKT) here and the <a href="https://d1wqtxts1xzle7.cloudfront.net/84654280/151396-libre.pdf?1650599468=&response-content-disposition=inline%3B+filename%3DWhich_cognitive_abilities_underlie_compu.pdf&Expires=1768320057&Signature=EER2TD1hqLeEObUMZ2gHS7akOxxvAc6dbcehs8oEFMJkj0uhAmonwgZvFW0LXe7JltJq9nJnTrRHXwseP477O5VJtBAAyeGiU0B8olmYBIWK-USFlhZpBapKOff5eDz3FDx3DlF1fC2WHxwAIB7fQb2FDTuXtY0KetfOkEnS9~NRgtRuLbEZbtPrSa-9QU5q93rS~NYtzECFScMJ90zHWUzLSUMVWa~8aedmnfgooqvBlD1pGtvRRkGaSvqA01OMzhC3oQU6qikPX-e9C0u3yO3DBh06xUv5qBs66w0rOjA1lQftlAq6K0MXYdKnyjAO4f2atYVEWT4r-8EFAQDnpw__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA" target="_blank" rel="noreferrer noopener">Computational Thinking test (CTt) here or look at the original items here. You can also learn about the Beginners Computational Thinking test (BCTt) by watching a Raspberry Pi research seminar about it or reading about it here. Join our next seminar In our current seminar series, we’re exploring applied AI and how AI can be taught across the curriculum. In our next seminar in this series on 17 March at 17.00 UK time, we welcome Rebecca Fiebrink (University of the Arts London) who will explore the questions of how and why we might teach AI for creative practitioners, including children, students, and professionals. To take part in the seminar, click the button below to register. We hope to see you there. Register for the next seminar The schedule of our upcoming seminars is available online. You can catch up on past seminars on our blog and on the previous seminars and recordings page. Answers Q1: 2 Q2: 3 The post The challenges of measuring AI literacy appeared first on Raspberry Pi Foundation.
Levelling up with Python: Create with data
Learning Python often starts with the same building blocks: variables, functions, and loops. However, once young people have learnt these essential foundations, they may be eager to grow their skills and start using Python to explore data and create something meaningful to them. Our free ‘More Python’ project path helps learners move beyond the basics… The post Levelling up with Python: Create with data appeared first on Raspberry Pi Foundation.
Learning Python often starts with the same building blocks: variables, functions, and loops. However, once young people have learnt these essential foundations, they may be eager to grow their skills and start using Python to explore data and create something meaningful to them. <img loading="lazy" decoding="async" width="800" height="522" src="https://www.raspberrypi.org/app/uploads/2026/02/CC_York-05-800x522.jpg" alt="A young learner showing a Python project in the Code Editor." class="wp-image-92583" style="aspect-ratio:1.5314494074749316;width:774px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/CC_York-05-800x522.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-05-300x196.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-05-500x327.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-05-768x502.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-05-1536x1003.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-05-450x294.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-05-900x588.jpg 900w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-05.jpg 2000w" sizes="auto, (max-width: 800px) 100vw, 800px" /> Our free ‘More Python’ project path helps learners move beyond the basics and use data to create impactful projects of their own. Python as a tool for exploring the world Python is the most widely used programming language in the world, not just because it’s accessible, but because it’s powerful. It is used to analyse data, build models, create data visualisations, and explore important questions. <img loading="lazy" decoding="async" width="800" height="533" src="https://www.raspberrypi.org/app/uploads/2026/02/CC_York-48-800x533.jpg" alt="A young learners is excited about his Python project." class="wp-image-92577" style="aspect-ratio:1.500418643594753;width:750px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/CC_York-48-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-48-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-48-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-48-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-48-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-48-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-48-900x600.jpg 900w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-48.jpg 2000w" sizes="auto, (max-width: 800px) 100vw, 800px" /> For young learners, this means learning Python can become more than a coding exercise. It can be a way to investigate topics they care about, analyse and understand information, and tell powerful stories about real-world issues. <img loading="lazy" decoding="async" width="688" height="361" src="https://www.raspberrypi.org/app/uploads/2026/02/banner-blog.png" alt="A illustration featuring examples of different types of graphs: a line graph, a bar chart, and a venn diagram." class="wp-image-92579" style="aspect-ratio:1.9058521007009248;width:760px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/banner-blog.png 688w, https://www.raspberrypi.org/app/uploads/2026/02/banner-blog-300x157.png 300w, https://www.raspberrypi.org/app/uploads/2026/02/banner-blog-500x262.png 500w, https://www.raspberrypi.org/app/uploads/2026/02/banner-blog-450x236.png 450w" sizes="auto, (max-width: 688px) 100vw, 688px" /> Working with data helps learners see how coding connects to the world around them — and builds confidence along the way. Why learning with data matters In our day-to-day lives, data is everywhere: in sports results, maps, and scientific research, to name only a few examples. Learning how to work with data helps young people develop skills that go far beyond programming, including: Thinking logically and solving problems Interpreting and questioning information Making decisions based on evidence Data also underpins many of the AI systems people use today. For example, large language models, used to build tools such as ChatGPT, are trained on vast amounts of data. Therefore, understanding how data is collected, organised, and used is an important part of AI literacy. In Python, structures like lists and dictionaries make it possible to organise, analyse, and explore data in creative ways. Using these tools to build projects can help abstract computing concepts start to feel more concrete and meaningful. What learners