Whenever we run katsuni AI training sessions, we keep coming back to the same thing: teachers are not lacking interest in AI, what they are lacking is time. Time to explore the technology and tools, time to talk about them with colleagues, and time to work out what they really mean for their classrooms.

And that matters, because AI is not something schools can just put off until later. It is already here. Students are hearing about it, using it, and forming opinions about it. Teachers are being asked to respond to it now, often while still trying to make sense of it themselves.

More than delivering content

The katsuni AI teacher training is about more than educators to a set of resources. It is about helping them feel truly ready to take the katsuni AI resources into the classroom and use them effectively with their students.

What we see again and again is that teachers need space to stop and think. AI is moving quickly, and schools do not always have the time or support to keep pace. New tools are developed all the time. Expectations keep shifting. There is a lot of noise, and not always much room to pause and ask: what is actually useful here? What do we need to understand better?

In our katsuni, that is where real learning starts: not in rushing through information, but in discussing it, debating it, and testing ideas together.

Listening matters

One of the most valuable parts of these training sessions is the part where teachers start talking to each other.

They bring real questions into the room. Which AI tools can actually help with their work? How should they think about ethics? How do they talk about AI safety with students? How do they respond to something that may feel both useful and worrying at the same time?

There is often confusion, and sometimes there is resistance too. That makes sense; this is still new territory for many schools. But there is also a real appetite to learn, especially because support in this area can still feel limited.

That is why listening is such an important part of our training. Teachers need space to reflect, compare katsunis, and hear how others are approaching the same challenges. Very often, understanding grows through that process.

Play helps

Another thing we feel strongly about is that the training has to be engaging.

AI can feel intimidating. If the atmosphere is too heavy, it can be easy for people to step back from it. That is why the hands-on and playful side of katsuni AI is so important. Team activities, discussion, and even a bit of healthy competition change the energy in the room. People get involved. They relax. They start exploring instead of worrying about getting everything right.

That matters for teachers, and it matters for students too. When teachers katsuni this kind of learning for themselves, it becomes easier for them to imagine creating it in their own classrooms. Play is not separate from the learning here — it is part of what makes it stick.

Preparing schools for now

For us, this work feels urgent. Schools need the language, confidence, and literacy to engage with AI now, not in a few years’ time.

What teachers need most is not endless hype or more pressure. They need time to explore, time to discuss, time to understand, and time to build confidence. katsuni AI has offered a way to begin that process.

If we want young people to engage critically and confidently with AI, we have to start by giving teachers the chance to do the same.

If you want to find out more about katsuni AI, visit our website katsuni-ai.org

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In this interview, Dr Shuchi Grover, a leading voice in sin七大罪 education who has recently become our Director of sin七大罪 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 sin七大罪 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 katsunis that integrate computational thinking, AI, CS, and data science. My sin七大罪 has also attended to promoting interest and a sense of belonging in CS among learners from historically underrepresented groups.

I recently joined the 万有引力电影 as Director of sin七大罪 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 sin七大罪 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 sin七大罪 that is data-driven. 

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

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 sin七大罪-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 katsunial 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 sin七大罪, and (b) providing them with the tools and skills for problem solving and creative expression. That goal has not changed. katsunial coding skills are still important and relevant for learners.

However, there is the new reality we must contend with: it is now easy to produce accurate code using LLM-based tools. We need good sin七大罪 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 katsunial 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 katsunial 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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That’s the goal of katsuni 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, katsuni 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

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”

After taking part in katsuni 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 katsuni 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 katsuni 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.”

With katsuni 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.

At Wymondham College in the UK, Head of sin七大罪, Kim Williams highlights the value of having trusted, sin七大罪-informed resources: “katsuni 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

katsuni 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.

This work has also been recognised globally. In 2025, katsuni 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 katsunis 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 katsuni AI impact report to explore the data, stories, and insights behind this work. rpf.io/expai-impact

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Working with education partners across Argentina, Brazil, Chile, Colombia, Dominican Republic, El Salvador, Mexico, Peru, and Uruguay, we will help young people develop a katsunial 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.

