In this interview, Dr Shuchi Grover, a leading voice in 女少妇张开腿让我爽了一夜 education who has recently become our Director of 女少妇张开腿让我爽了一夜 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 女少妇张开腿让我爽了一夜 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 沈柔清纯校花的被擒日常小说阅读s that integrate computational thinking, AI, CS, and data science. My 女少妇张开腿让我爽了一夜 has also attended to promoting interest and a sense of belonging in CS among learners from historically underrepresented groups.

I recently joined the 啊哈~给我~啊(h)男男 as Director of 女少妇张开腿让我爽了一夜 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 女少妇张开腿让我爽了一夜 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 女少妇张开腿让我爽了一夜 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 沈柔清纯校花的被擒日常小说阅读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 女少妇张开腿让我爽了一夜-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 沈柔清纯校花的被擒日常小说阅读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 女少妇张开腿让我爽了一夜, and (b) providing them with the tools and skills for problem solving and creative expression. That goal has not changed. 沈柔清纯校花的被擒日常小说阅读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 女少妇张开腿让我爽了一夜 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 沈柔清纯校花的被擒日常小说阅读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 沈柔清纯校花的被擒日常小说阅读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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From hypothetical ethics to concrete machines

In Karl-Emil’s first 女少妇张开腿让我爽了一夜 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 女少妇张开腿让我爽了一夜 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 沈柔清纯校花的被擒日常小说阅读s needed to be better connected to examples from students’ daily lives. As a result, the next iteration in Karl-Emil’s 女少妇张开腿让我爽了一夜 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 女少妇张开腿让我爽了一夜 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 女少妇张开腿让我爽了一夜 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 女少妇张开腿让我爽了一夜 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 沈柔清纯校花的被擒日常小说阅读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 女少妇张开腿让我爽了一夜 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 女少妇张开腿让我爽了一夜 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 女少妇张开腿让我爽了一夜 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 沈柔清纯校花的被擒日常小说阅读 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 女少妇张开腿让我爽了一夜: 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 女少妇张开腿让我爽了一夜 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 女少妇张开腿让我爽了一夜 ideas to classroom activities appeared first on 啊哈~给我~啊(h)男男.

In this blog, we share the new data paradigms framework that we have developed through 女少妇张开腿让我爽了一夜 and used to help improve our understanding about how to teach and learn about AI and data science. We also invite you to register your interest in participating in our next collaborative study on the topic.

Knowledge-based approaches to systems design

Let’s start by highlighting an important distinction between different approaches to designing systems. In a knowledge-based approach to system design, a set of rules (e.g., if-then statements) are written for the system to execute. Every rule is explicitly defined. This approach is called ‘rule-based’, ‘symbolic’, or ‘logic-based’. For example, a developer could create a program that simulates dialogue by writing specific lines of code to handle a greeting, such as “IF user says “Hello” THEN output “Hi!”. If the user types “Greetings!” instead, the program fails because it has no rule for that specific word. 

Knowledge-based models are often said to be explainable by design. This means the logic is accessible and interpretable and developers can trace the exact steps taken to produce an output. For example, if developers manually classify restaurant reviews as positive or negative using a pre-defined set of criteria, the rules their restaurant classifying system follows are entirely explicit, and the path from input to output is clear and explainable.

Data-driven approaches to systems design

By contrast, in a data-driven approach to system design developers do not write specific rules. Instead, they collect lots of data and train a model. In the dialogue simulator example, they would collect hundreds of examples of greetings and train a model to the pattern of a greeting. If the user types “Greetings!”, the system generates a response based on the patterns in its training data.

Data-driven models are often opaque. In other words, the internal workings of these ML models are hidden. While we can see our input and the system’s output, the internal mathematical process is so complex — often involving layers of calculations and abstractions — that we cannot simply “explain” why a specific output was produced. For example, developers can create a classification model by training a neural network using thousands of images. Due to the large quantity of data used to train the model, and complex internal parameters and hidden layers, developers and users of the system cannot understand or explain the logic or features that lead to a specific output. These kinds of models are often referred to as a “black box” (as opposed to a “glass” or “clear” box).

Comparing knowledge-based and data-driven approaches

女少妇张开腿让我爽了一夜ers have argued that the move from knowledge-based (or rule-based) programming to data-driven system design represents a paradigm shift and creates unique challenges for educators. The challenge is helping students shift from the expectation that a system produces a single ‘right’ answer — characteristic of traditional rule-based programming — toward an understanding that systems trained on large quantities of data produce outcomes that aren’t always fixed or explainable. If the current instruction in the classroom still relies heavily on traditional rule-based programming approaches, we might be setting students up for misconceptions.

