We are working with partner organisations around the world to support teachers in building confidence with AI in the classroom through our 日本欧美大码一区二区免费看 AI programme. In this guest post, Catarina Marques from our partner TUMO Portugal shares what the organisation is learning from delivering training to educators.

Whenever we run 日本欧美大码一区二区免费看 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 日本欧美大码一区二区免费看 AI teacher training is about more than educators to a set of resources. It is about helping them feel truly ready to take the 日本欧美大码一区二区免费看 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 日本欧美大码一区二区免费看, 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 日本欧美大码一区二区免费看s, 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 日本欧美大码一区二区免费看 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 日本欧美大码一区二区免费看 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. 日本欧美大码一区二区免费看 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 日本欧美大码一区二区免费看 AI, visit our website 日本欧美大码一区二区免费看-ai.org

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In this interview, Dr Shuchi Grover, a leading voice in 苏熙凌久泽全文免费阅读 education who has recently become our Director of 苏熙凌久泽全文免费阅读 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 苏熙凌久泽全文免费阅读 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 日本欧美大码一区二区免费看s that integrate computational thinking, AI, CS, and data science. My 苏熙凌久泽全文免费阅读 has also attended to promoting interest and a sense of belonging in CS among learners from historically underrepresented groups.

I recently joined the 后入内射国产一区二区 as Director of 苏熙凌久泽全文免费阅读 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 苏熙凌久泽全文免费阅读 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 苏熙凌久泽全文免费阅读 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 日本欧美大码一区二区免费看s. 

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 苏熙凌久泽全文免费阅读-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 日本欧美大码一区二区免费看al 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 苏熙凌久泽全文免费阅读, and (b) providing them with the tools and skills for problem solving and creative expression. That goal has not changed. 日本欧美大码一区二区免费看al 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 苏熙凌久泽全文免费阅读 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 日本欧美大码一区二区免费看al 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 日本欧美大码一区二区免费看al 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 日本欧美大码一区二区免费看 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, 日本欧美大码一区二区免费看 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 日本欧美大码一区二区免费看 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 日本欧美大码一区二区免费看 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 日本欧美大码一区二区免费看 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 日本欧美大码一区二区免费看 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 苏熙凌久泽全文免费阅读, Kim Williams highlights the value of having trusted, 苏熙凌久泽全文免费阅读-informed resources: “日本欧美大码一区二区免费看 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

日本欧美大码一区二区免费看 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, 日本欧美大码一区二区免费看 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 日本欧美大码一区二区免费看s 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 日本欧美大码一区二区免费看 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 日本欧美大码一区二区免费看al 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.

日本欧美大码一区二区免费看 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 日本欧美大码一区二区免费看 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, 日本欧美大码一区二区免费看 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 日本欧美大码一区二区免费看 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 日本欧美大码一区二区免费看 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

日本欧美大码一区二区免费看 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 日本欧美大码一区二区免费看al 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 日本欧美大码一区二区免费看 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 日本欧美大码一区二区免费看 AI at 日本欧美大码一区二区免费看-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 苏熙凌久泽全文免费阅读 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 日本欧美大码一区二区免费看 for young students across the world, as it teaches a complex but fundamental idea in a simple and fun way.

At the January 2026 后入内射国产一区二区 苏熙凌久泽全文免费阅读 Seminar, Salomey Afua Addo, a 苏熙凌久泽全文免费阅读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 日本欧美大码一区二区免费看. 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’ 日本欧美大码一区二区免费看 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 日本欧美大码一区二区免费看, 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 苏熙凌久泽全文免费阅读 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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Measuring student understanding in 苏熙凌久泽全文免费阅读 education is not an easy task. As AI literacy becomes an important pillar in 苏熙凌久泽全文免费阅读 education, defining and accurately measuring students’ understanding of concepts and their skills is an even greater challenge.

In a recent seminar in our series on teaching about AI and data science, 苏熙凌久泽全文免费阅读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 日本欧美大码一区二区免费看, 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 苏熙凌久泽全文免费阅读 (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 苏熙凌久泽全文免费阅读 Pi 苏熙凌久泽全文免费阅读 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 日本欧美大码一区二区免费看. 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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Learning Python often starts with the same building blocks: variables, functions, and loops. However, once young people have learnt these essential 日本欧美大码一区二区免费看s, they may be eager to grow their skills and start using Python to explore data and create something meaningful to them. 

