Tag Archives: AI education

Experience AI evolves with flexible resources for every classroom

Post Syndicated from Ben Garside original https://www.raspberrypi.org/blog/experience-ai-evolves-with-flexible-resources-for-every-classroom/

Experience AI equips young people with a meaningful understanding of artificial intelligence (AI) and machine learning by giving educators the knowledge and confidence to teach these topics in ways that suit their classrooms.

Whether you’re introducing AI to learners for the first time, helping them deepen their understanding, exploring generative AI with them, or integrating AI literacy across the curriculum, Experience AI offers you all the resources you need for free.

Since we started publishing Experience AI resources in 2023, they have been downloaded over a million times in 195 countries, and we have worked with partner organisations in more than 40 countries to train educators to teach AI literacy. Thanks to partners, we have learned a lot about how teachers around the world use the resources in their classrooms, and this has given us direction for what new resources to develop.

The updated suite of Experience AI resources

Many teachers looking for AI literacy resources are not computing specialists and have very busy timetables. Under pressure to deliver more content without more instructional time, what educators need are resources they can integrate into what they already teach. To support them, we’re now offering an updated suite of Experience AI resources that make AI literacy more accessible, flexible, and relevant.

The resources include those co-developed by the Raspberry Pi Foundation and Google DeepMind, alongside those developed independently by the Raspberry Pi Foundation.

Screenshot of the Discover AI resources on the Experience AI website

The new Discovering AI resources, aimed at learners aged 8–12 and learners aged 13–16, are single lessons for introducing the fundamental ideas behind AI. They support educators with learners who have little or no prior knowledge of AI, and include engaging, age-appropriate activities.

Screenshot of Experience AI resources.

From there, our updated Foundations of AI units let teachers support their learners to develop a deeper understanding of how AI systems work, how they’re trained, and how they can be applied to real-world problems.

Slide from one of the Experience AI activities exploring AI and a real-world problem such as flood-forecasting.

And a new and growing collection of thematic resources supports teachers and learners to explore AI through cross-subject topics such as creativity, the environment, and critical thinking.

Experience AI now fully reflects our belief that AI literacy needs to be cross-curricular. Application of AI technologies isn’t limited to neat domains or subjects, so learners’ opportunities to understand them shouldn’t be either.

Why we are creating thematic resources

Education systems vary significantly between countries, and in most national curricula, AI literacy is not yet clearly defined. Nevertheless, teachers are both under pressure to deliver this new topic area in their limited classroom time, and eager to rise to the challenge to support their learners.

So we asked ourselves: how can we help educators to teach AI literacy in any subject without additional lesson time, when we cannot create specific resources for every subject in every education system?

Our answer came from educators themselves. Through our network of global partners, we learned that teachers were not waiting for us to tell them in what subjects the Experience AI resources belonged. They were already adapting them to teach AI literacy in all sorts of contexts. For example, we saw that some educators adapted the resource on AI and ecosystems, which we had developed for Biology classrooms, for their Geography classrooms, where it supported similar learning goals.

Photo of a group of educators being trained to teach and use Experience AI resources.

This insight into teachers’ classroom practice prompted us to change our approach.

Now, rather than designing resources for a single subject, we design and organise them in themes that fit across subjects, such as environment, creativity, ethics, and critical thinking. So a resource for exploring the environmental impact of AI data centres could be used in Geography, Physics, Citizenship, or Business Studies classrooms. An activity about AI-generated media could prompt discussions in Digital Literacy, Art, or Computing lessons. The same material supports different curricular goals, depending on how teachers choose to use it.

Helping every educator teach AI literacy with confidence

A key advantage of this new thematic approach is flexibility. Our thematic resources support teachers to:

  • Introduce AI literacy through topics they can easily fit into their subject
  • Integrate AI literacy without additional lesson time
  • Adapt the included activities for different learners and classroom contexts

Like all Experience AI materials, the new resources:

  • Encourage classroom discussions and promote critical thinking and reflection about AI technologies
  • Help learners understand how AI impacts society, not just how AI tools work
Photo of an educator teaching Experience AI in a classroom.

While Experience AI will continue to focus on core AI literacy concepts — how AI systems work, how they are used, and how to think critically about them — the resources we offer will increasingly be:

  • Thematic: Built around real-world topics with broad relevance
  • Modular: Adaptable to different classroom contexts rather than tied to a fixed sequence
  • Differentiated: Designed specifically for learners aged 8–12 and 13–16
  • Varied in format: Full lessons, stand-alone discussion activities, and extended project guides

In this way, we aim to make teaching AI literacy practical and achievable for every educator, regardless of their subject specialism or previous experience of using or teaching about AI.

Share your feedback with us

We’ve tested the new thematic resources with our Experience AI partner, Digital Moment in Canada, who also co-created our Social Media and Flood Forecasting units. Educators’s feedback shows that they value the added flexibility and find the new materials easier to bring into their teaching.

With our new approach, we’re able to offer a more flexible Experience AI programme that supports a wider range of educators, however they want to bring AI literacy into their classrooms.

Discover the new resources

If you use the resources in your classrooms, please tell us what you think. We’ll continue refining and expanding the Experience AI programme and resources in response to feedback from educators and partners around the world.

Get in touch and share your stories of using Experience AI in the classroom via our email: [email protected]

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Supporting a technical AI-focused qualification for young people in England

Post Syndicated from Diane Dowling original https://www.raspberrypi.org/blog/support-for-ai-focused-qualification-young-people-england/

Young people see and use artificial intelligence (AI) systems frequently, and hear a lot about how they are applied across many areas of life. That makes them understandably eager to find out more about how AI works. For some, this interest extends to wanting to experiment with the technology to investigate their own questions and build something themselves. How can schools support learners who want to take their interest further and gain recognition for the knowledge and skills they develop along the way?

Computer science students at a desktop computer in a classroom.

For learners in the UK, there is currently no established qualification that develops technical AI skills. This can make it difficult to create space in the curriculum for dedicated AI learning, especially for learners from age 14, for whom preparation for high-stakes exams is prioritised over non-examined learning.

We have been working over the past year to develop a research-informed curriculum framework for data science, which lays out many of the foundational skills and knowledge needed to understand and develop AI models. Alongside this, we have been making the case in England for a Level 3 qualification in data science and AI. However, establishing a new qualification can take years.

In the meantime, we are starting to support students in England who choose an established route for extending their skills and getting that effort recognised: the Extended Project Qualification (EPQ).

An established qualification with room to explore AI

The EPQ is a well-established qualification for 16- to 19-year-olds offered in England, Northern Ireland, and Wales by several exam boards (including AQA, Pearson Edexcel, OCR, Eduqas/WJEC, and City & Guilds). It is completed by around 1 in 10 learners at this stage of education.

Learners pursuing an EPQ undertake an extended, self-directed investigation or project. They choose an area of interest where they want to develop their own project and take responsibility for planning, completing, and evaluating the project work.

A teenager learning computer science.

Its self-directed nature makes the EPQ particularly well suited to learners who want to explore emerging areas, such as artificial intelligence and data science. For students fascinated by AI, an EPQ offers an opportunity to do more than simply learn about the technology. With the right foundations and support, they can investigate a question of their own, build a machine learning model, and use that work to gain a recognised qualification.

We are now working to offer those foundations through a new introductory ‘Data Science and AI’ course that will enable learners to build a machine learning model as their EPQ artefact.

Building the foundations for an independent AI investigation

Creating a machine learning model involves much more than choosing a statistical technique and using easily available training data. It involves understanding the problem being addressed, deciding what data to use, exploring and preparing that data, building and evaluating a model, and interpreting the model’s outputs.

Learners who want to do this work for their EPQ need not only technical knowledge and skills, but also skills for critical thinking, research, decision-making, evaluation, and reflection — skills that are valuable for any further study and future career.

To help learners develop these foundations, we are creating an introductory ‘Data Science and AI’ course, based on our curriculum framework for data science and designed to be completed before learners begin their AI-focused EPQ.

The course will guide students through a structured investigation based on a recognised data science lifecycle. By working through it, students will:

  • Learn about each stage of the data science lifecycle
  • Develop the technical understanding they need to make independent decisions during an AI investigation 
  • Gain practical experience of working with data and building a machine learning model
  • Learn about key concepts behind the techniques they use, through theoretical content that supports them to understand not just what to do, but why

The course will consist of 10 units, each involving 2 to 3 hours of independent study for learners. We are designing the activities with a no-code approach to allow students to focus on understanding data science concepts and processes.

Supporting independent project work

Importantly, the work learners produce during this course won’t be the EPQ investigation itself. Working through ‘Data Science and AI’ will give learners the knowledge and skills they need to undertake an AI-focused project with greater independence and confidence.

A young person in a university computing classroom.

After they have completed the course, when they begin their EPQ project pursuing a question that interests them, students can apply their new skills and knowledge to make decisions about their AI investigation and document their progress.

We are looking forward to supporting learners in England to turn their interest in one of today’s most significant technologies into the opportunity to develop independent research and project skills through a recognised qualification.

What’s next?

The ‘Data Science and AI’ course will be available to selected schools in England from September 2026, where students and teachers will test the materials and we will learn from their feedback to improve the course materials. We plan to make the course freely available in 2027. 

The post Supporting a technical AI-focused qualification for young people in England appeared first on Raspberry Pi Foundation.

Supporting an AI-focused qualification for young people in England

Post Syndicated from Diane Dowling original https://www.raspberrypi.org/blog/support-for-ai-focused-qualification-young-people-england/

Young people see and use artificial intelligence (AI) systems frequently, and hear a lot about how they are applied across many areas of life. That makes them understandably eager to find out more about how AI works. For some, this interest extends to wanting to experiment with the technology to investigate their own questions and build something themselves. How can schools support students who want to take their interest further and gain recognition for the knowledge and skills they develop along the way?

Computer science students at a desktop computer in a classroom.

Currently, there is no widely available subject qualification in AI for students in the UK. This can make it difficult to create space in the curriculum for dedicated AI learning, especially for students from age 14, for whom preparation for high-stakes exams is prioritised over non-examined learning.

We have been working over the past year to develop a research-informed curriculum framework for data science, which lays out many of the foundational skills and knowledge needed to understand and develop AI models. Alongside this, we have been making the case in England for a Level 3 qualification in data science and AI. However, establishing a new qualification can take years.