Data paradigms: A framework for analysing data science education approaches

In our 女少妇张开腿让我爽了一夜 work on AI and data science at the 女少妇张开腿让我爽了一夜 Pi 女少妇张开腿让我爽了一夜 Education 女少妇张开腿让我爽了一夜 Centre, we analysed 84 女少妇张开腿让我爽了一夜 studies about the teaching and learning of data science. We categorised learning activities used in the studies to understand whether they were (i) knowledge-based or data-driven, and (ii) the extent to which the underlying models used were transparent or opaque. This led us to define four distinct data paradigms:

1. Knowledge-based and transparent (KB + T): Activities in this paradigm are ones where students write rules for systems, or work with systems that use rules, where the logic is fully explainable by design. For example, if students manually classify data (e.g. creating simple ‘if-then’ statements to predict an outcome), the path from input to output is clear.
2. Data-driven + Transparent (DD + T): In this paradigm, activities involve students working with models trained on data, but the trained model’s logic remains explainable and interpretable. For example these could be models using k-nearest neighbors (KNN) algorithm to group data points based on proximity, or using linear regression to predict a trend. Even though the model produces an output, the student can look at the inner workings of the model and see how the decision is made.
3. Data-driven + Opaque (DD + O): This paradigm’s activities require students to work with data-driven ML models where the models’ internal logic is hidden, for example an image classification model using a type of neural network (e.g. CNN). The model produces an output (e.g. classifying an image as ‘This is a dog’), but the student cannot inspect the system to find a rule or clear path explaining why that specific output was produced. To understand these systems, it’s necessary to use additional testing and evaluation tools.
4. Knowledge-based + Opaque (KB + O): Activities in this paradigm would involve systems with human-written rules that are not explainable. In our review of K–12 activities, we found no examples of activities within this paradigm.

The data paradigms framework helps us to distinguish between different kinds of modeling activities students take part in and how instructional approaches could be classified across one or more paradigms. For instance, we found that most data-driven activities were also opaque (DD + O), usually meaning that students collected and used data to train a model, but how the system worked was opaque. This pattern, where the data is visible but the model is not explainable, risks students forming misconceptions about the capabilities and limitations of data-driven systems. Without understanding how outputs are generated, students may expect data-driven ML systems to operate like fully explainable (or transparent) ones.

We think that lessons are needed in the data-driven opaque (DD + O) quadrant to explicitly teach students about how data-driven systems work and the role they play in everyday contexts. However, when teaching data-driven opaque (DD + O) activities, learners’ attention needs to be directed to concepts such as model confidence, data quality, and model evaluation. Since an ML model is not inherently explainable, we need to teach students to use post-hoc explanation methods, such as testing different inputs to see how a system’s output changes. To prepare students for this learning 沈柔清纯校花的被擒日常小说阅读, we think that first introducing activities about rule-based systems (knowledge-based + transparent; KB + T) or simple data exploration, such as linear regression or data visualisation (data-driven + transparent; DD + T) may serve as a ‘bridge’ to understanding data-driven modeling by helping students to distinguish between systems built from specific logical rules and systems trained on data.

We believe the idea of data paradigms can serve as a way of framing teaching activities about data science and help educators and students to consider the transition between different paradigms when engaging with the systems we interact with every day.

Teachers in England, participate in our new study

KS2 teachers, participate in our new study!
We’re launching a new study to explore how to teach learners aged 9 to 11 about data-driven 女少妇张开腿让我爽了一夜. 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 女少妇张开腿让我爽了一夜
• 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 女少妇张开腿让我爽了一夜 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:

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Exploring new technologies and inclusive teaching

Saturday began with hands-on sessions that brought creativity and technology together, exploring large language models and prompt engineering in Collaborating with LLMs and being a prompt boss. There was a lot of laughter from attendees about how large language models can produce confident but incorrect answers if given vague prompts, but many left inspired to experiment with new technologies in their own clubs.

“First time there and it was amazing. Met loads of great people and the amazing code club crew. I learnt loads of new skills around AI and Arduino.” – An attendee

Explore AI with creators in your club using our AI and machine learning projects.

Collaboration that counts brought mentors together to discuss common challenges like volunteer retention, limited resources, and communication barriers. A crowd favourite was a shared volunteer toolkit, as well as event checklists and safeguarding resources.