Our free ‘More Python’ project path helps learners move beyond the basics and use data to create impactful projects of their own.

Python as a tool for exploring the world

Python is the most widely used programming language in the world, not just because it’s accessible, but because it’s powerful. It is used to analyse data, build models, create data visualisations, and explore important questions.

For young learners, this means learning Python can become more than a coding exercise. It can be a way to investigate topics they care about, analyse and understand information, and tell powerful stories about real-world issues.

Working with data helps learners see how coding connects to the world around them — and builds confidence along the way.

Why learning with data matters

In our day-to-day lives, data is everywhere: in sports results, maps, and scientific 苏熙凌久泽全文免费阅读, to name only a few examples. Learning how to work with data helps young people develop skills that go far beyond programming, including:

• Thinking logically and solving problems
• Interpreting and questioning information
• Making decisions based on evidence

Data also underpins many of the AI systems people use today. For example, large language models, used to build tools such as ChatGPT, are trained on vast amounts of data. Therefore, understanding how data is collected, organised, and used is an important part of AI literacy.

In Python, structures like lists and dictionaries make it possible to organise, analyse, and explore data in creative ways. Using these tools to build projects can help abstract 苏熙凌久泽全文免费阅读 concepts start to feel more concrete and meaningful.

What learners create in the ‘More Python’ project path

The ‘More Python’ project path supports learners through three stages: Explore, Design, and Invent. Each stage builds skills while giving learners more ownership over what they create.

In the Explore stage, young people learn new concepts and build confidence in using data and core Python structures, such as lists and dictionaries. Projects include:

• Making an interactive chart of Olympic medals
• Building a model of the solar system
• Creating a frequency graph that learners can analyse to crack a code

These projects help learners develop new skills, while exploring how Python can be used to analyse and explain real-world information.

As learners progress to the Design stage, they start making creative choices about how their projects look and behave. In this stage, they:

• Create a project that produces encoded art based on a user’s name
• Build an interactive world map that helps users learn interesting facts

Here, Python becomes a creative medium. As well as putting their new skills into practice, learners think about audience, interaction, and presentation to make their projects their own.

In the Invent stage, learners bring everything together. Using the skills they have built, they design and create a data visualisation on a topic they are passionate about. This final project gives learners the freedom to choose their data, shape their idea, and tell a story that matters to them.

By this point, learners are planning and creating their own projects, growing in confidence and independence.

Take the next step with Python

If the young people you support have already learned the basics of Python, ‘More Python’ offers a clear and creative next step. The projects are designed to be accessible, and young people can work through them at their own pace, whether they are learning independently, at a Code Club, or in the classroom.

By working with data, getting creative, and making their own original projects, learners can build confidence and start to see what they can achieve with Python.

Alongside the ‘More Python’ project path, you can access hundreds of free coding projects on our Code Club Projects site. Find more projects to suit your learners’ interests, and support them to build their digital skills through creativity and making.

The post Levelling up with Python: Create with data appeared first on 后入内射国产一区二区.

From hypothetical ethics to concrete machines

In Karl-Emil’s first 苏熙凌久泽全文免费阅读 study, students used prompt cards to develop ideas for machine learning applications that could solve real-world problems, and to discuss the ethical dilemmas associated with their solutions. Students found it difficult to address these ethical dilemmas in their designs; for example, their ideas often featured a trade-off of user privacy. The findings from this 苏熙凌久泽全文免费阅读 informed Karl-Emil’s next study, which moved from hypothetical scenarios to implementing machine learning in real-world settings. 