In the meantime, we are starting to support students in England who choose an established route for extending their skills and getting that effort recognised: the Extended Project Qualification (EPQ).

An established qualification with room to explore AI

The EPQ is a well-established qualification for 16- to 19-year-olds offered in England, Northern Ireland, and Wales by several exam boards (including AQA, Pearson Edexcel, OCR, Eduqas/WJEC, and City & Guilds). It is completed by around 1 in 10 learners at this stage of education.

Learners pursuing an EPQ undertake an extended, self-directed investigation or project. They choose an area of interest where they want to develop their own project and take responsibility for planning, completing, and evaluating the project work.

A teenager learning computer science.

Its self-directed nature makes the EPQ particularly well suited to learners who want to explore emerging areas, such as artificial intelligence and data science. For students fascinated by AI, an EPQ offers an opportunity to do more than simply learn about the technology. With the right foundations and support, they can investigate a question of their own, build a machine learning model, and use that work to gain a recognised qualification.

We are now working to offer those foundations through a new introductory ‘Data Science and AI’ course that will enable learners to build a machine learning (ML) model as their EPQ artefact.

Building the foundations for an independent AI investigation

Creating an ML model involves much more than choosing a statistical technique and using easily available training data. It involves understanding the problem being addressed, deciding what data to use, exploring and preparing that data, building and evaluating a model, and interpreting the model’s outputs.

Learners who want to do this work for their EPQ need not only technical knowledge and skills, but also skills for critical thinking, research, decision-making, evaluation, and reflection — skills that are valuable for any further study and future career.

To help learners develop these foundations, we are creating an introductory ‘Data Science and AI’ course, based on our curriculum framework for data science and designed to be completed before learners begin their AI-focused EPQ.

The course will guide students through a structured investigation based on a recognised data science lifecycle. By working through it, students will:

  • Learn about each stage of the data science lifecycle
  • Develop the technical understanding they need to make independent decisions during an AI investigation 
  • Gain practical experience of working with data and building a machine learning model
  • Learn about key concepts behind the techniques they use, through theoretical content that supports them to understand not just what to do, but why

The course will consist of 10 units, each involving 2 to 3 hours of independent study for learners. We are designing the activities with a no-code approach to allow students to focus on understanding data science concepts and processes.

Supporting independent project work

Importantly, the work learners produce during this course won’t be the EPQ investigation itself. Working through ‘Data Science and AI’ will give learners the knowledge and skills they need to undertake an AI-focused project with greater independence and confidence.

A young person in a university computing classroom.

After they have completed the course, when they begin their EPQ project pursuing a question that interests them, students can apply their new skills and knowledge to make decisions about their AI investigation and document their progress.

We are looking forward to supporting learners in England to turn their interest in one of today’s most significant technologies into the opportunity to develop independent research and project skills through a recognised qualification.

What’s next?

The ‘Data Science and AI’ course will be available to selected schools in England from September 2026, where students and teachers will test the materials and we will learn from their feedback to improve the course materials. We plan to make the course freely available in 2027. 

The post Supporting an AI-focused qualification for young people in England appeared first on Raspberry Pi Foundation.

Supporting AI education for 150,000 learners in Aotearoa New Zealand and Australia

Post Syndicated from Anna Burton original https://www.raspberrypi.org/blog/supporting-ai-education-for-150000-learners-in-aotearoa-new-zealand-and-australia/

We’re pleased to share that we are expanding our Experience AI programme to Australia and Aotearoa New Zealand to train 5000 educators who can reach 150,000 students by 2028, thanks to generous funding of $1.2 million from Google.org.

CSER Team delivering a workshop at Adelaide University

Working with local education organisations, we will support young people to develop a foundational understanding of AI technologies, their social and ethical implications, and the role that AI can play in their lives.

AI literacy across the world through Experience AI

AI systems are a common part of everyday life and influence how we access information, and how we work and solve problems. We believe that young people need more than the ability to use AI tools: they need the knowledge, skills, and confidence to understand how AI technology works, to think critically about its impact, and to create AI-based solutions of their own. 

Experience AI is our free educational programme co-developed with Google DeepMind that helps teachers and students learn about artificial intelligence. Through the Experience AI training, lessons, classroom resources, and hands-on activities, teachers introduce young people to how AI systems work, how they can be used, and what their impacts may be.

CSER Team example of teacher workshop 2026

We bring AI literacy to young people across the world with Experience AI by building trusted partnerships with local organisations that lead sustainable delivery of the programme in ways that suit their contexts. Through this global network of Experience AI partners, we have trained over 50,000 educators who can reach an estimated 4.8m young people. Today, Experience AI resources are used in over 195 countries and available in 22 languages. In recognition of its global impact, Experience AI was named a laureate of the 2025 UNESCO King Hamad Bin Isa Al-Khalifa Prize for the Use of ICT in Education.

Experience AI partnerships in Australia and Aotearoa New Zealand

In Australia, the first partner we are working with is the Computer Science Education Research Group (CSER), based at Adelaide University. Professor Katrina Falkner, the university’s Pro-Vice-Chancellor, Learning and Teaching, says about the partnership:

“We are thrilled to partner with the Raspberry Pi Foundation to bring the Experience AI programme to Australian schools. It is so important that teachers are provided opportunities to understand AI so they can help students develop the self-regulated learning skills needed to thrive in a world where AI is increasingly part of everyday learning and work. Educators can play a critical role in ensuring that a human lens of critical thinking, ethical judgement, creativity, and meaningful human connection remain at the heart of AI education and adoption, preparing students for careers where effective collaboration with AI tools will be essential.”

In Aotearoa New Zealand, we are working with Tōnui Collab Charitable Trust, a Maōri-led organisation dedicated to creating innovative STEM learning opportunities.

Collaboration with Tonui

Shanon O’Connor, Director of Tōnui Collab, says about the partnership (1):

“We are partnering with the Raspberry Pi Foundation to provide this kaupapa to educators in Aotearoa, adapting and contextualising their global Experience AI program to make it meaningful and relevant in Aotearoa.

This kaupapa isn’t about learning to code; it’s not a kaupapa designed solely for the ‘tech enthusiasts’, it’s about fostering digital equity, ensuring rangatahi have the tools and knowledge to thrive in a world increasingly shaped by technology. It’s our attempt to ensure the digital divide doesn’t become a digital chasm. 

We’re also facilitating robust conversations about data bias and the impact this has on the ways we as Māori engage with AI-powered technologies, creating space for kōrero about tech tikanga and our collective responsibilities when using or engaging with AI-powered technologies.”

Looking ahead

All young people need opportunities to develop the skills, knowledge, and confidence to navigate and shape a world where AI technologies are widely used. With support from Google.org and education partners across Aotearoa New Zealand, Australia, we will continue to expand access to high-quality AI education.

Find out more about Experience AI at experience-ai.org

(1) Shanon uses some Māori words that are common in Aotearoa New Zealand for both speakers and non-speakers of reo Māori:

  • kaupapa: the guiding purpose, philosophy, or approach underpinning the work
  • rangatahi: younger generation, youth
  • “kōrero about tech tikanga”: having discussions about the correct protocols, ethics, and practices for engaging with technology

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AI literacy begins with data literacy: An example from healthcare

Post Syndicated from Bobby Whyte original https://www.raspberrypi.org/blog/ai-literacy-begins-with-data-literacy-an-example-from-healthcare/

The development of AI and data science has transformed how we gain insights from data. In healthcare, AI tools are being used in the development of new treatments as researchers apply machine learning methods to datasets. However, applying AI in healthcare also brings risks, particularly when systems amplify existing biases in data or design.

In our fourth seminar in our current series on teaching about AI in the arts, humanities, and sciences, Kathy Jessen Eller (The Concord Consortium) introduced the Data Science, AI & You (DSAIY) programme, a high school curriculum that helps students critically evaluate the role of data and AI in healthcare.

A picture of Kathy Jessen Eller.
Kathy Jessen Eller (The Concord Consortium)

The role of critical thinking skills in AI education

Kathy began her seminar by arguing that for many students who use AI tools in their coursework, questions remain about whether they are critically evaluating the tools’ outputs. Students may or may not check an AI-generated answer against primary sources to see if the answer is accurate. There is also growing concern that students’ use of AI tools lets them offload cognitive work rather than engage in deeper thinking. This presents a challenge for educators: how do we help students use AI productively while still supporting them to develop the critical judgement needed to evaluate its outputs?

Introducing Data Science, AI & You (DSAIY)

To tackle this challenge, Kathy and her colleagues have developed the Data Science, AI & You (DSAIY) programme (pronounced ‘Daisy’). DSAIY is a semester-long high school curriculum designed to introduce students to AI by actively engaging them in the machine learning process.

The programme introduces machine learning as the engine behind many AI tools, and introduces the concept of bias through real-world examples. Students use a variety of tools to collect and prepare data, train, test, and evaluate models. It culminates in an ‘AI-a-thon’ where young people work in cross-disciplinary teams alongside data scientists, clinicians, and their own teachers to gain real-world experience.

Students in class during an Experience AI lesson.

At the time of the seminar, 11 teachers had delivered the programme to over 800 students across a variety of settings in Rhode Island, USA. Teachers are heavily supported with four days of professional development and ongoing technical assistance throughout implementation. The students that took part had a wide variety of prior experience, including many with no prior background in computer science or statistics. Female participation is notably high; one teacher even remarked that the course saw more girls enrolled than any of his other computer science classes.

Hands-on with machine learning

In DSAIY, students experience the full machine learning pipeline from data collection and data preparation, to modeling and deployment. Using Python, they train and test simple machine learning models on authentic healthcare data. The aim of the programme is to move students from basic graphing to evaluating complex models, transitioning them from merely plotting data to deeply reasoning about it.

The programme makes use of CODAP (the Common Online Data Analysis Platform), a free, web-based tool developed by The Concord Consortium. CODAP provides an interactive, highly visual environment that lowers the barrier to entry. Students can visualise large datasets and click into individual data points, allowing them to see individual cases within a larger dataset.

A graphic showing the CODAP tool for data visualisation and analysis.
CODAP, a tool for data visualisation and analysis

Understanding bias in healthcare systems

The curriculum uses real-world examples from healthcare to introduce concepts of bias and fairness. For example, students learn about pulse oximeters, which estimate blood oxygen levels. However, as these use red and infrared light, readings can vary depending on skin pigmentation, which can lead to inaccurate readings.