create in the ‘More Python’ project path The ‘More Python’ project path supports learners through three stages: Explore, Design, and Invent. Each stage builds skills while giving learners more ownership over what they create. In the Explore stage, young people learn new concepts and build confidence in using data and core Python structures, such as lists and dictionaries. Projects include: Making an interactive chart of Olympic medals Building a model of the solar system Creating a frequency graph that learners can analyse to crack a code These projects help learners develop new skills, while exploring how Python can be used to analyse and explain real-world information. <img loading="lazy" decoding="async" width="800" height="533" src="https://www.raspberrypi.org/app/uploads/2026/02/CC_York-24-800x533.jpg" alt="A young learner uses the Code Club Projects site on computer to do Python coding." class="wp-image-92584" style="aspect-ratio:1.500418643594753;width:778px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/CC_York-24-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-24-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-24-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-24-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-24-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-24-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-24-900x600.jpg 900w, https://www.raspberrypi.org/app/uploads/2026/02/CC_York-24.jpg 2000w" sizes="auto, (max-width: 800px) 100vw, 800px" /> As learners progress to the Design stage, they start making creative choices about how their projects look and behave. In this stage, they: Create a project that produces encoded art based on a user’s name Build an interactive world map that helps users learn interesting facts Here, Python becomes a creative medium. As well as putting their new skills into practice, learners think about audience, interaction, and presentation to make their projects their own. In the Invent stage, learners bring everything together. Using the skills they have built, they design and create a data visualisation on a topic they are passionate about. This final project gives learners the freedom to choose their data, shape their idea, and tell a story that matters to them. <img loading="lazy" decoding="async" width="688" height="361" src="https://www.raspberrypi.org/app/uploads/2026/02/banner-blog-2.png" alt="An illustration of a robot on wheels." class="wp-image-92585" style="width:772px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/banner-blog-2.png 688w, https://www.raspberrypi.org/app/uploads/2026/02/banner-blog-2-300x157.png 300w, https://www.raspberrypi.org/app/uploads/2026/02/banner-blog-2-500x262.png 500w, https://www.raspberrypi.org/app/uploads/2026/02/banner-blog-2-450x236.png 450w" sizes="auto, (max-width: 688px) 100vw, 688px" /> By this point, learners are planning and creating their own projects, growing in confidence and independence. Take the next step with Python If the young people you support have already learned the basics of Python, ‘More Python’ offers a clear and creative next step. The projects are designed to be accessible, and young people can work through them at their own pace, whether they are learning independently, at a Code Club, or in the classroom. By working with data, getting creative, and making their own original projects, learners can build confidence and start to see what they can achieve with Python. Explore the ‘More Python’ project path Alongside the ‘More Python’ project path, you can access hundreds of free coding projects on our Code Club Projects site. Find more projects to suit your learners’ interests, and support them to build their digital skills through creativity and making. The post Levelling up with Python: Create with data appeared first on Raspberry Pi Foundation.
Embodied machine learning: From research ideas to classroom activities
Where do great research ideas come from in computer science education? We might think of research breakthroughs as a single moment of genius, but in reality impactful research is often the result of many years of iterative development. In November’s research seminar, we heard from Karl-Emil Kjær Bilstrup, a researcher at the University of Copenhagen,… The post Embodied machine learning: From research ideas to classroom activities appeared first on Raspberry Pi Foundation.
Where do great research ideas come from in computer science education? We might think of research breakthroughs as a single moment of genius, but in reality impactful research is often the result of many years of iterative development. In November’s research seminar, we heard from Karl-Emil Kjær Bilstrup, a researcher at the University of Copenhagen, about his work to develop ML-Machine. This work uses embodied learning principles and the BBC micro:bit to introduce learners to machine learning concepts. Findings from this research have been used to develop the micro:bit CreateAI resources, and in this blog, we will explain the research journey from initial small-scale work to educational resources used by many young learners around the world. <img loading="lazy" decoding="async" width="1300" height="731" src="https://www.raspberrypi.org/app/uploads/2026/02/image1.jpg" alt="Karl-Emil Kjær Bilstrup, a tool designer and researcher from the University of Copenhagen" class="wp-image-92556" style="aspect-ratio:1.7784246915213868;width:786px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/image1.jpg 1300w, https://www.raspberrypi.org/app/uploads/2026/02/image1-300x169.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/02/image1-500x281.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/02/image1-768x432.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/02/image1-800x450.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/02/image1-450x253.