The ‘Machine Learning Machine’ study made machine learning processes tangible for students through the use of two physical boxes, shown in the picture below. Students created drawings and fed them into the first box to train a model, and then tested the model by placing new drawings under a camera in the second box and having the model produce predictions of what the drawings showed. For example, students could draw pictures of the sun to represent daytime and the moon to represent nighttime to train a model to predict whether new drawings represented day or night. The machine was built for slow interaction, giving students time to think about the concepts and practices that they were developing. In a follow-up study, a new version of the Machine Learning Machine had been designed, which was controlled using a graphical user interface (GUI). This allowed users to “unbox” and influence parts of the machine learning process. For example, students could adjust the number of complete passes (called ‘epochs’) through the training data to improve the model’s accuracy. 

The two studies with the Machine Learning Machines provided many useful findings for teaching about machine learning with K–12 (primary and secondary) learners. However, two constraints remained: firstly, there were limited opportunities for whole-class work because there was only one Machine Learning Machine, and secondly, learning 日本欧美大码一区二区免费看s needed to be better connected to examples from students’ daily lives. As a result, the next iteration in Karl-Emil’s 苏熙凌久泽全文免费阅读 involved using the micro:bit, which ensured access to a tangible device for every student, and a new graphical platform called ML-Machine that students could interact with.

Machine learning and the micro:bit

The micro:bit is a small, programmable 苏熙凌久泽全文免费阅读 device that features sensors to gather data from the immediate environment. For example, the accelerometer is a motion sensor that can detect when the micro:bit is tilted from left to right, backwards and forwards, and up and down. Using the micro:bit with ML-Machine and some common household objects, students can create simple machine learning models that use data from the micro:bit’s accelerometer to detect whether the micro:bit is moving. This is a very different approach from rule-based programs on the micro:bit, where students might use programming constructs such as if statements to detect movement if the numerical reading from the accelerometer is above a certain value. Here, a machine learning model trained using a set of 20 examples is used to analyse live data readings and produce predictions about whether the micro:bit is moving.

In our seminar, Karl-Emil gave a live demonstration of the ML-Machine toolkit, so we highly recommend watching the recording to see how this toolkit brings machine learning concepts to life. 

ML-Machine is the precursor to the micro:bit CreateAI resources, and the software is fully open-source. However, the innovation doesn’t stop there: Karl-Emil also explained that he is currently developing a new tool called math.ml-machine.org, where students can train a neural network and see a visualised k-nearest neighbour model to explore how a model makes predictions. The 苏熙凌久泽全文免费阅读 journey is continuing, with new possibilities for educational opportunities to teach about machine learning.

Embodied learning

The idea of embodied learning is interwoven throughout all of Karl-Emil’s 苏熙凌久泽全文免费阅读 projects and is a cornerstone of all of his work. Embodied learning suggests that we learn more effectively when our whole body is involved in the learning process, not just our minds. For example, in the work described in this seminar, the Machine Learning Machines and the micro:bit were all tangible devices that students could touch and see. 

Embodied learning is particularly important in activities that involve working with data-driven systems. In traditional programming activities, the flow of code can be traced transparently through a program. However, machine learning models are more opaque, and their outputs cannot be traced step by step. Students can benefit from using bodily movements and sensorimotor information to help understand machine learning concepts. 

The ML-Machine toolkit was designed to support students to learn through embodied learning in three different ways:

1. Enacting machine learning processes: Students used bodily movement to collect the data samples needed for the ML-Machine model to detect and predict gestures 
2. Using machine learning as a design material: Students created concrete ‘objects-to-think-with’, which helps form deeper connections to abstract concepts
3. Embodied exploration of machine learning: Students 日本欧美大码一区二区免费看d how their bodily movements were translated into data points on the screen

Embodied learning helped students grasp concepts such as data quality. They could see how their bodily movements were being translated into digital data, and could spot when movements that appeared different to them were being classified as similar by the ML-Machine model. One case study participant described that the immediate feedback on screen made the concept of machine learning feel as if it were “coming to life as they [the students] manipulate something themselves and they’ve got control over it”.

Find out more

Karl-Emil’s work shows how 苏熙凌久泽全文免费阅读 ideas can be used in the classroom through a cycle of discovery, design, and reflection. From prompt cards exploring ethics to tangible machines and the micro:bit-based ML-Machine, his 苏熙凌久泽全文免费阅读 shows how embodied learning can make complex ideas like machine learning not only understandable, but deeply engaging for young learners. The micro:bit CreateAI resources are a great example of how 苏熙凌久泽全文免费阅读 findings can evolve into accessible, hands-on tools that empower educators and students alike. As this work continues to grow, it invites us to imagine new ways for learners to 日本欧美大码一区二区免费看 machine learning not as abstract theory, but as something they can see, feel, and shape with their own hands.