Students also collect their own blood oxygen data and plot it using CODAP to observe variability. They consider the accuracy of their measurements and grapple with the ethics of removing outliers from a dataset. This led to students asking critical questions about the makeup of their datasets, the context in which data are collected, and the implications of how data are used in healthcare.

Through the DSAIY programme, Kathy reported that students developed stronger data reasoning skills, gained a deeper awareness of inherent AI biases and risks, and built confidence in public speaking and collaborating with others. Students were also highly engaged and appreciated the focus on real-world healthcare applications and their social implications.

The importance of data literacy for AI literacy

Kathy concluded the seminar by arguing that AI literacy must start with data literacy. When students learn to examine, question, and reason about the data behind AI technologies, they develop the critical thinking skills needed to engage with outputs from real-world systems or everyday technologies like ChatGPT. This can then help them evaluate both the trustworthiness of these tools and their role in important decision-making processes.

You can watch the seminar here:

If you are interested in learning more about Kathy’s work, you can read about the DSAIY programme here or you can read the paper here. You can also learn about CODAP, the data visualisation tool featured in this seminar here.

Join our next seminar

In our current seminar series, we’re exploring how AI is taught across the curriculum. In our next seminar on Tuesday 14 July at 17:00–18:30 BST, we welcome Dan Verständig (Goethe University Frankfurt) who will explore the connection between Social explainable AI (Social XAI) and Critical Computational Literacy (CCL). To take part in the seminar, click the button below to register. We hope to see you there.

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.

The post AI literacy begins with data literacy: An example from healthcare appeared first on Raspberry Pi Foundation.

Hello World #30 out now: Critical thinking in the age of AI

Post Syndicated from Meg Wang original https://www.raspberrypi.org/blog/hello-world-30-out-now-critical-thinking-in-the-age-of-ai/

Today’s data-driven tools can make many aspects of our personal lives less time-consuming, because they present us with options and even make decisions for us. By relying on predictive text features, we write messages more quickly and outsource our word choices. By using music and film recommendations, we outsource our personal taste. And by using AI chatbots that produce confident answers to every single one of our questions, we outsource our thinking.

Of course, I use and enjoy all of these products. But because I grew up before data-driven tools existed, I also know the accidental delights of exploring a city without a smartphone map, the joy of thoughtfully choosing a gift for a friend, and the satisfaction of comparing insurance quotes and understanding their details. These non-AI-assisted acts exercise my critical thinking skills — something that is harder to do in a world where AI products promise so much convenience.

Image of Hello World magazine, Issue 30 'Critical thinking in the age of AI'.

Critical thinking is even more vital now in the age of AI. The brand-new issue of Hello World — and our new podcast mini series — offers research, advice, and practical resources for teaching young people, and ourselves, to think critically.

Download your free digital copy

Critical thinking in the age of AI

In issue 30 we share articles from educators who have already been thinking deeply about the role of critical thinking in the age of AI. They discuss a range of questions such as:
What do educators bring to the table when teaching with digital technologies?
Why AI professional learning should build teachers’ critical thinking, not just their confidence in using tools
Whose knowledge is shaping AI?

Our feature articles also include:
• Managing cognitive load for deeper thinking
• AI systems in assessment
• Promoting human decision-making

From the team at the Computer Science Teachers Association (CSTA) in the USA we have an article about their newly rewritten CSTA K–12 Standards, a research-backed framework designed to prepare students for a future that seems to be arriving very fast on some days. As their article says:

“AI can generate answers instantly, but understanding and evaluating answers still
requires human judgement. In a world moving at supersonic speed, CS education needs to find a new balance. Students must learn to think critically so they can direct AI rather than being directed by it.” – Amanda O’Mara, Smita Kolhatkar, and Tiffany Jones in Hello World issue 30

Download Hello World issue 30 for free

Developing critical thinking skills is important for young people, regardless of the discipline you teach. In the age of AI, computing education is uniquely situated to cultivate this mindset, encouraging students to engage more thoughtfully with the AI tools they use daily.

Also in issue 30:
• Flatgames
• Predictive classroom systems
• A physics meets technology project

And much, much more.

Download issue 30 now

Let us know which articles you found most helpful for your teaching or which resources you tried out by sending us a message or tagging us on social media.

Thank you to Oracle for sponsoring this issue of Hello World.

The post Hello World #30 out now: Critical thinking in the age of AI appeared first on Raspberry Pi Foundation.

Can AI support creativity? What educators can learn from creative machine learning

Post Syndicated from Manni Cheung original https://www.raspberrypi.org/blog/can-ai-support-creativity-what-educators-can-learn-from-creative-machine-learning/

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

Rebecca Fiebrink.
Rebecca Fiebrink is Professor of Creative Computing at the Creative Computing Institute, University of the Arts London.

Our current seminar series focuses on teaching applied AI and how educators of subjects beyond computing can make AI and machine learning relevant in their classroom. We were delighted to have Rebecca join us to share insights about the place of machine learning in artistic creation. In her talk, Rebecca explored three connected questions:

  • How machine learning can be valuable to musicians, artists, and other creators
  • What machine learning tools for creators should look like
  • What creators need to know about machine learning in order to use it effectively

Using movement, sound, and image data to teach about machine learning

One of the seminar’s key ideas was that machine learning can help creators work with forms of data that already matter to them. Rebecca showed that useful data can come from many sources, including microphones, webcams, phones, wearables, sensors, and body movement. She argued that collecting data is often relatively easy, while interpreting and using it is much harder. 

This suggests a different starting point for AI education. Instead of beginning with a large dataset prepared by somebody else, learners can start with data that is meaningful in their own context. For instance, data about hand gestures can be linked to different musical rhythms, colours, or game actions.

Visual examples of how hand gestures can be associated with rhythm, video game actions, or visuals using machine learning.
From hand gestures to rhythms and game actions. Images from the speaker’s presentation.

What counts as input?

The seminar also points to a broader shift in how we think about input if we consider creative work. Traditional computing often treats input as something abstract and controlled: a click, a typed command, or a button press. But many creative practices do not work like that. They depend on timing, gesture, rhythm, touch, sound, and movement.

Instead of asking learners to translate everything into words or code first, Fiebrink suggested that educators can use machine learning to allow learners to begin with movement, demonstration, or sound. This is especially relevant in art forms shaped by flow and physical expression, such as music, dance, performance, and interactive media.

Educators can use machine learning to allow learners to begin with movement, demonstration, or sound [instead of with code].

That creates interesting possibilities for teaching. AI does not have to be explored only through screens, prompts, and abstract models. It can also be approached through embodied activities, where learners use gestures, performance, and experimentation to see how an AI system responds. This can make machine learning feel more connected to forms of making that young people already understand.

Teaching machine learning through examples

A second important theme in the seminar was that machine learning allows people to instruct computers through data and examples. Rebecca suggested that this can be especially valuable in creative and embodied work, where what a person wants to express may be difficult to describe in words, maths, or code alone.

Contrasting pictures of painting and violin playing compared to a snapshot of code.
The seminar suggested that data and examples can communicate creative intent in ways that code or language cannot always capture.

One of the strongest examples in the seminar was ‘Wekinator‘, a tool Rebecca has been developing since 2008. She described the tool’s approach as ‘interactive machine learning’: users demonstrate training examples, train a model, test it in real time, then modify their examples and repeat the process.

This is a useful example for the classroom because it shows that training a machine learning model is not a single event, after which the model is trained and finished. Instead it is an iterative process. With Wekinator, learners can try something out, observe the result, and improve the system by changing the examples they provide. That makes ideas such as testing, evaluation, and bias much easier to discuss.

Supporting creativity and learner agency

Rebecca also argued that machine learning can help more people become creators. She contrasted large, one-size-fits-all systems that encourage users to imitate existing styles with smaller, more personal systems that can be trained on new data for specific purposes. She captured this contrast clearly, from prompts such as ‘Write music like Bach!’ to examples of personalised tools and interfaces.

Examples from the seminar showing how large models can make it easier for novices to conform to familiar creative styles like those of Bach or Monet.
Examples from the seminar showing how large models can make it easier for novices to conform to familiar creative styles.

This is an important distinction in teaching and learning. If learners only use AI tools to reproduce familiar outputs, then creative work can become narrow and formulaic. But if they can build or train systems around their own interests, intentions, and materials, then machine learning can support experimentation and authorship.

If [learners] can build or train systems around their own interests, intentions, and materials, then machine learning can support experimentation and authorship.

Teaching AI without turning it into a black box

In the final part of the seminar, Rebecca moved from examples to teaching principles. One of the clearest was that machine learning should be taught at a high level with minimal maths, but not as a black box.

Learners do not need advanced mathematics to start exploring machine learning meaningfully, but they do need to understand that:

  • Machine learning models are built from data
  • Models make predictions based on patterns
  • People can inspect, test, and improve models

Rebecca also argued that small data and interactive machine learning can be highly effective. She highlighted quick experimentation, creative usefulness, and the opportunity to build intuition about ideas such as outliers, features, regularisation, and bias in data. Small-scale activities can make technical ideas more visible and manageable for learners.

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Small-data, interactive machine learning can support experimentation and build understanding of how models work.

Why this matters for teaching

Rebecca ended on an inspiring note: she argued that learning and teaching creative machine learning is both worth doing and possible. She pointed to a growing set of tools that support experimentation and original creative work without much maths or coding, including Wekinator, Teachable Machine, Micro:bit CreateAI, and more.

The seminar also addressed some important limitations. Rebecca warned that commercial tools are not always good at supporting learning or genuine creative work. She also discussed the difficulty of making generative AI tools safe for children, noting the need for built-in filters, moderation, prompt design, and extensive testing. Therefore, what’s important is to think about what learners are actually learning, and to make space for experimentation without losing sight of safety and critical thinking.

Join our next seminar

Our research seminars brings together educators and researchers to explore key questions in computing education.

Next in our series on applied AI, Prof. Gianfranco Polizzi (University of Birmingham, UK) will talk about media literacy in the age of AI. Sign up now to join the seminar on 16 June, 17:00 BST:

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What does ‘thinking’ mean now?