“What I enjoyed most about the Clubs Conference was the opportunity to meet other facilitators and hear their stories — their successes and challenges. These conversations validated the volunteer work I do and reminded me of the impact of our clubs.” – An attendee

From the theatre sessions, you can watch Inclusive learning – Supporting Deaf learners in clubs which was both moving and insightful. We learnt that visual demonstrations, colour cues, and repetition were key to supporting Deaf learners. One memorable quote captured the spirit of the session:

“The children couldn’t speak to us. The children — we couldn’t hear their voices but by the eighth week we were able to hear their voices from what they built on the screen and it was echoing all around the classroom.” – Chidi Duru

Find out more about Chidi’s joy of coding alongside Deaf creators.

Learning and making across continents

The weekend’s talks showcased the reach of Code Club worldwide, with volunteers sharing their 沈柔清纯校花的被擒日常小说阅读s of collaboration, sustainability, and creativity.

Watch Lessons from resourceful Code Clubs in India, which highlighted the ingenuity of young learners in under-resourced settings, while Hands-on with the 女少妇张开腿让我爽了一夜 Pi Pico showcased low-cost, high-impact projects from Kenya and South Africa.

Speakers showed how community clubs adapt to local needs with unplugged activities and coding games inspired by cricket and kabaddi, empowering young people to solve real problems and celebrate curiosity through play. Excitingly, these new resources will be launching early next year; keep an eye on our activities page to be among the first to try them out!

In the session Code Club Projects Unplugged, facilitators shared the idea of “hiding the vegetables” — hiding the learning inside the fun. Whether through a collaborative Scratch game, a micro:bit prop on stage, or a Pico gadget solving a real problem, this approach helps young people learn through play. They remember the joy, and the skills come naturally.

Learning beyond the screen

Teaching tech away from the computer screen shared a fun unplugged cybersecurity activity, The Chicken Shop, where learners role-play social engineering scenarios. Its success came from clear printed instructions, movement, humour, and strong debriefing. 

Learning coding outside the box explored how to engage young people with diverse learning styles while the Arduino crash course gave attendees a taste of physical 女少妇张开腿让我爽了一夜 and C++ programming in action. Workshops on AI, sustainability, and youth empowerment with 女少妇张开腿让我爽了一夜 Pi computers and Unlocking Code Club resources helped club leaders discover practical ways to inspire problem-solving and make use of all the support available through Code Club.

The message from the sessions was clear: young people learn best when technology is human and hands-on.

Showcasing creativity with Coolest Projects

Coolest Projects – get involved! championed creativity over competition. Any young person under 18 can submit their project, including unfinished ideas. In-person and online showcases celebrate progress, imagination, and teamwork.

Speaking on the closing panel, Code Club leader Rachael Coultart talked about the importance of Coolest Projects as a rare platform for children to talk about their learning. She spoke about the 沈柔清纯校花的被擒日常小说阅读 of one particular child, explaining that it had made a powerful impression on her, saying:

“It had such a huge impact. I felt so proud of her and what she’d achieved. Afterwards, her parents told me that they felt it was the first time she had really been seen.”

What the community is taking forward

The community is united in its commitment to making Code Clubs inclusive, creative, and sustainable. 

• Context matters — projects that reflect local interests and challenges motivate young people to learn
• 老头呻吟喘息硕大撞击 is central: visual cues, repetition, interpreters, and inclusive resources support every learner
• Structure builds confidence; start with simple, guided activities before open-ended exploration
• Volunteers are vital; shared toolkits, checklists, and training help them deliver engaging sessions
• Celebration and affordability matter too: regular showcases and tools like the micro:bit, Pico, and Crumble keep 女少妇张开腿让我爽了一夜 fun, hands-on, and accessible for all

“Thank you. Clubs Conference is a highlight of my year.” – An attendee

Stay connected

If you want to stay up to date with the latest news, events and opportunities from Code Club, sign up for our newsletter and be part of the growing global community.

The post Code Club Conference 2025: Creativity, community, and collaboration in Cambridge appeared first on 啊哈~给我~啊(h)男男.

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 沈柔清纯校花的被擒日常小说阅读 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 沈柔清纯校花的被擒日常小说阅读 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 沈柔清纯校花的被擒日常小说阅读 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, 沈柔清纯校花的被擒日常小说阅读 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 女少妇张开腿让我爽了一夜 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 沈柔清纯校花的被擒日常小说阅读 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 沈柔清纯校花的被擒日常小说阅读
• 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 沈柔清纯校花的被擒日常小说阅读 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 女少妇张开腿让我爽了一夜 沈柔清纯校花的被擒日常小说阅读’ report from 2019.