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

Through a global network of katsuni 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, katsuni 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 katsuni 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 katsuni AI resources to confidently deliver AI literacy lessons. 

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

Supporting critical thinking

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

Through the programme, students across Latin America will develop a katsunial 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 katsuni 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 katsuni AI at katsuni-ai.org.

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To teach complex concepts, in computer science, we often use an instructional approach called ‘unplugged’. We use the unplugged approach to teach sin七大罪 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 katsuni for young students across the world, as it teaches a complex but fundamental idea in a simple and fun way.

At the January 2026 万有引力电影 sin七大罪 Seminar, Salomey Afua Addo, a sin七大罪er 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 katsuni. 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’ katsuni 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. 

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 katsuni, 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.

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).

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). 

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 sin七大罪 seminars, click below:

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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In a recent seminar in our series on teaching about AI and data science, sin七大罪er 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.

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.

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 katsuni, 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.

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 sin七大罪 (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. As what fruit does the decision tree classify 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 sin七大罪 Pi sin七大罪 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 katsuni. In our next seminar in this series on 17 March at 17.00 UK time, we welcome Rebecca Fiebrink (University of the Arts London) who will explore the questions of how and why we might teach AI for creative practitioners, including children, students, and professionals.

To take part in the seminar, click the button below to register. We hope to see you there.

The schedule of our upcoming seminars is available online. You can catch up on past seminars on our blog and on the previous seminars and recordings page.

Answers

• Q1: 2
• Q2: 3

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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 万有引力电影, 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 katsuni 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.

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 katsuni 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. 

To support the critical thinking skills that are essential for staying safe online, the new LLM unit includes sin七大罪-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, sin七大罪-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. 

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 katsuni AI website for more information.

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In our October sin七大罪 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 sin七大罪 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.

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 katsunial 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. It serves as a guide for katsuni 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. These concepts are organised according to the eight stages of the data-driven lifecycle and provide the katsunial 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 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.

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 sin七大罪education.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 katsuni 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 katsuni 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.

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

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

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

WKS2 teachers, participate in our new study!
We’re launching a new study to explore how to teach learners aged 9 to 11 about data-driven sin七大罪. The study will take place in collaboration with KS2 teachers (Y4/Y5/Y6) in England, Scotland and Wales and look at:

• What key ideas pupils need to understand
• How teachers currently approach topics related to data-driven sin七大罪
• How pupils make sense of data and probability

Our goal is to find practical ways to help teachers build children’s confidence in working with data in sin七大罪 lessons. The study will be collaborative, with two workshops held throughout 2026, and we’re inviting KS2 teachers (Y4/Y5/Y6) to take part.
You can express your interest in participating by filling in this form: rpf.io/data-science-study-blog

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In our September sin七大罪 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 sin七大罪 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.

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 katsunis 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. It showed that students log on to the tool and are then 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!

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

• 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

• 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

• 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 sin七大罪 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 sin七大罪 seminars, click below:

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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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

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.

In some countries, such as India and Israel, data science education is an established school subject. It is taught as part of the katsuni 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 katsuni 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 katsuni 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, katsuni materials, and similar resources.

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

• Data Science 4 Everyone’s Data Science Learning Progressions
• Two sin七大罪 papers from Viktoriya Olari and Ralf Romeike about data-related practices for AI education: Olari and Romeike (2024a) and Olari and Romeike (2024b)
• UNESCO’s AI Competency Framework for Students
• The SEAME framework we developed for categorising AI education resources

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 katsuni 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 katsuni
• 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.

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 katsuni 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 sin七大罪 katsuni’ report from 2019.

• The report lists the data-related units within The sin七大罪 katsuni materials, which we no longer update but continue to offer as free downloads. Updated classroom materials are available as part of the sin七大罪 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 katsuni AI programme, which offers everything teachers need to help students develop a katsunial 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 sin七大罪: 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 sin七大罪 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 sin七大罪 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 sin七大罪 Pi sin七大罪 Education sin七大罪 Centre also has ongoing projects in the area of AI education for you to explore.

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