Post Syndicated from Meg Wang original https://www.raspberrypi.org/blog/what-does-thinking-mean-now/

At a time when artificial intelligence (AI) systems and tools based on large language models (LLMs) are being rapidly introduced into industries and daily life, the basic definition of ‘thinking’ and the essential skills we teach the next generation are being called into question.

Shuchi Grover showing children something on a laptop screen
Dr Shuchi Grover working with learners in a classroom.

In this interview, Dr Shuchi Grover, a leading voice in computing education who has recently become our Director of Research and Impact, shares how her work in computational thinking is evolving.

Can you share the story of your path in computer science (CS) education?

Most people in the education and CS education world know me from my research in computational thinking and K–12 CS education over the last 15 years. What is less known, perhaps, is that I started my career as a software engineer after completing my undergraduate and graduate studies in CS. About 25 years ago, I made a concerted shift to education, completing a Masters in Education from Harvard University in 2003, and then after a gap earning a PhD in the learning sciences (with a focus on K–12 CS education) from Stanford University in 2014.

Over these last two and a half decades, I have trained my efforts on helping young learners and school-aged children develop 21st-century competencies in computer science, data science, AI, and cybersecurity; as well as on STEM and non-STEM learning experiences that integrate computational thinking, AI, CS, and data science. My research has also attended to promoting interest and a sense of belonging in CS among learners from historically underrepresented groups.

Two students use computers in a classroom.

I recently joined the Raspberry Pi Foundation as Director of Research and Impact. I feel very fortunate, as this role builds on all the work I have done over the course of my professional life and also affords me an unparalleled opportunity on a global scale to continue this work I’ve been so passionate about in both formal and non-formal learning settings.

You are well-known for your work on computational thinking. Since the development of LLMs, how has the definition of ‘thinking’ been changing?

This question is deep and thorny, and I’m not sure we have a complete answer to it yet. I believe that thinking as a human endeavour continues to be valid and means what it always has meant: a cognitive process that involves making new connections and creating meaning. In the education literature, thinking is often equated to problem solving. So teaching students ‘thinking skills’ has meant teaching them logic and ways to solve problems — typically in the context of a domain. In the context of K–12 CS education, computational thinking essentially means computational problem solving.

What changes with LLMs is not the definition of thinking itself, but rather what thinking skills students need most urgently. For students, the idea of ‘critical thinking’ has become much more critical (no pun intended) in an era when LLM-based tools offer quick and easy ways to produce answers. Students need to be equipped with the skills to evaluate AI outputs, and to follow up in deliberate and mindful ways to ensure that the AI-generated answer they ultimately take away is factually accurate, unbiased (to the extent that it can be), and valid for their context. They should also have the ability to recognise when an output is not suitable for their purposes, and when they would be better off approaching a problem or project as they would have in the pre-LLM era. These kinds of metacognition and evaluation skills must be crucial elements of AI literacy training.

How has data changed AI, and how has it impacted CS education?

Over the past 5 to 10 years, the scope, pervasiveness, and complexity of computing applications have grown substantially. This growth has been propelled by developments in AI and machine learning (ML). Many of the ML methods that underpin these developments have been in existence for much longer, but two key ingredients were still needed: large quantities of data, and the requisite computational power to process those quantities of data efficiently. Around 10 years ago, these became a reality. Combining so-called ‘big data’ captured from the countless human activities on the World Wide Web with new, powerful graphics processing units (GPUs) enabled AI scientists to build powerful prediction, classification and, most recently, generative AI models. Thus these scientists ushered in a new paradigm of computing that is data-driven. 

Learners at laptops in a computing classroom.

This has expanded the scope of what we need to teach students as part of CS education. In the context of AI and ML, you now have traditional programs that follow the algorithmic, deterministic paradigm of programming, but also ML applications that follow a data-driven, non-deterministic/probabilistic paradigm. CS curricula must help students develop an understanding of both. And data and data science are the crucial connective tissue between CS and AI/ML, so data literacy (which also captures elements of data agency and data equity) is critical to CS and AI learning experiences. 

Ethical issues in the context of data and AI have become more heightened and pertinent: issues of data privacy, safety, bias, responsible and explainable AI, and most importantly, impacts of AI systems on society. Understanding of these issues — what we can call ‘sociotechnical literacy’ — needs to be much more central to CS education now.

Considering the advances in AI and LLMs, what computing-related skills that we are used to teaching as part of CS are still relevant for young learners?

Let me begin by saying that there is no AI without CS. So understanding CS is important and foundational even in this age of AI and LLMs. The rationale for teaching CS and coding to learners aged 5 to 18 has always been primarily about (a) preparing the next generation to understand, and thrive in, a world where countless aspects of day-to-day life are driven by computing, and (b) providing them with the tools and skills for problem solving and creative expression. That goal has not changed. Foundational coding skills are still important and relevant for learners.

Photo of a class of students at computers, in a computer science classroom.

However, there is the new reality we must contend with: it is now easy to produce accurate code using LLM-based tools. We need good research on what this means in terms of how we teach coding. There are many questions related to this issue for which we need empirical evidence: What are the foundational skills for programming effectively with AI tools? What CS topics, skills, and concepts must we emphasise or de-emphasise? Could teachers be supported by generative AI tools in teaching coding, and if so, how? Will use of AI tools result in poor learning for students? How might students leverage LLM tools in ways that don’t harm their foundational understanding of coding concepts, and at what age and stage? What kinds of LLM tools are safe and suitable, and what preparation must students have before they use them? What bigger, more sophisticated projects might students create with the help of an LLM tool? How might LLM tools aid student learning through formative feedback? Can LLM tools aid in metacognition by prompting reflection at the right moments in a project? These are just some of the many, many questions we need to answer to shape CS education over the coming years.

A version of this interview also appears in issue 29 of Hello World, available as a free download. Subscribe to the magazine to never miss an upcoming issue.

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Experience AI: Reaching millions of young people with AI literacy

Post Syndicated from Anna Burton original https://www.raspberrypi.org/blog/experience-ai-reaching-millions-of-young-people-with-ai-literacy/

AI is shaping the world young people are growing up in, and understanding how it works, as well as its benefits and risks, is now essential.

That’s the goal of Experience AI, our global education programme created in collaboration with Google DeepMind. Today, as we celebrate three years of the programme, we’re sharing our latest impact report, highlighting how the programme is helping educators and young people around the world build the knowledge and confidence to engage with AI critically and responsibly.

From tens of thousands of educators to millions of learners

Since launching 3 years ago in April 2023, Experience AI has grown into a truly global initiative:

  • More than 30,000 educators trained, who can reach an estimated 2.9 million young people
  • Over 700,000 resource downloads across 180+ countries
  • A network of partners in 38 countries
  • Resources available in 19 languages
Infographic

These numbers reflect the growing global demand for AI literacy teaching, and the power of partnerships to meet that need.

But the real impact is what happens in classrooms.

From “AI is complicated” to confident teaching

For John Pierce, a teacher at Mwingo Academy Primary School in Kenya, AI once felt out of reach. “I thought that AI was complicated — maybe a puzzle”

John Pierce, a teacher at Mwingo Academy Primary School in Kenya

After taking part in Experience AI training, John’s perspective shifted. With structured lessons and ready-to-use resources, he now helps his students see AI as something they can understand and engage with.

“My learners are really enjoying the lessons… They keep asking, ‘Teacher, when are you having computer classes?’”

John’s experience reflects what we see across the programme: when teachers feel confident, students become curious, engaged, and motivated to learn more, often continuing those conversations beyond the classroom.

Building confidence, not just knowledge

A core focus of Experience AI is supporting educators, many of whom are new to teaching AI concepts. Our evaluation shows this approach is working:

  • 93% of educators say the training increased their knowledge of AI concepts
  • 87% report increased confidence in teaching AI

In Malaysia, educator Lee Siew Ling had previously struggled to explain AI concepts clearly. “Before this, I just shared simple examples… It was too hard to explain the concepts clearly to my students.”

Lee Siew Ling, educator in Malaysia

With Experience AI resources, that changed. “The materials make it easier to teach AI… After using them, I became more confident to guide my students.”

By reducing preparation time and providing clear, structured lessons, the programme enables teachers to focus on what matters most: supporting their students’ learning.

Helping young people understand, and question, AI

The impact extends directly to learners. Across classrooms worldwide:

  • 89% of students say they better understand what AI and machine learning are
  • 87% say they better understand the benefits and risks of AI

This is critical. AI literacy isn’t just about using technology, it’s about understanding how it works, questioning it, and recognising its societal impact.

Kim Williams, Head of Computing, at Wymondham College in the UK

At Wymondham College in the UK, Head of Computing, Kim Williams highlights the value of having trusted, research-informed resources: “Experience AI gave us a real structure to follow… It helps us deal with misconceptions and gives students the right messages.”

Through these lessons, students are not just learning about AI, they are developing the critical thinking skills they need to navigate a world shaped by it.

A global effort to democratise AI education

Experience AI’s reach is only possible through collaboration. Working with partners around the world, we localise content to make it relevant to different cultures and contexts, ensuring that AI education is not only accessible, but meaningful.

At the award ceremony of the 2025 UNESCO King Hamad Bin Isa Al-Khalifa Prize. © Government of the Kingdom of Bahrain

This work has also been recognised globally. In 2025, Experience AI was named a laureate of the UNESCO King Hamad Bin Isa Al-Khalifa Prize for the Use of ICT in Education, highlighting its strong ethical foundations and international impact.

Looking ahead

We’re continuing to expand and evolve the programme, updating resources, developing new materials for different age groups, and growing our global partner network.

By the end of 2026, we expect to reach over 45,000 educators who can reach an estimated 4.4 million young people.

Because the challenge is clear: AI literacy should not be limited to a few. Every young person deserves the opportunity to understand and shape the technologies influencing their future.

Read the full Experience AI impact report to explore the data, stories, and insights behind this work. rpf.io/expai-impact

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Bringing AI education to 1.25 million students across Latin America

Post Syndicated from Anna Burton original https://www.raspberrypi.org/blog/bringing-ai-education-to-1-25-million-students-across-latin-america/

We’re excited to share that we are expanding our Experience AI programme across Latin America with the aim of training 24,000 educators and reaching 1.25 million students by 2028, thanks to generous funding of $4.6 million from Google.org.