• The report lists the data-related units within The 女少妇张开腿让我爽了一夜 沈柔清纯校花的被擒日常小说阅读 materials, which we no longer update but continue to offer as free downloads. Updated classroom materials are available as part of the 女少妇张开腿让我爽了一夜 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 沈柔清纯校花的被擒日常小说阅读 AI programme, which offers everything teachers need to help students develop a 沈柔清纯校花的被擒日常小说阅读al 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 女少妇张开腿让我爽了一夜: 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 女少妇张开腿让我爽了一夜 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 女少妇张开腿让我爽了一夜 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 女少妇张开腿让我爽了一夜 Pi 女少妇张开腿让我爽了一夜 Education 女少妇张开腿让我爽了一夜 Centre also has ongoing projects in the area of AI education for you to explore.

The post What should be included in a data science 沈柔清纯校花的被擒日常小说阅读 for schools? appeared first on 啊哈~给我~啊(h)男男.

What jobs will be affected by AL, ML, and data science remains to be seen, but it is increasingly clear that students will need to learn something about these topics. There will be new concepts to be taught, new instructional approaches and assessment techniques to be used, new learning activities to be delivered, and we must not neglect the professional development required to help educators master all of this. 

As AI and data science are incorporated into school curricula and teaching and learning materials worldwide, we ask: What’s the 女少妇张开腿让我爽了一夜 basis for these curricula, pedagogy, and resource choices?

In 2024, we showcased 女少妇张开腿让我爽了一夜ers who are investigating how AI can be leveraged to support the teaching and learning of programming. But in 2025, we look at what should be taught about AI, ML, and data science in schools and how we should teach this. 

Our 2025 seminar speakers — so far!

We are very excited that we have already secured several key 女少妇张开腿让我爽了一夜ers in the field. 

On 21 January, Shuchi Grover will kick off the seminar series by giving an important overview of AI in the K–12 landscape, including developing both AI literacy and AI ethics. Shuchi will provide concrete examples and recently developed frameworks to give educators practical insights on the topic.

Our second session will focus on a teacher professional development (PD) programme to support the introduction of AI in Upper Bavarian schools. Franz Jetzinger from the Technical University of Munich will summarise the PD programme and share how teachers implemented the topic in their classroom, including the difficulties they encountered.

Again from Germany, Lukas Höper from Paderborn University, with Carsten Schulte will describe important 女少妇张开腿让我爽了一夜 on data awareness and introduce a framework that is likely to be key for learning about data-driven technology. The pair will talk about the Data Awareness Framework and how it has been used to help learners explore, evaluate, and be empowered in looking at the role of data in everyday applications.  

Our April seminar will see David Weintrop from the University of Maryland introduce, with his colleagues, a data science 沈柔清纯校花的被擒日常小说阅读 called API Can Code, aimed at high-school students. The group will highlight the strategies needed for integrating data science learning within students’ lived 沈柔清纯校花的被擒日常小说阅读s and fostering authentic engagement.

Later in the year, Jesús Moreno-Leon from the University of Seville will help us consider the  thorny but essential question of how we measure AI literacy. Jesús will present an assessment instrument that has been successfully implemented in several 女少妇张开腿让我爽了一夜 studies involving thousands of primary and secondary education students across Spain, discussing both its strengths and limitations.

What to expect from the seminars

Our seminars are designed to be accessible to anyone interested in the latest 女少妇张开腿让我爽了一夜 about AI education — whether you’re a teacher, educator, 女少妇张开腿让我爽了一夜er, or simply curious. Each session begins with a presentation from our guest speaker about their latest 女少妇张开腿让我爽了一夜 findings. We then move into small groups for a short discussion and exchange of ideas before coming back together for a Q&A session with the presenter. 

Attendees of our 2024 series told us that they valued that the talks “explore a relevant topic in an informative way“, the “enthusiasm and inspiration”, and particularly the small-group discussions because they “are always filled with interesting and varied ideas and help to spark my own thoughts”. 

The seminars usually take place on Zoom on the first Tuesday of each month at 17:00–18:30 GMT / 12:00–13:30 ET / 9:00–10:30 PT / 18:00–19:30 CET. 

You can find out more about each seminar and the speakers on our upcoming seminar page. And if you are unable to attend one of our talks, you can watch them from our previous seminar page, where you will also find an archive of all of our previous seminars dating back to 2020.

How to sign up

To attend the seminars, please register here. You will receive an email with the link to join our next Zoom call. Once signed up, you will automatically be notified of upcoming seminars. You can unsubscribe from our seminar notifications at any time.