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/02/image1-900x506.jpg 900w" sizes="auto, (max-width: 1300px) 100vw, 1300px" /> Karl-Emil Kjær Bilstrup, a tool designer and researcher from the University of Copenhagen From hypothetical ethics to concrete machines In Karl-Emil’s first research study, students used prompt cards to develop ideas for machine learning applications that could solve real-world problems, and to discuss the ethical dilemmas associated with their solutions. Students found it difficult to address these ethical dilemmas in their designs; for example, their ideas often featured a trade-off of user privacy. The findings from this research informed Karl-Emil’s next study, which moved from hypothetical scenarios to implementing machine learning in real-world settings. The ‘Machine Learning Machine’ study made machine learning processes tangible for students through the use of two physical boxes, shown in the picture below. Students created drawings and fed them into the first box to train a model, and then tested the model by placing new drawings under a camera in the second box and having the model produce predictions of what the drawings showed. For example, students could draw pictures of the sun to represent daytime and the moon to represent nighttime to train a model to predict whether new drawings represented day or night. The machine was built for slow interaction, giving students time to think about the concepts and practices that they were developing. In a follow-up study, a new version of the Machine Learning Machine had been designed, which was controlled using a graphical user interface (GUI). This allowed users to “unbox” and influence parts of the machine learning process. For example, students could adjust the number of complete passes (called ‘epochs’) through the training data to improve the model’s accuracy. <img loading="lazy" decoding="async" width="772" height="314" src="https://www.raspberrypi.org/app/uploads/2026/02/image3.png" alt="The two components of the Machine Learning Machine: the training box (left) and the evaluator box (right) " class="wp-image-92558" style="width:698px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/image3.png 772w, https://www.raspberrypi.org/app/uploads/2026/02/image3-300x122.png 300w, https://www.raspberrypi.org/app/uploads/2026/02/image3-500x203.png 500w, https://www.raspberrypi.org/app/uploads/2026/02/image3-768x312.png 768w, https://www.raspberrypi.org/app/uploads/2026/02/image3-450x183.png 450w" sizes="auto, (max-width: 772px) 100vw, 772px" /> The two components of the Machine Learning Machine: the training box (left) and the evaluator box (right) The two studies with the Machine Learning Machines provided many useful findings for teaching about machine learning with K–12 (primary and secondary) learners. However, two constraints remained: firstly, there were limited opportunities for whole-class work because there was only one Machine Learning Machine, and secondly, learning experiences needed to be better connected to examples from students’ daily lives. As a result, the next iteration in Karl-Emil’s research involved using the micro:bit, which ensured access to a tangible device for every student, and a new graphical platform called ML-Machine that students could interact with. Machine learning and the micro:bit The micro:bit is a small, programmable computing device that features sensors to gather data from the immediate environment. For example, the accelerometer is a motion sensor that can detect when the micro:bit is tilted from left to right, backwards and forwards, and up and down. Using the micro:bit with ML-Machine and some common household objects, students can create simple machine learning models that use data from the micro:bit’s accelerometer to detect whether the micro:bit is moving. This is a very different approach from rule-based programs on the micro:bit, where students might use programming constructs such as if statements to detect movement if the numerical reading from the accelerometer is above a certain value. Here, a machine learning model trained using a set of 20 examples is used to analyse live data readings and produce predictions about whether the micro:bit is moving. <img loading="lazy" decoding="async" width="808" height="367" src="https://www.raspberrypi.org/app/uploads/2026/02/image2.png" alt="A visualisation of a simple machine learning model to detect whether a micro:bit is being shaken or is still" class="wp-image-92560" style="width:772px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/image2.png 808w, https://www.raspberrypi.org/app/uploads/2026/02/image2-300x136.png 300w, https://www.raspberrypi.org/app/uploads/2026/02/image2-500x227.png 500w, https://www.raspberrypi.org/app/uploads/2026/02/image2-768x349.png 768w, https://www.raspberrypi.org/app/uploads/2026/02/image2-800x363.png 800w, https://www.raspberrypi.org/app/uploads/2026/02/image2-450x204.png 450w" sizes="auto, (max-width: 808px) 100vw, 808px" /> A visualisation of a simple machine learning model to detect whether a micro:bit is being shaken or is still In our seminar, Karl-Emil gave a live demonstration of the ML-Machine toolkit, so we highly recommend watching the recording to see how this toolkit brings machine learning concepts to life. ML-Machine is the precursor to the micro:bit CreateAI resources, and the software is fully open-source. However, the innovation doesn’t stop there: Karl-Emil also explained that he is currently developing a new tool called math.ml-machine.org, where students can train a neural network and see a visualised k-nearest neighbour model to explore how a model makes predictions. The research journey is continuing, with new possibilities for educational opportunities to teach about machine learning. Embodied learning The idea of embodied learning is interwoven throughout all of Karl-Emil’s research projects and is a cornerstone of all of his work. Embodied learning suggests that we learn more effectively when our whole body is involved in the learning process, not just our minds. For example, in the work described in this seminar, the Machine Learning Machines and the micro:bit were all tangible devices that students could touch and see. Embodied learning is particularly important in activities that involve working with data-driven systems. In traditional programming activities, the flow of code can be traced transparently through a program. However, machine learning models are more opaque, and their outputs cannot be traced step by step. Students can benefit from using bodily movements and sensorimotor information to help understand machine learning concepts. The ML-Machine toolkit was designed to support students to learn through embodied learning in three different ways: Enacting machine learning processes: Students used bodily movement to collect the data samples needed for the ML-Machine model to detect and predict gestures Using machine learning as a design material: Students created concrete ‘objects-to-think-with’, which helps form deeper connections to abstract concepts Embodied exploration of machine learning: Students experienced how their bodily movements were translated into data points on the screen <img loading="lazy" decoding="async" width="2560" height="1707" src="https://www.raspberrypi.org/app/uploads/2023/11/Spencer-5--scaled.jpg" alt="Secondary school age learners in a computing classroom." class="wp-image-85670" style="aspect-ratio:1.4997489259610557;width:740px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2023/11/Spencer-5--scaled.jpg 2560w, https://www.raspberrypi.org/app/uploads/2023/11/Spencer-5--300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2023/11/Spencer-5--500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2023/11/Spencer-5--768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2023/11/Spencer-5--1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2023/11/Spencer-5--2048x1365.jpg 2048w, https://www.raspberrypi.org/app/uploads/2023/11/Spencer-5--800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2023/11/Spencer-5--450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2023/11/Spencer-5--900x600.jpg 900w" sizes="auto, (max-width: 2560px) 100vw, 2560px" /> Embodied learning helped students grasp concepts such as data quality. They could see how their bodily movements were being translated into digital data, and could spot when movements that appeared different to them were being classified as similar by the ML-Machine model. One case study participant described that the immediate feedback on screen made the concept of machine learning feel as if it were “coming to life as they [the students] manipulate something themselves and they’ve got control over it”. Find out more Karl-Emil’s work shows how research ideas can be used in the classroom through a cycle of discovery, design, and reflection. From prompt cards exploring ethics to tangible machines and the micro:bit-based ML-Machine, his research shows how embodied learning can make complex ideas like machine learning not only understandable, but deeply engaging for young learners. The micro:bit CreateAI resources are a great example of how research findings can evolve into accessible, hands-on tools that empower educators and students alike. As this work continues to grow, it invites us to imagine new ways for learners to experience machine learning not as abstract theory, but as something they can see, feel, and shape with their own hands. If you’d like to try out some of the ideas from this seminar, here are some useful resources: Explore machine learning projects using the micro:bit: micro:bit CreateAI and our Dance detector project are great places to start Find out more about the research: Read more about Karl-Emil’s work in this open-access paper Investigate new tools: Explore neural networks and k-nearest neighbours algorithms in the new maths-focused version of ML-Machine at math.ml-machine.org Join our next seminar Join us at our next seminar on Tuesday 17 March from 17:00 to 18:30 GMT to hear Rebecca Fiebrink (University of the Arts London speak about teaching AI for creative practitioners. This will be the second seminar in our new series on how to teach about AI across disciplines. We hope to see you there! To sign up and take part in our research seminars, click below: I want to join the next seminar You can also view the schedule of our upcoming seminars, and catch up on past seminars on our previous seminars page. The post Embodied machine learning: From research ideas to classroom activities appeared first on Raspberry Pi Foundation.
Helping young people stay safe online in the age of AI
The online world that young people navigate today is different from the one we encountered just a few years ago: the search engines, social media platforms and digital tools they use to find information, interact with friends and complete schoolwork are now deeply embedded with AI technologies. While the core aims of online safety education… The post Helping young people stay safe online in the age of AI appeared first on Raspberry Pi Foundation.
The online world that young people navigate today is different from the one we encountered just a few years ago: the search engines, social media platforms and digital tools they use to find information, interact with friends and complete schoolwork are now deeply embedded with AI technologies. <img loading="lazy" decoding="async" width="600" height="338" src="https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage1-1.png" alt="Experience AI Safety Image" class="wp-image-92537" style="width:690px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage1-1.png 600w, https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage1-1-300x169.png 300w, https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage1-1-500x282.png 500w, https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage1-1-450x254.png 450w" sizes="auto, (max-width: 600px) 100vw, 600px" /> While the core aims of online safety education remain the same, the scope must now expand to include AI literacy: the ability to use, question and navigate AI tools so young people can make responsible choices online. This is a shared challenge for anyone who supports young people as they navigate the online world: parents and carers, youth leaders and volunteers, and educators across all subjects. Many young people use these AI tools independently, often without guidance, so having open and useful conversations about trust, risk and responsibility matter just as much in the classroom as they do at dinner tables and Code Clubs. Why AI literacy is essential for staying safe online At the Raspberry Pi Foundation, we have developed various AI literacy resources for educators, club leaders and parents to address this challenge in age-appropriate and practical ways. Our ‘AI safety’ resources, part of our Experience AI programme, are a set of free comprehensive teaching activities to support you in educating young people aged 11–14 in navigating key safety issues linked to AI, including privacy, misinformation, trust and responsibility. Delivered through videos, unplugged activities and discussions, the activities are adaptable to a range of learning settings, and reflect the real decisions young people are already making online. <img loading="lazy" decoding="async" width="600" height="338" src="https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage2-1.png" alt="Experience