Working with education partners across Argentina, Brazil, Chile, Colombia, Dominican Republic, El Salvador, Mexico, Peru, and Uruguay, we will help young people develop a foundational understanding of AI technologies, their social and ethical implications, and the role that AI can play in their lives.

AI literacy across the globe

AI systems are part of everyday life in how we find information, work, and solve problems. We think that young people need more than access to AI tools: they need the knowledge, skills, and confidence to understand and create their own AI tools.

Experience AI, developed in partnership with Google DeepMind, is a free educational programme that helps teachers and students learn about artificial intelligence (AI). It introduces young people to how AI systems work and how they are used in everyday contexts through lessons, classroom resources, and hands-on activities. The resources give young people opportunities to think critically about the role of AI in society.

The winners of the 2025 UNESCO King Hamad Bin Isa Al-Khalifa Prize.
The winners of the 2025 UNESCO King Hamad Bin Isa Al-Khalifa Prize. © Government of the Kingdom of Bahrain

Through a global network of Experience AI partners, we have so far reached an estimated 2.9m young people and trained 30,000 educators. The programme’s resources are used in more than 180 countries and are available in 19 languages. In recognition of its impact, Experience AI was named a laureate of the 2025 UNESCO King Hamad Bin Isa Al-Khalifa Prize for the Use of ICT in Education.

Impact through partnerships

In Latin America, as in other parts of the Experience AI network, our focus will be on sustainable, locally led delivery through partner organisations. Using our established ‘train-the-trainer’ model, we will equip 24,000 educators with the skills and knowledge to use the Experience AI resources to confidently deliver AI literacy lessons. 

Educators at a workshop

Our aim is to create a lasting impact for teachers and classrooms across the region and ensure that high-quality AI education is accessible to young people in a wide range of settings.

Supporting critical thinking

Experience AI is designed not only to build technical understanding, but also to help young people think critically about AI and its impacts.

Three teenage girls at a laptop.

Through the programme, students across Latin America will develop a foundational understanding of how AI works, while exploring key topics including how data is used in AI systems, how to identify AI-generated misinformation, and how to use generative AI tools responsibly. This will help them to understand the opportunities and challenges of AI, and to make informed decisions about how they use these technologies.

Looking ahead

As AI systems are being built into many aspects of today’s world, it’s essential that young people have the opportunity to understand, question, and build with these technologies.

With support from Google.org, we will expand access to high-quality AI education across Latin America through Experience AI, helping over a million young people develop the skills, knowledge, and confidence to navigate and shape a world where AI technologies are widely used.

You can find out more about Experience AI at experience-ai.org.

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Do you have some rope? Then let’s teach about AI concepts

Post Syndicated from Jane Waite original https://www.raspberrypi.org/blog/do-you-have-some-rope-then-lets-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 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.

Salomey Afua Addo is a third-year PhD student at the Raspberry Pi Computing Education Research Centre, University of Cambridge.
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. 

Students using ropes to signify the strength of connections between nodes.
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.

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.
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).

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). 

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.

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The challenges of measuring AI literacy

Post Syndicated from Bobby Whyte original https://www.raspberrypi.org/blog/the-challenges-of-measuring-ai-literacy/

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.

A girl doing Scratch coding in a Code Club classroom

In a recent seminar in our series on teaching about AI and data science, researcher Jesús Moreno-León (Universidad de Sevilla) talked about his work in developing assessment tools for computational thinking (CT) and AI literacy. Jesús is also co-founder of Programamos, a non-profit organisation that promotes the development of computational thinking, supporting teachers through training and sharing resources.

Jesús Moreno-León (Universidad de Sevilla/Programamos).
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.

Dr Scratch tool.
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.

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?

  1. Train the computer with photos of dogs
  2. Train the computer with several photos of different apples, taken in different places and contexts
  3. Train the computer with several similar photos of the same apple, taken in the same place
  4. 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 with a small, round, yellow apple.

  1. Apple
  2. Watermelon
  3. Lemon
  4. Banana

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 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

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Helping young people stay safe online in the age of AI

Post Syndicated from Rehana Al-Soltane original https://www.raspberrypi.org/blog/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. 

Experience AI Safety Image

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.

Experience AI safety image

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. 

Experience AI safety image

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. 

Experience AI safety image

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.

How to put data first in K–12 AI education by using data case studies

Post Syndicated from Manni Cheung original https://www.raspberrypi.org/blog/how-to-put-data-first-in-k-12-ai-education-by-using-data-case-studies/

In Germany, as in many countries, AI topics are rapidly entering formal computer science education. Yet, this haste often risks us focusing on fleeting technological developments rather than fundamental concepts. As computer science educator Viktoriya Olari, from Free University of Berlin, discovered in her research, the fundamental role of data, which powers most modern AI systems, is critically underestimated in many existing frameworks. If students are to become responsible designers of such systems, they can’t afford to treat AI as an opaque box. Rather, they must first master the messy, human process that begins with the data itself.

Viktoriya Olari, from Free University of Berlin.
Viktoriya Olari

In our October research seminar, Viktoriya shared the results of her work over the last four years on how schools can shift the focus from the latest technologies to the underlying data. Her research offers a clear structure for what young people should learn about data and how teachers can make it work inside ordinary classrooms.

Why begin with data?

Viktoriya’s analysis of existing AI education frameworks found the data domain is underrepresented, with essentials such as data cleaning often not addressed at all. She argues that, because modern AI systems are data driven, students need both language and routines for working with data: being able to name concepts like training vs test data, data quality, and bias, and to explain practices such as collection, cleaning, and pre-processing. That’s the rationale for teaching data concepts and practices first, and then placing modelling inside an explicit, staged lifecycle.

Word clouds for two foundational components: data concepts and data practices.
A slide from Viktoriya’s presentation. Click to enlarge.

Her talk presented this argument in the German school context, where AI topics are entering state curricula quickly. Her critique targets how existing frameworks fail to address data and how that gap undermines responsible evaluation and design. The proposed model centres data by pairing an eight-stage, data-driven lifecycle with a curated set of key concepts and practices, and by making “data-based judgment skills” a key outcome.

Viktoriya’s work organises this understanding into two foundational components: data concepts (the vocabulary, e.g. training/test data, data quality, overfitting) and data practices (the actions, e.g. collect, clean, train, evaluate).

A lifecycle for learning

Viktoriya’s framework is built around an eight-stage data lifecycle, stretching from defining a task through gathering, preparing, modeling, evaluating, and finally sharing or archiving results. Inside that backbone she has identified two layers of learning targets:

  • Data concepts – roughly a hundred ideas that give teachers and students a common language, from “training vs. test data” and “bias” to “features”, “labels”, and “provenance”.
  • Data practices – 28 kinds of hands-on work (and 69 subpractices) that materialise those ideas: for instance collecting, cleaning, splitting datasets, checking quality, training and evaluating models, and handling privacy and deletion responsibly.

More details are available in her work on data-related concepts and practice.

Viktoriya’s 8-stage process model of the data-driven lifecycle.
A slide from Viktoriya’s presentation. Click to enlarge.

Viktoriya’s 8-stage process model of the data-driven lifecycle. It serves as a guide for curriculum developers and teachers, outlining 28 key data-related practices and providing 69 examples of subpractices for use in K–12 computer science education.

A collection of 133 key data-related concepts.
A slide from Viktoriya’s presentation. Click to enlarge.

A collection of 133 key data-related concepts. These concepts are organised according to the eight stages of the data-driven lifecycle and provide the foundational vocabulary for teaching AI education.

Making it teachable

Viktoriya’s team set out to redesign the format so that real data work could happen within ordinary lessons. They ended up with three “Data Case Study” architectures, each using authentic datasets and domain questions. The materials are supported by Orange 3, an unplugged machine learning and data visualisations tool familiar to the teachers participating. Variants emerged across three design cycles to address specific challenges, but teachers choose among them based on learning objectives and class context.

  1. Bottom-up: Students create a workflow step by step (e.g. import, inspect, clean, transform, split, train, evaluate). This approach is excellent for procedural fluency, but teachers reported an over-emphasis on operating Orange and too little reflection on the lifecycle unless explicit reflection is added. 
  2. Top-down: Students start from a prepared workflow, read plots, infer the role of each branch, identify issues in the data/practices, and justify changes. This architecture directly counters the reflection gap seen in bottom-up and leans into reasoning rather than routine. 
  3. Puzzle-like: Using “widgets,” visualisations of data tables, that stand for parts of a data pipeline, students rebuild a valid flow collaboratively. This encourages discussion, works without devices, and makes thinking visible.
The school-specific data case study
A slide from Viktoriya’s presentation.

The data case study method uses real-world data and context to help students achieve three key learning outcomes: go through the data-driven lifecycle, reflect on data practices and concepts in a criteria-guided manner, and develop data-based problem-solving and judgment skills.

What happened in the German classrooms

Viktoriya’s team ran three design cycles with small groups in Germany, with students aged 14 to 15. Each cycle lasted around 48 hours of teaching. Because participating teachers already knew Orange 3, the emphasis was on pedagogy rather than software training.

The projects drew on manageable real-world data: spreadsheets, time-series sets, a few geographical samples. Two examples are:

  • Forecasting Berlin air quality – Students explored how data quality, feature choice, and evaluation metrics shape predictions, then argued which model best answered the civic question.
  • Classifying Tasmanian abalone – A deceptively simple dataset that invites talk about imbalance, feature engineering, and what counts as “good enough” accuracy.

Some groups experimented with collecting their own sensor data, a plan that occasionally failed when the hardware didn’t cooperate. However, even that became part of the lesson: reliability, risk, and missing data are real features of data science, not mistakes to hide.

A computing classroom filled with learners

Student work reflected the three architectures. In the bottom-up groups, guided builds produced complete workflows and concise reflections, while top-down groups submitted annotated screenshots and critiques, and the puzzle-based lessons ended with posters and verbal presentations. Across them all, assessment focused on reasoning: not whether the “right” model appeared, but whether students could explain the stage they were in and justify their choices.

Teaching resources

Everything Viktoriya described is open and classroom-ready (currently in German). The computingeducation.de/proj-datacases hub hosts teacher guides, student tasks, and sample Orange 3 files. The growing library of data cases covers topics from climate data to air quality analytics.