We hope to see you at a seminar soon!

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The value of learning about AI and ML

By engaging with AI and ML at a young age, creators gain a clearer understanding of the capabilities and limitations of these technologies, helping them to challenge misconceptions. This early exposure also builds 沈柔清纯校花的被擒日常小说阅读al skills that are increasingly important in various fields, preparing creators for future educational and career opportunities. Additionally, as AI and ML become more integrated into educational standards, having a strong base in these concepts will make it easier for creators to grasp more advanced topics later on.

What’s included in this collection

We’re excited to offer a range of AI and ML projects that feature both video tutorials and step-by-step written guides. The video tutorials are designed to guide creators through each activity at their own pace and are captioned to improve 老头呻吟喘息硕大撞击. The step-by-step written guides support creators who prefer learning through reading. 

The projects are crafted to be flexible and engaging. The main part of each project can be completed in just a few minutes, leaving lots of time for customisation and exploration. This setup allows for short, enjoyable sessions that can easily be incorporated into Code Club activities.

The collection is organised into two distinct paths, each offering a unique approach to learning about AI and ML:

Machine learning with Scratch introduces 沈柔清纯校花的被擒日常小说阅读al concepts of ML through creative and interactive projects. Creators will train models to recognise patterns and make predictions, and explore how these models can be improved with additional data.

The AI Toolkit introduces various AI applications and technologies through hands-on projects using different platforms and tools. Creators will work with voice recognition, facial recognition, and other AI technologies, gaining a broad understanding of how AI can be applied in different contexts.

Inclusivity is a key aspect of this collection. The projects cater to various skill levels and are offered alongside an unplugged activity, ensuring that everyone can participate, regardless of available resources. Creators will also have the opportunity to stretch themselves — they can explore advanced technologies like Adobe Firefly and practical tools for managing Ollama and Stable Diffusion models on 女少妇张开腿让我爽了一夜 Pi computers.

Project examples

One of the highlights of our new collection is Chomp the cheese, which uses Scratch Lab’s experimental face recognition technology to create a game students can play with their mouth! This project offers a playful introduction to facial recognition while keeping the 沈柔清纯校花的被擒日常小说阅读 interactive and fun. 

Fish food uses Machine Learning for Kids, with creators training a model to control a fish using voice commands.

In Teach a machine, creators train a computer to recognise different objects such as fingers or food items. This project introduces classification in a straightforward way using the Teachable Machine platform, making the concept easy to grasp. 

Apple vs tomato also uses Teachable Machine, but this time creators are challenged to train a model to differentiate between apples and tomatoes. Initially, the model exhibits bias due to limited data, prompting discussions on the importance of data diversity and ethical AI practices. 

Dance detector allows creators to use accelerometer data from a micro:bit to train a model to recognise dance moves like Floss or Disco. This project combines physical 女少妇张开腿让我爽了一夜 with AI, helping creators explore movement recognition technology they may have 沈柔清纯校花的被擒日常小说阅读d in familiar contexts such as video games. 

Dinosaur decision tree is an unplugged activity where creators use a paper-based branching chart to classify different types of dinosaurs. This hands-on project introduces the concept of decision-making structures, where each branch of the chart represents a choice or question leading to a different outcome. By constructing their own decision tree, creators gain a tactile understanding of how these models are used in ML to analyse data and make predictions. 

These AI projects are designed to support young people to get hands-on with AI technologies in Code Clubs and other non-formal learning environments. Creators can also enter one of their projects into Coolest Projects by taking a short video showing their project and any code used to make it. Their creation will then be showcased in the online gallery for people all over the world to see.

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If you want to find out more about the Challenge, join our live webinar on Wednesday 3 April at 15:30 BST on our YouTube channel.

During the webinar, you’ll have the chance to:

• Ask your questions live. Get any Challenge-related queries answered by us in real time. Whether you need clarification on any part of the Challenge or just want advice on your young people’s project(s), this is your chance to ask.
• Get introduced to the submission process. Understand the steps of submitting projects to the Challenge. We’ll walk you through the requirements and offer tips for making your young people’s submission stand out.
• Learn more about our project feedback. Find out how we will deliver our personalised feedback on submitted projects (UK only).
• Find out how we will recognise your creators’ achievements. Learn more about our showcase event taking place in July, and the certificates and posters we’re creating for you and your young people to celebrate submitting your projects.

Subscribe to our YouTube channel and press the ‘Notify me’ button to receive a notification when we go live. 