AI safety image" class="wp-image-92538" style="width:746px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage2-1.png 600w, https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage2-1-300x169.png 300w, https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage2-1-500x282.png 500w, https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage2-1-450x254.png 450w" sizes="auto, (max-width: 600px) 100vw, 600px" /> For example, in the ‘Trusted Sources’ activity from the ‘Media literacy in the age of AI’ lesson, young people reflect on the ways they look for information related to schoolwork, news and in their free time. They consider which sources are likely to allow the use of generative AI and how that affects their trustworthiness. Rather than labelling sources as ‘good’ or ‘bad’, learners explore questions around responsibility, credibility and oversight, and build practical skills for fact-checking and staying safe online. Supporting responsible use of generative AI through Experience AI Alongside the ‘AI safety’ resources, we have also developed a new ‘Large Language Models (LLMs)’ unit for learners aged 11–13 and 14–17, currently being tested in classrooms. The unit focuses on another important aspect of online safety: how young people interact responsibly with AI tools that generate content. While helpful, learners’ uncritical use of these tools could lead to cognitive offloading and limit the development of their higher-order thinking skills. The confident, persuasive tone of LLMs can also make it harder for young people to judge accuracy, recognise bias or notice missing information in outputs. <img loading="lazy" decoding="async" width="600" height="338" src="https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage3-1.png" alt="Experience AI safety image" class="wp-image-92542" style="width:756px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage3-1.png 600w, https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage3-1-300x169.png 300w, https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage3-1-500x282.png 500w, https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage3-1-450x254.png 450w" sizes="auto, (max-width: 600px) 100vw, 600px" /> To support the critical thinking skills that are essential for staying safe online, the new LLM unit includes research-informed lessons that explore how LLMs are created, why their outputs are not always accurate and how to evaluate AI-generated responses. The unit also encourages learners to reflect on when using an LLM is helpful to their learning, when it is not, and how they can remain in control of their own thinking, learning and skills development. Starting the conversation this Safer Internet Day Helping young people stay safe online in the age of AI doesn’t require having all the answers. Instead, it’s about creating the space to pause, question, and think critically about what they are encountering online. Through carefully designed, research-informed and pedagogically aligned AI literacy resources, we aim to help you start the conversations that empower young people to think critically, stay curious and remain in control of their learning and online lives. <img loading="lazy" decoding="async" width="1920" height="1080" src="https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage4-1.png" alt="Experience AI safety image" class="wp-image-92544" style="width:742px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage4-1.png 1920w, https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage4-1-300x169.png 300w, https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage4-1-500x281.png 500w, https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage4-1-768x432.png 768w, https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage4-1-1536x864.png 1536w, https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage4-1-800x450.png 800w, https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage4-1-450x253.png 450w, https://www.raspberrypi.org/app/uploads/2026/02/AISAfetyImage4-1-900x506.png 900w" sizes="auto, (max-width: 1920px) 100vw, 1920px" /> This Safer Internet Day, we invite educators, parents and anyone who supports young people to explore our AI literacy resources and start the conversation. Visit the Experience AI website for more information. The post Helping young people stay safe online in the age of AI appeared first on Raspberry Pi Foundation.
Join our new study on AI and data-driven computing in UK primary classrooms
Raspberry Pi Computing Education Research Centre
Are you a primary school teacher in England, Scotland or Wales interested in AI and data science and how students learn about AI and data in computing? The Raspberry Pi Computing Education Research Centre is starting an exciting new research project investigating how to teach about AI and data in the primary computing classroom, and… The post Join our new study on AI and data-driven computing in UK primary classrooms appeared first on Raspberry Pi Foundation.
Are you a primary school teacher in England, Scotland or Wales interested in AI and data science and how students learn about AI and data in computing? <img loading="lazy" decoding="async" width="1000" height="600" src="https://www.raspberrypi.org/app/uploads/2024/01/Young-people-doing-digital-making-1.png" alt="" class="wp-image-86142" style="width:688px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2024/01/Young-people-doing-digital-making-1.png 1000w, https://www.raspberrypi.org/app/uploads/2024/01/Young-people-doing-digital-making-1-300x180.png 300w, https://www.raspberrypi.org/app/uploads/2024/01/Young-people-doing-digital-making-1-500x300.png 500w, https://www.raspberrypi.org/app/uploads/2024/01/Young-people-doing-digital-making-1-768x461.png 768w, https://www.raspberrypi.org/app/uploads/2024/01/Young-people-doing-digital-making-1-800x480.png 800w, https://www.raspberrypi.org/app/uploads/2024/01/Young-people-doing-digital-making-1-450x270.png 450w, https://www.raspberrypi.org/app/uploads/2024/01/Young-people-doing-digital-making-1-900x540.png 900w" sizes="auto, (max-width: 1000px) 100vw, 1000px" /> The Raspberry Pi Computing Education Research Centre is starting an exciting new research project investigating how to teach about AI and data in the primary computing classroom, and we would like you to get involved. The study will look at: How AI and data-driven computing is currently