Why it matters now

In the UK, a curriculum review has been recently released and along with the Government’s response. Across Europe and beyond, education systems are racing to add AI content to their curricula. Tools will come and go, and benchmarks will keep moving. What endures is the capacity to reason about data: to know what stage of work you’re in, what evidence supports your decisions, and what trade-offs you’re making. That is why Viktoriya’s contribution is unique — it gives teachers a map, a shared vocabulary, and practical ways to make data visible and the focus of discussion in schools.

You can read this blog to see how we’ve used Viktoriya’s framework in our work designing a data science curriculum for schools.

Join our next seminar

Join us at our seminar on Tuesday 27 January from 17:00 to 18:30 GMT to hear Salomey Afua Addo talk about how to teach about neural networks in Junior High Schools in Ghana.

To sign up and take part, click the button below. We’ll then send you information about joining.

I want to sign up for the next seminar

We hope to see you there. This will be the final seminar in our series on teaching about AI and data science — the next series focuses on how to teach about applied AI across subjects and disciplines.

You can view the schedule of our upcoming seminars, and catch up on past seminars on our previous seminars page.

Teachers in England, take part in our new data science study

We’re looking for upper key stage 2 teachers in England who want to join our new collaborative study exploring how to teach learners aged 9 to 11 about data-driven computing. The study will look at:

  • How Computing teachers currently approach topics related to data-driven computing
  • What key ideas pupils need to understand
  • How pupils make sense of data and probability

Our aim for the study is to find practical ways for Computing teachers to build young people’s confidence in working with data in lessons. The study will involve two workshops held throughout 2026.

Register your interest by filling in this form.

The post How to put data first in K–12 AI education by using data case studies appeared first on Raspberry Pi Foundation.

How AI shapes your feed: An explainable social media simulator for the classroom

Post Syndicated from Diana Kirby original https://www.raspberrypi.org/blog/how-ai-shapes-your-feed-an-explainable-social-media-simulator-for-the-classroom/

Social media can have a powerful impact on the way we see and experience the world. What we see in our feeds is not random: it is determined by AI-driven systems that collect vast amounts of data, build user profiles, analyse engagement, and generate recommendations. But while young people are prolific users of social media, studies show that many have little understanding of what is happening ‘under the hood’

Henriikka Vartiainen and Matti Tedre from the University of Eastern Finland
Researchers Henriikka Vartiainen and Matti Tedre.

In our September research seminar, we welcomed back Henriikka Vartiainen and Matti Tedre from the University of Eastern Finland. They introduced Somekone, a social media simulator that is designed to help learners understand some of the fundamental processes behind social media platforms. Their team has been developing AI education materials and tools since 2019, including GenAI Teachable Machine, which they presented at our May research seminar.

Collaboration and co-design

Henriikka explained that the development of the Somekone tool emerged from the team’s long-term collaboration with teachers and schools in Finland. They co-developed the tool with the aim of making concepts like data collection, engagement, profiling, recommendations, filter bubbles, and polarisation visible and explainable for students aged 11 to 13 years old.

Photo of three school pupils together looking at a mobile phone.

A four-phase learning model

Henriikka described the pedagogical model that the team follows in all of their AI education interventions. Their goal is not only to support students to develop their understanding of AI concepts, but also to foster ethical awareness and a sense of agency.

  • Phase 1: Contextualisation and familiarisation
    Students begin by discussing their experiences with social media and their initial ideas about how platforms such as TikTok, YouTube, and Instagram work. This activates students’ prior knowledge and helps connect the learning to their own interests. It also enables teachers to uncover any misconceptions the students may have.
  • Phase 2: Exploration
    Students explore their initial ideas by experimenting with the Somekone tool. They discover how different types of data are collected and combined for profiling in a way that connects these new concepts to their own everyday lives.
  • Phase 3: Design and inquiry
    Students explore the Somekone tool more deeply. Teachers guide them through activities where the students analyse, interpret, and discuss the data they can see in the tool. Importantly, the data they are using has all been gathered from their activity on the platform. Students can see how the likes, follows, and comments they and their classmates make change the images they are shown, and this is all real time.
  • Phase 4: Ethical and societal reflection
    Students reflect on what they have learnt and consider the broader impacts of social media. Teachers encourage them to think critically, question the way social media platforms currently work, and imagine alternatives. At the end of the project, students write letters to decision-makers with their suggestions for how social media could better serve children’s interests.

Inside the simulator

Matti then gave a live demonstration of Somekone. Nothing compares to seeing the tool in action, so do check out the video of his demo here!

Students log on to the tool and are presented with an Instagram-style feed of images. They scroll through the feed and like, share, or comment on images that catch their attention or match their interests. For many students this is a very familiar type of environment, and they really enjoy playing with the app!

Four young people sitting at their desks, on their mobile phones.

However, the unique value of Somekone is that it provides students with a real-time view of the way data is collected from every single user interaction, and demonstrates what is done with that data. It also allows students to experiment with a social media tool in the classroom without any data protection issues, as all of the data is stored locally.

Learners explore:

  • Data collection in real time. Working in pairs, one student browses the image feed, while the other watches a live view of the data that the simulator is collecting every time their partner interacts with or simply pauses on a post.
  • Profile building. Somekone shows how all this data accumulates to build a profile. Students watch their profiles developing based on the way they and their classmates are interacting with their feeds.
  • Clustering and connections. Students then see how the tool groups profiles to create clusters of users with similar interests. Often friendship groups in the classroom are evident on screen because students sitting next to each other have all chosen to engage with the same things!
The simulator creates clusters of users with similar interests, which update in real time as students interact with posts on their feeds

The simulator creates clusters of users with similar interests, which update in real time as students interact with posts on their feeds

  • Explainable recommendations. A key feature of Somekone is that it provides explanations for why it recommends posts to users. Students learn that recommendations can be based on various things, such as the image’s tag matching the tag on other posts they liked, or the image being popular among other users with similar profiles to theirs. These are the mechanisms that underpin real recommendation systems, but Somekone makes them explicit.
The tool provides an explanation for why each post is recommended

The tool provides an explanation for why each post is recommended

  • Filter bubbles and polarisation. A filter bubble forms when a user only sees social media posts that match their existing interests or beliefs, due to highly personalised recommendation systems. Somekone presents this concept in a visually compelling way through a heatmap showing all the content in the system, with a colour scale indicating which posts are most likely to be shown to a particular user, and which they will never encounter. By comparing different users’ filter bubbles side by side, students start to understand how polarisation can arise. As Matti said: “If our feeds are so different from each other that I never see the pictures that you see and you never see the pictures I see, then […] we don’t even share the same reality”.
Two users’ heatmaps presented side by side, showing their respective filter bubbles

Two users’ heatmaps presented side by side, showing their respective filter bubbles

  • Algorithm settings. A key learning opportunity is that students can adjust the algorithm’s parameters and observe how this changes their feed and their filter bubble. They can choose between personalised or non-personalised recommendations, select how posts are ranked, and decide whether to allow any diversity in the popularity of posts recommended to them. This is key to ‘opening up the box’.

For teachers, the tool has a simple guided interface to make it easy to use in class. There is also a button that teachers can use to pause the app, stopping students from scrolling (much to their dismay!) in order to focus their attention on the teacher when they are explaining concepts.

Evidence of impact

The research team used pre- and post-tests to evaluate what impact the intervention had on students’ understanding of social media mechanisms and on their sense of agency in relation to data. They conducted the post-test a week after the intervention, and then also did a delayed post-test six months later to see whether any changes were sustained. They found:

  • Improved understanding of key concepts. Learners showed statistically significant improvements in identifying different types of data traces and in understanding how data profiling works. They also showed some improvement in grasping recommendation mechanisms.
  • Retention over time. These improvements were generally still evident six months later, particularly in the case of understanding data traces.
  • Stronger sense of agency. The team found that students’ sense of data agency improved after taking part in the intervention. This is really important as students are more likely to want to study a topic further if they have feelings of agency and self-efficacy.

Accessing the tool

The Somekone tool is freely available online — in Finnish, English, German, and French — at somekone.gen-ai.fi. The developer Nick Pope has also made the source code available on GitHub at github.com/knicos/genai-somekone. 

However, the supporting materials and teacher resources are currently only available in Finnish and the underpinning pedagogies relate to the Finnish context.

Join our next seminar

Join us at our next seminar on Tuesday, 11 November from 17:00 to 18:30 GMT to hear Karl-Emil Bilstrup (Copenhagen University) speak about using the micro:bit to explore machine learning practices. We hope to see you there!

To sign up and take part in our research seminars, click below:

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 How AI shapes your feed: An explainable social media simulator for the classroom appeared first on Raspberry Pi Foundation.

What should be included in a data science curriculum for schools?

Post Syndicated from Jan Ander original https://www.raspberrypi.org/blog/what-should-be-included-in-a-data-science-curriculum-for-schools/

Current artificial intelligence (AI) methods, especially machine learning (ML), rely heavily on data. To complement our work on AI literacy, we have been investigating what data science teaching resources and education research are currently available. Our goal is to work out what data science concepts should be taught in a data science curriculum for schools.

In a computing classroom, a smiling girl raises her hand.

Read on to find out what resources and materials we have reviewed, and what concept themes we have identified.

What is data science? Why is teaching it important?

Data science is an interdisciplinary science of learning from large datasets, aided by modern computational tools and methods (Ow‑Yeong et al., 2023). We see data science skills as fundamental for using, creating, and thinking critically about:

  • Insights from data, generally
  • Data-driven computational tools and methods (such as machine learning) and their outputs and predictions, specifically
Someone explains a graph shown on a computer screen.

To navigate a world where decision making in many areas is influenced by data-driven insights and predictions, young people need to be taught about data science. Data science skills empower young people to become critical thinkers, discerning consumers, adaptable professionals, and informed citizens.

Worldwide, countries are taking a variety of approaches to introducing data science into their education systems, as highlighted in a 2024 report from the coalition Data Science 4 Everyone.

An overview of data science education across the world
An overview of data science education across the world. Source: Beyond Borders 2024: Primary and Secondary Data Science Education Around the World, republished with kind permission of Data Science 4 Everyone. Click the image to enlarge it.

In some countries, such as India and Israel, data science education is an established school subject. It is taught as part of the curriculum in at least one of the primary, secondary, or post-16 age phases. Meanwhile in other countries, for example Canada, Germany, and Poland, data science is a very new school subject, or there are still only recommendations to develop it into a school subject.