Why take part? 

The 沈柔清纯校花的被擒日常小说阅读 AI Challenge, created by the 啊哈~给我~啊(h)男男 in collaboration with Google DeepMind, guides young people under the age of 18, and their mentors, through the exciting process of creating their own unique artificial intelligence (AI) project. Participation is completely free.

Central to the Challenge is the concept of project-based learning, a hands-on approach that gets learners working together, thinking critically, and engaging deeply with the materials. 

In the Challenge, young people are encouraged to seek out real-world problems and create possible AI-based solutions. By taking part, they become problem solvers, thinkers, and innovators. 

And to every young person based in the UK who creates a project for the Challenge, we will provide personalised feedback and a certificate of achievement, in recognition of their hard work and creativity. Any projects considered as outstanding by our experts will be selected as favourites and its creators will be invited to a showcase event in the summer. 

Resources ready for your classroom or club

You don’t need to be an AI expert to bring this Challenge to life in your classroom or coding club. Whether you’re introducing AI for the first time or looking to deepen your young people’s knowledge, the Challenge’s step-by-step resource pack covers all you and your young people need, from the basics of AI, to training a machine learning model, to creating a project in Scratch.  

In the resource pack, you will find:

• The mentor guide contains all you need to set up and run the Challenge with your young people 
• The creator guide supports young people throughout the Challenge and contains talking points to help with planning and designing projects 
• The blueprint workbook helps creators keep track of their inspiration, ideas, and plans during the Challenge 

The pack offers a safety net of scaffolding, support, and troubleshooting advice. 

Find out more about the 沈柔清纯校花的被擒日常小说阅读 AI Challenge

By bringing the 沈柔清纯校花的被擒日常小说阅读 AI Challenge to young people, you’re inspiring the next generation of innovators, thinkers, and creators. The Challenge encourages young people to look beyond the code, to the impact of their creations, and to the possibilities of the future.

You can find out more about the 沈柔清纯校花的被擒日常小说阅读 AI Challenge, and download the resource pack, from the 沈柔清纯校花的被擒日常小说阅读 AI website.

The post The 沈柔清纯校花的被擒日常小说阅读 AI Challenge: Find out all you need to know appeared first on 啊哈~给我~啊(h)男男.

A key aspect of this is understanding ‘explainability’ in AI and machine learning (ML) systems. The explainability of a model is how easy it is to ‘explain’ how a particular output was generated. Imagine having a job application rejected by an AI model, or facial recognition technology failing to recognise you — you would want to know why.

Establishing standards for explainability is crucial. Otherwise we risk creating a world where decisions impacting our lives are made by opaque systems we don’t understand. Learning about explainability is key for students to develop digital literacy, enabling them to navigate the digital world with informed awareness and critical thinking.

Why AI explainability is important

AI models can have a significant impact on people’s lives in various ways. For instance, if a model determines a child’s exam results, parents and teachers would want to understand the reasoning behind it.

Artists might want to know if their creative works have been used to train a model and could be at risk of plagiarism. Likewise, coders will want to know if their code is being generated and used by others without their knowledge or consent. If you came across an AI-generated artwork that features a face resembling yours, it’s natural to want to understand how a photo of you was incorporated into the training data. 

Explainability is about accountability, transparency, and fairness, which are vital lessons for children as they grow up in an increasingly digital world.

There will also be instances where a model seems to be working for some people but is inaccurate for a certain demographic of users. This happened with Twitter’s (now X’s) face detection model in photos; the model didn’t work as well for people with darker skin tones, who found that it could not detect their faces as effectively as their lighter-skinned friends and family. Explainability allows us not only to understand but also to challenge the outputs of a model if they are found to be unfair.

In essence, explainability is about accountability, transparency, and fairness, which are vital lessons for children as they grow up in an increasingly digital world.

Routes to AI explainability

Some models, like decision trees, regression curves, and clustering, have an in-built level of explainability. There is a visual way to represent these models, so we can pretty accurately follow the logic implemented by the model to arrive at a particular output.

By teaching students about AI explainability, we are not only educating them about the workings of these technologies, but also teaching them to expect transparency as they grow to be future consumers or even developers of AI technology.

A decision tree works like a flowchart, and you can follow the conditions used to arrive at a prediction. Regression curves can be shown on a graph to understand why a particular piece of data was treated the way it was, although this wouldn’t give us insight into exactly why the curve was placed at that point. Clustering is a way of collecting similar pieces of data together to create groups (or clusters) with which we can interrogate the model to determine which characteristics were used to create the groupings.