taught (e.g. using Machine Learning for Kids, Google’s Teachable Machine) What key ideas about AI and data young people need to understand How young people make sense of working with data in computing Register here The study involves attending a workshop in Cambridge, co-designing a unit of work, and then teaching it. Where necessary, we can reimburse you for reasonable expenses, such as supply cover, travel, and accommodation. <img loading="lazy" decoding="async" width="2560" height="1709" src="https://www.raspberrypi.org/app/uploads/2025/01/ATHENS_CODE_CLUB_030-scaled.jpg" alt="A teacher assisting a young person with a coding project." class="wp-image-89308" style="aspect-ratio:1.4979937583593401;width:690px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2025/01/ATHENS_CODE_CLUB_030-scaled.jpg 2560w, https://www.raspberrypi.org/app/uploads/2025/01/ATHENS_CODE_CLUB_030-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2025/01/ATHENS_CODE_CLUB_030-500x334.jpg 500w, https://www.raspberrypi.org/app/uploads/2025/01/ATHENS_CODE_CLUB_030-768x513.jpg 768w, https://www.raspberrypi.org/app/uploads/2025/01/ATHENS_CODE_CLUB_030-1536x1026.jpg 1536w, https://www.raspberrypi.org/app/uploads/2025/01/ATHENS_CODE_CLUB_030-2048x1367.jpg 2048w, https://www.raspberrypi.org/app/uploads/2025/01/ATHENS_CODE_CLUB_030-800x534.jpg 800w, https://www.raspberrypi.org/app/uploads/2025/01/ATHENS_CODE_CLUB_030-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2025/01/ATHENS_CODE_CLUB_030-900x601.jpg 900w" sizes="auto, (max-width: 2560px) 100vw, 2560px" /> Our aim for the study is to understand how primary school teachers approach teaching about data-driven technologies, and to find suitable methods for building young people’s confidence in working with data in computing lessons. What is data-driven computing? Research has suggested that new data-driven technologies such as AI and machine learning (or ML) require a different approach to teaching about problem-solving in the computing classroom. Instead of defining a set of rules (e.g. if-then-else statements, or a rule-based approach), learners must instead collect lots of data to train a model (a data-driven approach) such as using Google’s Teachable Machine to classify image data. For educators and resource developers, we still lack a clear understanding of how to teach young people about how rule-based and data-driven systems differ, how we can talk about them, and how we develop young people’ mental models. We hope this study will help us to find practical ways for primary teachers to build young people’s understanding of AI data in the primary computing classroom. What does the study involve? If you teach at primary level (Years 4, 5 and 6 or P5–P7) in England, Scotland or Wales and are keen to shape how we teach young people about data-driven computing, we invite you to join our new study. Register here As part of the study, you will attend a workshop with us in Cambridge to co-design a series of data-driven computing lessons to teach in your classroom. <img loading="lazy" decoding="async" width="1000" height="600" src="https://www.raspberrypi.org/app/uploads/2024/01/primary-ed.png" alt="A young learners in the classroom" class="wp-image-86110" style="width:724px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2024/01/primary-ed.png 1000w, https://www.raspberrypi.org/app/uploads/2024/01/primary-ed-300x180.png 300w, https://www.raspberrypi.org/app/uploads/2024/01/primary-ed-500x300.png 500w, https://www.raspberrypi.org/app/uploads/2024/01/primary-ed-768x461.png 768w, https://www.raspberrypi.org/app/uploads/2024/01/primary-ed-800x480.png 800w, https://www.raspberrypi.org/app/uploads/2024/01/primary-ed-450x270.png 450w, https://www.raspberrypi.org/app/uploads/2024/01/primary-ed-900x540.png 900w" sizes="auto, (max-width: 1000px) 100vw, 1000px" /> Following the workshop, you will teach the unit of work in your classroom and we will observe one of your lessons and interview you about your experiences. How can I take part? If you are interested in taking part, register your interest by clicking the link below: Register here If you have any questions about the project, you can email bobby.whyte@raspberrypi.org. The post Join our new study on AI and data-driven computing in UK primary classrooms appeared first on Raspberry Pi Foundation.
How to evaluate your use of classroom technology with the PICRAT framework
There’s always something new to consider when teaching with technology. From the latest advancements in AI, to new software and hardware updates, it can be difficult to know which tools to use and how to incorporate it effectively into your lessons. In today’s blog, we explore the PICRAT framework and how it can help you… The post How to evaluate your use of classroom technology with the PICRAT framework appeared first on Raspberry Pi Foundation.
There’s always something new to consider when teaching with technology. From the latest advancements in AI, to new software and hardware updates, it can be difficult to know which tools to use and how to incorporate it effectively into your lessons. <img loading="lazy" decoding="async" width="500" height="263" src="https://www.raspberrypi.org/app/uploads/2026/02/PICRAT-QR-500x263.png" alt="" class="wp-image-92495" style="aspect-ratio:1.9011540651037624;width:715px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/PICRAT-QR-500x263.png 500w, https://www.raspberrypi.org/app/uploads/2026/02/PICRAT-QR-300x158.png 300w, https://www.raspberrypi.org/app/uploads/2026/02/PICRAT-QR-768x403.png 768w, https://www.raspberrypi.org/app/uploads/2026/02/PICRAT-QR-800x420.png 800w, https://www.raspberrypi.org/app/uploads/2026/02/PICRAT-QR-450x236.png 450w, https://www.raspberrypi.org/app/uploads/2026/02/PICRAT-QR-900x473.png 900w, https://www.raspberrypi.org/app/uploads/2026/02/PICRAT-QR.png 1200w" sizes="auto, (max-width: 500px) 100vw, 500px" /> In today’s blog, we explore the PICRAT framework and how it can help you reflect on your use of technology in the classroom. We also share our new PICRAT Quick Read, which you can download for free to: Find practical tips on how to use the PICRAT model when planning your lessons Read a summary of the research behind the framework What is the PICRAT framework? Technology is constantly changing, and educators must continually decide what tools to use in their practice. To help with this challenge, researchers started developing theoretical models that teachers (especially student teachers) could use to reflect on how they integrate technology in their classrooms. You might already be familiar with frameworks like TPACK (Technology, Pedagogy, and Content Knowledge) and SAMR (Substitution, Augmentation, Modification, Redefinition). While these models are useful, the PICRAT framework was created to address gaps in these earlier models, offering a clearer, student-focused approach. Significantly, it encourages you to treat technology as a tool to support learning, rather than the goal itself. It asks two simple questions: “How are students experiencing the technology?” and “How does this impact your practice?”