While we are currently considering what a comprehensive data science curriculum should include, we already offer several resources to support you with your teaching about data science and data-driven technologies. You can find a list of these resources at the end of this blog. Now, however, I’ll give you an overview of our recent work to identify concepts for a data science curriculum that fits with our approach to AI literacy.

Data science education: What should we teach?

To answer the question ‘What should we teach about data science to learners aged 5 to 19?’, we undertook a grey literature review of data science teaching materials. A grey literature review is structured like an academic literature review and conducted with the same rigour. The difference is that a grey literature review also considers publications that have not been peer-reviewed, including reports, white papers, curriculum materials, and similar resources.

To orient our work, we combined four frameworks for data science and AI/ML education:

With these combined frameworks as our map, we reviewed 79 data science learning resources. The resources varied:

  • In quality in terms of clarity and teaching approach
  • In their focus, e.g. on maths, coding, or a specific field such as biology
  • In their perspective on data science, with some prioritising theory and others real-world applications

From among the 79 resources, we chose 9 that included clear learning outcomes, and that together covered a wide field of concepts. We examined these 9 in detail to extract 181 explicit and implicit data science concepts. Next, we grouped the concepts into themes, and finally we refined these themes by comparing them against the four frameworks listed above.

The themes we have identified for a data science curriculum are:

  • Fundamentals of data literacy: Key terms and definitions
  • Understanding bias in data
  • Ethical responsibility in data use
  • Data creation, curation, and transformation
  • Analysis and modelling: Maths and statistics fundamentals
  • ML principles
  • Deploying and maintaining ML applications
  • Software tools and programming
  • Data visualisation
  • Presenting findings effectively

This set of themes both fits with the frameworks by Olari and Romeike and Data Science 4 Everyone, and expands them by covering ML principles and programming approaches and calling out data bias and ethics.

What’s next for this work?

Through our grey literature review on data science education, we’ve:

  • Pinpointed a large set of candidate concepts that could be taught within a data science curriculum
  • Created a set of clear themes to structure our work going forward

Our next step is to shape these candidate concepts into a progression framework to describe their relationships and establish which concepts could be taught at each age or phase of schooling.

Young people studying in a computing classroom.

The literature review also gave us an overview of the pedagogical approaches and tools used for teaching data science concepts. These findings will become useful once we start designing learning activities.

You’ll hear more about how this work is going here on our blog and on our social channels. In the meantime, comment below to let us know what you think about the themes, or to tell us what you’d like to see in a data science curriculum for the learners you work with.

Our resources related to data science

Classroom resources

You can read about our thinking behind the data science-related teaching resources we’ve created so far in our ‘Data and information within the computing curriculum’ report from 2019.

  • The report lists the data-related units within The Computing Curriculum materials, which we no longer update but continue to offer as free downloads. Updated classroom materials are available as part of the Computing materials we created for Oak National Academy in the UK for ages 5–11 and ages 12–19.
  • The Ada Computer Science platform offers learning materials on data and information, and on AI and ML, for ages 14–19.

You might also be interested in exploring the Experience AI programme, which offers everything teachers need to help students develop a foundational understanding of data-driven AI technologies, their social and ethical implications, and the role that AI can play in their lives.

Teacher training and development resources

Our free online course ‘Teach teens computing: Machine learning and AI‘ helps teachers understand and explain the types of problems that ML can help to solve, discuss how AI is changing the world, and think about the ethics of collecting data to train a ML model.

Teaching young people to understand data-driven AI technologies means teaching them thinking skills that are different to those needed to understand rule-based computer systems. You can read about these Computational Thinking 2.0 skills in our Quick Read PDF.

Our current research seminar series focuses on teaching about AI and data science. Sign up for an upcoming seminar session (the next one is on 11 November) or catch up on past sessions to find out what the latest research findings are in this area. You can also revisit our 2021/22 series on the same topic to see how work in this area has developed. The Raspberry Pi Computing Education Research Centre also has ongoing projects in the area of AI education for you to explore.

The post What should be included in a data science curriculum for schools? appeared first on Raspberry Pi Foundation.

Play, pedagogy, and real-world impact: What we learned from the AI Quests webinars

Post Syndicated from Liz Eaton original https://www.raspberrypi.org/blog/play-pedagogy-and-real-world-impact-what-we-learned-from-the-ai-quests-webinars/

Photo of two adult educators sitting around a table with a group of young people playing AI Quests.

How do you teach AI in a way that resonates with 11- to 14-year-olds long after the lesson ends? In two recent Experience AI webinars, we explored that question with collaborators from Google Research, Google DeepMind, and the Stanford Accelerator for Learning. During the webinars, we also showcased AI Quests, a gamified, classroom-first experience where learners use AI concepts to solve real problems.

“The AI technology you’ll experience is amazing, but it’s not magic. Success depends on the decisions you make.”

That line, delivered by Professor Sky, the in-game mentor, captures the core message of AI Quests: AI systems are built by people and shaped by human judgment at every step.

What is AI Quests?

We’ve embedded AI Quests into the Foundations of AI unit in Experience AI, our free AI literacy programme created with Google DeepMind. 

As Google Research’s Liat Ben Rafael explained, “AI Quests is a gamified experience… where students discover firsthand how AI is used in the real world to create positive impact.” Each quest is grounded in a real research programme and mirrors the AI project lifecycle you’ll recognise from our Experience AI lessons: define the problem, prepare data, train, test, deploy.

Photo of a young person playing AI Quests on a laptop. The AI Quests character Luna, can clearly be seen on the young person's screen.

The first quest, Market Marshes, asks students to help Luna, one of the central characters, to protect a riverside market from flooding. Players roam, gather candidate data (from rainfall stats to town gossip), clean it, choose relevant features, and train a model. If the model underperforms, they iterate, exactly as real AI developers would.

Emma Staves, Learning Manager at the Foundation, notes that a key moment is when learners test their model: “It’s made really clear that the data that’s being used to test the model is historic data.” That simple design choice can help you to unlock rich discussions with your learners about validation, reliability, and what counts as “accurate enough” for real decisions.

Designed around how students actually learn

Developed in collaboration with learning scientists at the Stanford Accelerator for Learning, the quests reflect what Victor Lee, Faculty Lead for AI and Education at Stanford, describes as “enduring understanding”:

“The enduring understanding is about how humans can initiate and design AI applications that can address some of humanity’s biggest unsolved challenges.”

To keep that focus, the team blends:

  • Situated learning – for example, a concrete flood scenario rather than abstract exercises
  • Pedagogical agents – characters who nudge, model, and explain
  • Embedded feedback and productive failure – learn by trying, revising, and trying again
  • Self-explanation prompts – ‘learning tickets’ that ask students to articulate what they’re doing and why

In other words, the quests are all about playing with purpose.

What teachers are seeing in the classroom

We piloted AI Quests with some teachers, including Dave Cross, Curriculum Leader for Computer Science at North Liverpool Academy, who tested the quests with his Year 7 students, before extending it to his GCSE classes:

“We see it moving forward as a really solid foundation… for that further learning.”

He also saw strong cross-curricular ties: geography colleagues spotted “massive opportunities” to use the flood quest in their own units, while broader staff discussions turned to digital citizenship, data literacy, and fairness. The cross-disciplinary nature of AI is increasingly apparent, and so AI literacy shouldn’t be limited to computing — students need to encounter AI across multiple subjects and in everyday life.

Where the research comes in: Forecasting floods days in advance

Graphic of the flooded marketplace from the Market Marshes quest on AI Quests.

The second webinar connected the classroom experience to the real project it’s modelled on: Google Research Flood Forecasting. Gila Loike, Product Manager, set the scene:

“Our research team develops AI models that predict flooding all over the world, five to seven days before the flood occurs.”

Deborah Cohen, the Research Scientist leading the team at Google Research focused on flooding, also explained that traditional models can’t easily predict floods in places with little data. However, AI can fill those gaps by combining information from rivers, weather forecasts, and satellites, to give accurate warnings around the world:

“With AI we were able to expand our coverage to the entire world.”

The results are real and practical. Accurate predictions help:

  • People stay safe by receiving flood alerts through familiar apps
  • Emergency teams plan routes and close roads in time
  • Farmers decide whether to move animals or harvest early
  • Aid organisations act sooner, delivering supplies or financial support before the flood hits
Graphic from the Market Marshes quest on AI Quests.

To make sure their models work well, the team compares predictions with real river data, where available, and with satellite images showing flooded areas. Students explore these same ideas in the AI Quest game, cleaning messy data, testing their models, and checking how accurate their results are.

“Students are really engaged by the real-world challenge,” said Emma Staves about the Market Marshes quest. “That authenticity makes learning come alive.” It helps students see how classroom ideas, like features, accuracy, bias, and model cards, connect directly to real decisions and their consequences.

Coming soon: Health quests and more languages

Graphic from the second AI Quests.

Liat also gave a sneak peek at the next quest, a health-focused story on blindness prevention. It introduces new layers — privacy, diverse data, field testing — while following the same lifecycle. More quests are in development, with additional languages planned from early 2026.

Why this matters now

The key message from both webinars is clear: AI literacy isn’t just about using technology — it’s about understanding our role in shaping it. As one Stanford researcher put it, “AI isn’t this magic thing that just happens to us. Humans decide how to use it, and how choices around data affect accuracy and fairness.”

Our goal with Experience AI is to help young people become thoughtful, creative problem-solvers who can navigate an AI-powered world with confidence and integrity — and AI Quests fits perfectly with that.

Find out more

You can watch both webinars anytime on our YouTube and LinkedIn channels

Webinar 1: LinkedIn, YouTube
Webinar 2: LinkedIn, YouTube

Explore our Experience AI resources — already used by nearly two million learners and educators to understand, question, and create with AI — to bring them and AI Quests into your classroom. You’ll find the Foundations of AI unit, alongside materials on large language models, ecosystems and AI, and AI safety, at rpf.io/experienceai-resources

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Experience AI receives global recognition from UNESCO

Post Syndicated from Philip Colligan, CBE original https://www.raspberrypi.org/blog/experience-ai-recognition-unesco/

I am very proud to share the news that Experience AI has been recognised as a laureate for the 2025 UNESCO King Hamad Bin Isa Al-Khalifa Prize for the Use of ICT in Education.