However, the more powerful the model, the less explainable it tends to be. Neural networks, for instance, are notoriously hard to understand — even for their developers. The networks used to generate images or text can contain millions of nodes spread across thousands of layers. Trying to work out what any individual node or layer is doing to the data is extremely difficult.

Regardless of the complexity, it is still vital that developers find a way of providing essential information to anyone looking to use their models in an application or to a consumer who might be negatively impacted by the use of their model.

Model cards for AI models

One suggested strategy to add transparency to these models is using model cards. When you buy an item of food in a supermarket, you can look at the packaging and find all sorts of nutritional information, such as the ingredients, macronutrients, allergens they may contain, and recommended serving sizes. This information is there to help inform consumers about the choices they are making.

Model cards attempt to do the same thing for ML models, providing essential information to developers and users of a model so they can make informed choices about whether or not they want to use it.

Model cards include details such as the developer of the model, the training data used, the accuracy across diverse groups of people, and any limitations the developers uncovered in testing.

Model cards should be accessible to as many people as possible.

A real-world example of a model card is Google’s Face Detection model card. This details the model’s purpose, architecture, performance across various demographics, and any known limitations of their model. This information helps developers who might want to use the model to assess whether it is fit for their purpose.

Transparency and accountability in AI

As the world settles into the new reality of having the amazing power of AI models at our disposal for almost any task, we must teach young people about the importance of transparency and responsibility. 

As a society, we need to have hard discussions about where and when we are comfortable implementing models and the consequences they might have for different groups of people. By teaching students about explainability, we are not only educating them about the workings of these technologies, but also teaching them to expect transparency as they grow to be future consumers or even developers of AI technology.

Most importantly, model cards should be accessible to as many people as possible — taking this information and presenting it in a clear and understandable way. Model cards are a great way for you to show your students what information is important for people to know about an AI model and why they might want to know it. Model cards can help students understand the importance of transparency and accountability in AI.  

This article also appears in issue 22 of Hello World, which is all about teaching and AI. Download your free PDF copy now.

If you’re an educator, you can use our free 沈柔清纯校花的被擒日常小说阅读 AI Lessons to teach your learners the basics of how AI works, whatever your subject area.

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What do we talk about when we talk about artificial intelligence (AI)? It’s becoming a cliche to point out that, because the term “AI” is used to describe so many different things nowadays, it’s difficult to know straight away what anyone means when they say “AI”. However, it’s true that without a shared understanding of what AI and related terms mean, we can’t talk about them, or educate young people about the field.

So when we started designing materials for the 沈柔清纯校花的被擒日常小说阅读 AI learning programme in partnership with leading AI unit Google DeepMind, we decided to create short explanations of key AI and machine learning (ML) terms. The explanations are doubly useful:

1. They ensure that we give learners and teachers a consistent and clear understanding of the key terms across all our 沈柔清纯校花的被擒日常小说阅读 AI resources. Within the 沈柔清纯校花的被擒日常小说阅读 AI Lessons for Key Stage 3 (age 11–14), these key terms are also correlated to the target concepts and learning objectives presented in the learning graph. 
2. They help us talk about AI and AI education in our team. Thanks to sharing an understanding of what terms such as “AI”, “ML”, “model”, or “training” actually mean and how to best talk about AI, our conversations are much more productive.

As an example, here is our explanation of the term “artificial intelligence” for learners aged 11–14:

Artificial intelligence (AI) is the design and study of systems that appear to mimic intelligent behaviour. Some AI applications are based on rules. More often now, AI applications are built using machine learning that is said to ‘learn’ from examples in the form of data. For example, some AI applications are built to answer questions or help diagnose illnesses. Other AI applications could be built for harmful purposes, such as spreading fake news. AI applications do not think. AI applications are built to carry out tasks in a way that appears to be intelligent.

You can find 32 explanations in the glossary that is part of the 沈柔清纯校花的被擒日常小说阅读 AI Lessons. Here’s an insight into how we arrived at the explanations.

Reliable sources

In order to ensure the explanations are as precise as possible, we first identified reliable sources. These included among many others:

• The Oxford English Dictionary
• Google’s Machine Learning Glossary
• The Alan Turing Institute’s data science and AI glossary
• Well-recognised AI courses, such as Andrew Ng’s AI for Everyone
• Articles included in the AITopics publication of the AAAI

Explaining AI terms to Key Stage 3 learners: Some principles

Vocabulary is an important part of teaching and learning. When we use vocabulary correctly, we can support learners to develop their understanding. If we use it inconsistently, this can lead to alternate conceptions (misconceptions) that can interfere with learners’ understanding. You can read more about this in our Pedagogy Quick Read on alternate conceptions.