. The answers to these questions form a matrix as pictured below. <img loading="lazy" decoding="async" width="500" height="288" src="https://www.raspberrypi.org/app/uploads/2026/02/PICRAT-Matrix-500x288.png" alt="" class="wp-image-92497" srcset="https://www.raspberrypi.org/app/uploads/2026/02/PICRAT-Matrix-500x288.png 500w, https://www.raspberrypi.org/app/uploads/2026/02/PICRAT-Matrix-300x173.png 300w, https://www.raspberrypi.org/app/uploads/2026/02/PICRAT-Matrix-450x259.png 450w, https://www.raspberrypi.org/app/uploads/2026/02/PICRAT-Matrix.png 600w" sizes="auto, (max-width: 500px) 100vw, 500px" /> PIC (which runs along the y-axis) refers to the student’s relationship to the technology: Passive – Students receive learning through technology Interactive – Students interact with the content or other learning through technology Creative – Students construct knowledge using technology RAT (which runs along the x-axis) refers to how the teacher uses the technology: Replaces – Using technology but with an existing pedagogy Amplifies – Using technology to improve pedagogy or outcomes Transforms – Using technology to create new pedagogical practices How can I apply the PICRAT model? First choose the lesson you’re planning to deliver. Consider what activities you’ll be running and the technologies involved. You’ll then be able to plot where they sit on the matrix using the PICRAT acronym. For example, if you are teaching a lesson on Python loops, you might initially plan for students to watch a pre-recorded coding tutorial on their laptops. In this scenario, the student experience is Passive (receiving info via tech), and the teacher’s use is Replacement because the video simply replaces a live lecture. To move up the matrix, you could instead have students use an online IDE to complete a “Parson’s Problem” puzzle where they rearrange blocks of code to fix a loop. This shifts the activity to Interactive and Amplification, as the digital tool provides immediate debugging feedback that a paper-based exercise could not. <img loading="lazy" decoding="async" width="500" height="288" src="https://www.raspberrypi.org/app/uploads/2026/02/Educator-Presenting-Clubs-Conference-2023-500x288.png" alt="Educator presenting in a classroom." class="wp-image-92500" style="width:552px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/Educator-Presenting-Clubs-Conference-2023-500x288.png 500w, https://www.raspberrypi.org/app/uploads/2026/02/Educator-Presenting-Clubs-Conference-2023-300x173.png 300w, https://www.raspberrypi.org/app/uploads/2026/02/Educator-Presenting-Clubs-Conference-2023-450x259.png 450w, https://www.raspberrypi.org/app/uploads/2026/02/Educator-Presenting-Clubs-Conference-2023.png 600w" sizes="auto, (max-width: 500px) 100vw, 500px" /> Next, think about how you might move your practice forwards. Although every position on the matrix has its own value, the framework is hierarchical. The overall goal is to try to move your practice towards the top right of the matrix to be Creative and Transformative. To help you achieve this, take some time to reflect on your current lessons, activities, and the technologies you use. Ask yourself questions like: What does the technology I’m using offer that could be used to amplify my practice? What benefits would this have for students? Does the technology present opportunities for students to interact with each other, not just the technology? What other technological tools might support collaboration? Research highlights that technology is rarely used in ways that allow young people to be creative. By using the PICRAT matrix, teachers can identify missed opportunities and explore ways to transform their lessons, ensuring learners can be creative and thrive. The benefits of the PICRAT model Potential benefits for educators: The framework encourages meaningful reflections, allowing teachers to easily evaluate how they’re using technology within their lessons Reflections and the PICRAT matrix helps teachers to identify missed opportunities and gaps in their practice, ultimately leading to better student experiences <img loading="lazy" decoding="async" width="2200" height="1467" src="https://www.raspberrypi.org/app/uploads/2026/02/AI-research-summit-10.jpg" alt="Photo of educators sharing ideas in a classroom." class="wp-image-92505" style="aspect-ratio:1.5015248128638758;width:539px;height:auto" srcset="https://www.raspberrypi.org/app/uploads/2026/02/AI-research-summit-10.jpg 2200w, https://www.raspberrypi.org/app/uploads/2026/02/AI-research-summit-10-300x200.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/02/AI-research-summit-10-500x333.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/02/AI-research-summit-10-768x512.jpg 768w, https://www.raspberrypi.org/app/uploads/2026/02/AI-research-summit-10-1536x1024.jpg 1536w, https://www.raspberrypi.org/app/uploads/2026/02/AI-research-summit-10-2048x1366.jpg 2048w, https://www.raspberrypi.org/app/uploads/2026/02/AI-research-summit-10-800x533.jpg 800w, https://www.raspberrypi.org/app/uploads/2026/02/AI-research-summit-10-450x300.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/02/AI-research-summit-10-900x600.jpg 900w" sizes="auto, (max-width: 2200px) 100vw, 2200px" /> You can use the PICRAT framework as part of your own reflections, or as part of a group activity. It’s a great way to spark discussion about technology integration with colleagues and improve best practices. Want to find out more about the PICRAT framework? If you’d like to learn more about the PICRAT model, you can download our Quick Read for free via our new Pedagogy Quick Reads page. Download the PICRAT Pedagogy Quick Read The post How to evaluate your use of classroom technology with the PICRAT framework appeared first on Raspberry Pi Foundation.