The winners of the 2025 UNESCO King Hamad Bin Isa Al-Khalifa Prize.
At the award ceremony of the 2025 UNESCO King Hamad Bin Isa Al-Khalifa Prize. © Government of the Kingdom of Bahrain

What is Experience AI?

Experience AI is a free educational programme that helps teachers and students learn about artificial intelligence (AI). It was developed by the Raspberry Pi Foundation in partnership with Google DeepMind and includes lessons, classroom resources, and hands-on activities to help students develop a foundational understanding of AI technologies, their social and ethical implications, and the role that AI can play in their lives.

It is based on original research into AI literacy and highlights real-world applications of AI technologies, including through videos featuring research scientists that help to bring the lessons to life for students. 

A group of students and educators at Penang Science Cluster's launch of Experience AI in Malaysia.
A group of students and educators at the launch of Experience AI in Malaysia.

Since we launched the first Experience AI resources in April 2023, they have been used to teach over 2 million students, and that number is growing fast. 

This reach is possible thanks to a global network of Experience AI education partners who work with us to localise and translate the resources and deliver large-scale teacher training in their regions. 

UNESCO recognition

Experience AI was one of four laureates of the prestigious global Prize selected by the Director-General of UNESCO, based on recommendations from an independent, international jury. The jury commended the programme for its strong ethical foundation and wide international reach. 

This year, marking its 20th anniversary, the Prize focused on the theme ‘Preparing learners and teachers for the ethical and responsible use of artificial intelligence’.

Students in class during an Experience AI lesson.
Romanian students in class during an Experience AI lesson.

The Prize was awarded at a ceremony at the University of Bahrain attended by the Director-General of UNESCO, Ministers of Education of the Gulf Cooperation Council, and members of the Raspberry Pi Foundation and Google DeepMind teams.

I want to say a heartfelt congratulations and thank you to everyone who has worked on Experience AI so far. It has been a fantastic, collaborative effort from colleagues across the Foundation, Google DeepMind, and all of our partner organisations. 

I also want to pay tribute to all of the teachers — all over the world — who have engaged so enthusiastically with Experience AI, for helping us develop the materials, including testing them in your classrooms and providing such thoughtful feedback, and for everything you do, every day, to inspire your students. This recognition is for all of your hard work, diligence, and care. Congratulations and thank you.

Teachers in Kenya during an Experience AI teacher training event.
Teachers in Kenya during an Experience AI teacher training event.

Experience AI is provided at no cost to schools, teachers, or students thanks to generous funding from Google.org. We are also very grateful to Broadcom Foundation, which has provided additional funding to support the programme. 

What next for Experience AI? 

We are exceptionally proud to have received this recognition for Experience AI, but we aren’t complacent. Equipping all young people, and their teachers, with a foundational understanding of AI technologies is one of the most urgent challenges facing all education systems.

We have made a great start, and we know there is much more to be done. That’s why we have lots of important updates and developments coming soon, including: 

  • Updating and improving the current lessons: We are finalising an update to the resources to respond to feedback from teachers and students, including significant improvements to make them more accessible. These will be published early in 2026. 
  • Expanding the range of lessons: Alongside the updates to the existing resources, we are developing new lessons. This will include lessons designed for both younger and older learners, as well as integrated lessons that enable teachers to bring AI concepts and skills into subjects such as science, language, and the arts. 
  • Updated and improved professional development: We are also updating and improving the training that we offer to teachers, including both online courses and webinars, and in-person training delivered through the global network of education partners. 
  • AI chatbot for educators: We recently integrated a chatbot into the Experience AI website. Powered by Gemini 2.5, this is intended as a tool to help teachers navigate and understand the concepts and lessons. This is an early experiment and we’d love to get your feedback, so please give it a try and let us know what you think. 
  • Expanding the global network of partners: We currently have partners supporting teacher professional development in 25 countries, from Malaysia to Mexico. Over the coming year we will be launching partnerships in at least 15 more countries. If your organisation is interested in becoming a partner, you can let us know by filling in this form.

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Introducing AI Quests: A new gamified learning experience within Experience AI

Post Syndicated from Liz Eaton original https://www.raspberrypi.org/blog/introducing-ai-quests-a-new-gamified-learning-experience-within-experience-ai/

Artificial intelligence (AI) tools are shaping our world in many ways. Helping young people develop AI literacy — in other words, helping them understand how AI tools work and how to use them responsibly — is essential. 

At the Raspberry Pi Foundation, we’re committed to empowering educators around the world with everything they need to teach AI confidently and help young people develop AI literacy. That’s why we developed Experience AI: a set of high-quality AI literacy resources designed in collaboration with Google DeepMind that any educator can use, no matter their level of tech knowledge.  

AI Quests

We’re excited to introduce a new addition to the Experience AI resources: AI Quests.

Enter AI Quests

Developed by Google Research in collaboration with the Stanford Accelerator for Learning, AI Quests is a browser-based learning experience that lets students step into the role of AI researchers. Through interactive, story-driven activities, they’ll explore essential AI topics such as:

  • Data preparation
  • Testing and evaluation
  • Bias in AI systems

Students will use what they learn about these topics to tackle simulated global challenges. The first quest, Market Marshes, introduces them to how AI technology can be used in flood forecasting, while upcoming quests will explore other real-world issues.

Why AI Quests matters

AI technology is frequently used but often poorly understood. AI Quests, like Experience AI more broadly, gives students practical experience of how AI technology works, and shows why it’s so important. 

Market Marshes AI Quest start screen

Here’s what sets AI Quests apart:

  • Gamified learning: Storytelling and role play turn abstract ideas into immersive experiences
  • Real-world relevance: Students see how AI technology addresses challenges like climate resilience and health equity
  • No prior knowledge required: Any teacher, regardless of subject specialism, can bring AI Quests into their classroom
  • Developed by experts: Built with Google Research and the Stanford Accelerator for Learning, content is high-quality and credible

What’s next

AI Quests is launching in English, with future plans for translations and additional quests to reach even more learners globally.

To support educators, we’re also hosting two free webinars on YouTube and LinkedIn:

  • 9 October at 4pm BST
  • 16 October at 4pm BST
AI Quests, search for data mid game screen

These sessions will walk you through AI Quests, offer classroom tips, and give you the chance to ask questions directly.

Register now on LinkedIn or subscribe on YouTube to get notified and join the live session. 

Ready to get started?

AI literacy is one of the most valuable skills young people can develop today. With this new addition to Experience AI, we’re making it even more engaging, practical, and accessible for classrooms everywhere.

Explore AI Quests in lesson 6 of our Experience AI resources.

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Teaching Experience AI: Lessons from educators in Mexico

Post Syndicated from Liz Eaton original https://www.raspberrypi.org/blog/teaching-experience-ai-lessons-from-educators-in-mexico/

In classrooms across Mexico, a transformation is unfolding. The Experience AI programme isn’t just teaching students about artificial intelligence, it’s empowering teachers and learners to explore, question, and create with it. By equipping educators with accessible tools and sparking curiosity among students, the initiative is shaping a new generation ready to use AI responsibly and creatively.

Teacher at the front of the classroom

Educators like Guadalupe Cortes, Lilia Violeta Garvia Sanchez, Ines Martinez, and Ana Judith Zavaleta are at the forefront of this shift. Their experiences reveal just how transformative Experience AI has become.

From fear to fascination: Demystifying AI

For many, AI can feel abstract, something from science fiction. Science and math teacher Lilia Violeta Garvia Sanchez remembers that both she and her students once viewed AI as “robots that would take over the world.” Fear gave way to fascination, however, once Experience AI entered the classroom.

Through hands-on lessons, students quickly discovered AI as a practical tool rather than a threat. “I’ve seen a change in the students,” Lilia explains. “They were afraid at first, but now they’re curious and engaged.”

Technology teacher Ines Martinez admits she was also surprised: “I thought the language would be more technical or complex, but it was pleasantly accessible — and very useful.”

Equipping educators with tools that work

A defining strength of Experience AI is its adaptability. Teachers can tailor materials to fit their classrooms while still leaning on the program’s robust foundation.

Guadalupe Cortes points to the built-in glossary as a game-changer: “It was really helpful for me.” She values being able to choose what fits her teaching to keep it relevant: “I selected certain parts to connect with projects I was already running.”

Sparking critical thinking and ethical awareness

Experience AI pushes students to think deeply about the ethics and implications of AI.

In Ines’s class, students raised concerns about water use in data centres and debated how to protect their digital identities. They weren’t just learning facts, they were making connections to real-world issues.

Educator supporting young learner in the classroom

Another teacher, Ileana Beurini, described an exercise where students asked different AI models the same political question. When answers varied, they discussed bias and the importance of consulting multiple sources. In another activity, searching images of “worker” led to a conversation about gender equity in technology.

As Ines puts it: “They don’t want it to do all the thinking for them. They said it should be a support — a tool to generate better information, not to replace reasoning or reflection.”

Transforming engagement and performance

The impact on student motivation has been striking. For Ana Judith Zavaleta, the shift was clear: “They’re much more hands-on now — they don’t rely as much on textbooks or theory.” One student who typically struggled academically became one of the most enthusiastic participants, even passing where he previously failed.

Guadalupe Cortes has seen similar enthusiasm: “They’re finding a real purpose in using AI for their own benefit.” At an entrepreneurship fair, her students applied AI concepts to improve their projects, proof that these lessons extend far beyond the classroom.

A call to action for educators

The teachers’ message is unanimous: embrace AI.

“We should give it a try,” urges Lilia. “Just because we’re teachers doesn’t mean we have to know everything. The world is evolving every single day, and we need to innovate with our students so they feel motivated to keep learning.”

Young learners work on the classroom wall

For Ines, the takeaway is simple but powerful: “Take the risk — really, take the chance to learn. Just like the internet became essential, AI will become part of our daily lives and necessary for all areas of teaching — and life itself.”

More than just a set of resources

Experience AI is more than a set of resources, it’s a movement preparing students to navigate the future with curiosity, critical thinking, and ethical awareness. By igniting minds in Mexico, it’s helping to cultivate responsible digital citizens who will shape not just their classrooms, but the world beyond them.

For more information about Experience AI, visit our website: rpf.io/experienceai

For more information about our global Experience AI partner in Mexico, visit: educacionparacompartir.org

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