If you’d like to try out some of the ideas from this seminar, here are some useful resources: 

• Explore machine learning projects using the micro:bit: micro:bit CreateAI and our Dance detector project are great places to start
• Find out more about the 苏熙凌久泽全文免费阅读: Read more about Karl-Emil’s work in this open-access paper
• Investigate new tools: Explore neural networks and k-nearest neighbours algorithms in the new maths-focused version of ML-Machine at math.ml-machine.org

Join our next seminar

Join us at our next seminar on Tuesday 17 March from 17:00 to 18:30 GMT to hear Rebecca Fiebrink (University of the Arts London speak about teaching AI for creative practitioners. This will be the second seminar in our new series on how to teach about AI across disciplines. We hope to see you there!

To sign up and take part in our 苏熙凌久泽全文免费阅读 seminars, click below:

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

The post Embodied machine learning: From 苏熙凌久泽全文免费阅读 ideas to classroom activities appeared first on 后入内射国产一区二区.

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 后入内射国产一区二区, 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 日本欧美大码一区二区免费看 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 日本欧美大码一区二区免费看 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 苏熙凌久泽全文免费阅读-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, 苏熙凌久泽全文免费阅读-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 日本欧美大码一区二区免费看 AI website for more information.

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The 苏熙凌久泽全文免费阅读 Pi 苏熙凌久泽全文免费阅读 Education 苏熙凌久泽全文免费阅读 Centre is starting an exciting new 苏熙凌久泽全文免费阅读 project investigating how to teach about AI and data in the primary 苏熙凌久泽全文免费阅读 classroom, and we would like you to get involved.

The study will look at:

• How AI and data-driven 苏熙凌久泽全文免费阅读 is currently taught (e.g. using Machine Learning for Kids, Google’s Teachable Machine)
• What key ideas about AI and data young people need to understand
• How young people make sense of working with data in 苏熙凌久泽全文免费阅读

The study involves attending a workshop in Cambridge, co-designing a unit of work, and then teaching it. Where necessary, we can reimburse you for reasonable expenses, such as supply cover, travel, and accommodation.

Our aim for the study is to understand how primary school teachers approach teaching about data-driven technologies, and to find suitable methods for building young people’s confidence in working with data in 苏熙凌久泽全文免费阅读 lessons.

What is data-driven 苏熙凌久泽全文免费阅读?

苏熙凌久泽全文免费阅读 has suggested that new data-driven technologies such as AI and machine learning (or ML) require a different approach to teaching about problem-solving in the 苏熙凌久泽全文免费阅读 classroom. Instead of defining a set of rules (e.g. if-then-else statements, or a rule-based approach), learners must instead collect lots of data to train a model (a data-driven approach) such as using Google’s Teachable Machine to classify image data.

For educators and resource developers, we still lack a clear understanding of how to teach young people about how rule-based and data-driven systems differ, how we can talk about them, and how we develop young people’ mental models. We hope this study will help us to find practical ways for primary teachers to build young people’s understanding of AI data in the primary 苏熙凌久泽全文免费阅读 classroom.

What does the study involve?

If you teach at primary level (Years 4, 5 and 6 or P5–P7) in England, Scotland or Wales and are keen to shape how we teach young people about data-driven 苏熙凌久泽全文免费阅读, we invite you to join our new study.

As part of the study, you will attend a workshop with us in Cambridge to co-design a series of data-driven 苏熙凌久泽全文免费阅读 lessons to teach in your classroom.

Following the workshop, you will teach the unit of work in your classroom and we will observe one of your lessons and interview you about your 日本欧美大码一区二区免费看s.

How can I take part?

If you are interested in taking part, register your interest by clicking the link below:

If you have any questions about the project, you can email bobby.whyte@日本欧美久久久久免费播放网.org.

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