A day with young creators at Coolest Projects Mzansi 2025
Coolest Projects is the world’s leading technology showcase for young people, a space where creativity, curiosity, and problem-solving come together through code. Last November, Coolest Projects returned to South Africa, providing another exciting opportunity to celebrate and amplify the voices of young digital creators across Mzansi, a local name for South Africa. Coolest Projects brings… The post A day with young creators at Coolest Projects Mzansi 2025 appeared first on Raspberry Pi Foundation.
Coolest Projects is the world’s leading technology showcase for young people, a space where creativity, curiosity, and problem-solving come together through code. Last November, Coolest Projects returned to South Africa, providing another exciting opportunity to celebrate and amplify the voices of young digital creators across Mzansi, a local name for South Africa. Coolest Projects brings young people together to share what excites them the most about creating with technology, whether that’s making a game, a website, a robot, or a simple Scratch animation. What matters most is not perfection, but participation. A showcase for young makers Coolest Projects, an initiative of the Raspberry Pi Foundation, is brought to South Africa by our long-standing partner Coder:LevelUp, giving young people the space to share how they get creative with technology. I spent the day surrounded by young people explaining their ideas with a mix of excitement, nerves, and confidence — the kind of emotions you get when you build something yourself and someone is finally asking you about it. The event brought together over 250 young people to share the projects they had been working on. Some had been preparing for months, while others had begun exploring their ideas more recently. All of them arrived ready to explain what they made, how it worked, and what they would do differently next time. There wasn’t a single kind of project. There was a Scratch animation at one table, a game at the next, and a hardware project across the room. Some projects were polished, others rougher around the edges. But the event isn’t about perfection, it’s about learning and having the courage to show your work. For a lot of participants, this was the first time they had presented their work to an audience, and you could see their confidence build as they spoke. By the end of the day, many were already talking about what they wanted to build next! From experience, we know that many of these young people will come back to Coolest Projects in the future, with our global impact data showing that over 1 in 10 participants in the Coolest Projects online showcase in 2025 had also taken part in Coolest Projects in 2024. The coolest projects and the coolest people One of my favourite things about Coolest Projects is that although you might come for the projects, what stays with you is often the people behind them. Coolest Projects celebrates both what young people build and who they are becoming. The coolest projects A project that really stood out to me was created by a group of girls who built a language app that translates words from isiZulu, one of South Africa’s official languages, into French and other languages — an ambitious and thoughtful idea. <img loading="lazy" decoding="async" width="500" height="375" src="https://www.raspberrypi.org/app/uploads/2026/02/unnamed-5-500x375.jpg" alt="A group of students looking at a computer screen." class="wp-image-92467" srcset="https://www.raspberrypi.org/app/uploads/2026/02/unnamed-5-500x375.jpg 500w, https://www.raspberrypi.org/app/uploads/2026/02/unnamed-5-300x225.jpg 300w, https://www.raspberrypi.org/app/uploads/2026/02/unnamed-5-450x338.jpg 450w, https://www.raspberrypi.org/app/uploads/2026/02/unnamed-5.jpg 512w" sizes="auto, (max-width: 500px) 100vw, 500px" /> What made this project especially meaningful to me was how well it reflected South Africa’s identity as a rainbow nation, with 12 official languages and countless cultures intersecting everyday. Their work wasn’t just technical, it was deeply relevant. It showed how young creators are already thinking about inclusion, communication, and using technology to bridge the gap between people in different parts of the country. Seeing these young girls confidently present such a culturally aware solution was incredibly inspiring. And the coolest people A group of boys arrived with a project that, honestly, fell apart on the day. Things didn’t work the way they had planned. But instead of shutting down or panicking, they laughed it off, explained what should have happened, and kept the energy positive. Their confidence, humour, and teamwork were great to see. They showed that Coolest Projects is not about having everything work perfectly. It’s about showing up, trying something ambitious, and being willing to talk about what went wrong. Why does Coolest Projects matter? Coolest Projects goes beyond showcasing technical skills. It helps young people: Build confidence in presenting ideas Develop problem-solving and communication skills See themselves as creators, not just consumers, of technology For many young creators in South Africa, participating in Coolest Projects is a powerful way to gain recognition and see that their ideas matter and that they belong in the world of technology. We look forward to Coolest Projects South Africa returning in 2026 to once again showcase the incredible potential of young tech creators across the country. Inspired to take part? The Coolest Projects 2026 global online showcase is now open for entries. Find all the details on how to enter, as well as details of Coolest Projects in-person events you can look forward to throughout 2026, in our recent blog post. To be the first to hear about events near you, sign up to the Coolest Projects newsletter. The post A day with young creators at Coolest Projects Mzansi 2025 appeared first on Raspberry Pi Foundation.