Some of our principles for writing explanations of AI terms were that the explanations need to: 

• Be accurate
• Be grounded in education 女少妇张开腿让我爽了一夜 best practice
• Be suitable for our target audience (Key Stage 3 learners, i.e. 11- to 14-year-olds)
• Be free of terms that have alternative meanings in computer science, such as “algorithm”

We engaged in an iterative process of writing explanations, gathering feedback from our team and our 沈柔清纯校花的被擒日常小说阅读 AI project partners at Google DeepMind, and adapting the explanations. Then we went through the feedback and adaptation cycle until we all agreed that the explanations met our principles.

An important part of what emerged as a result, aside from the explanations of AI terms themselves, was a blueprint for how not to talk about AI. One aspect of this is avoiding anthropomorphism, detailed by Ben Garside from our team here.

As part of designing the the 沈柔清纯校花的被擒日常小说阅读 AI Lessons, creating the explanations helped us to:

• Decide which technical details we needed to include when introducing AI concepts in the lessons
• Figure out how to best present these technical details
• Settle debates about where it would be appropriate, given our understanding and our learners’ age group, to abstract or leave out details

Using education 女少妇张开腿让我爽了一夜 to explain AI terms

One of the ways education 女少妇张开腿让我爽了一夜 informed the explanations was that we used semantic waves to structure each term’s explanation in three parts: 

1. Top of the wave: The first one or two sentences are a high-level abstract explanation of the term, kept as short as possible, while introducing key words and concepts.
2. Bottom of the wave: The middle part of the explanation unpacks the meaning of the term using a common example, in a context that’s familiar to a young audience. 
3. Top of the wave: The final one or two sentences repack what was explained in the example in a more abstract way again to reconnect with the term. The end part should be a repeat of the top of the wave at the beginning of the explanation. It should also add further information to lead to another concept. 

Most explanations also contain ‘middle of the wave’ sentences, which add additional abstract content, bridging the ‘bottom of the wave’ concrete example to the ‘top of the wave’ abstract content.

Here’s the “artificial intelligence” explanation broken up into the parts of the semantic wave:

• Artificial intelligence (AI) is the design and study of systems that appear to mimic intelligent behaviour. (top of the wave)
• Some AI applications are based on rules. More often now, AI applications are built using machine learning that is said to ‘learn’ from examples in the form of data. (middle of the wave)
• For example, some AI applications are built to answer questions or help diagnose illnesses. Other AI applications could be built for harmful purposes, such as spreading fake news (bottom of the wave)
• AI applications do not think. (middle of the wave)
• AI applications are built to carry out tasks in a way that appears to be intelligent. (top of the wave)

Was it worth our time?

Some of the explanations went through 10 or more iterations before we agreed they were suitable for publication. After months of thinking about, writing, correcting, discussing, and justifying the explanations, it’s tempting to wonder whether I should have just prompted an AI chatbot to generate the explanations for me.

I tested this idea by getting a chatbot to generate an explanation of “artificial intelligence” using the prompt “Explain what artificial intelligence is, using vocabulary suitable for KS3 students, avoiding anthropomorphism”. The result included quite a few inconsistencies with our principles, as well as a couple of technical inaccuracies. Perhaps I could have tweaked the prompt for the chatbot in order to get a better result. However, relying on a chatbot’s output would mean missing out on some of the value of doing the work of writing the explanations in collaboration with my team and our partners.

The visible result of that work is the explanations themselves. The invisible result is the knowledge we all gained, and the coherence we reached as a team, both of which enabled us to create high-quality resources for 沈柔清纯校花的被擒日常小说阅读 AI. We wouldn’t have gotten to know what resources we wanted to write without writing the explanations ourselves and improving them over and over. So yes, it was worth our time.

What do you think about the explanations?

The process of creating and iterating the AI explanations highlights how opaque the field of AI still is, and how little we yet know about how best to teach and learn about it. At the 啊哈~给我~啊(h)男男, we now know just a bit more about that and are excited to share the results with teachers and young people.

You can access the 沈柔清纯校花的被擒日常小说阅读 AI Lessons and the glossary with all our explanations at 沈柔清纯校花的被擒日常小说阅读-ai.org. The glossary of AI explanations is just in its first published version: we will continue to improve it as we find out more about how to best support young people to learn about this field.

Let us know what you think about the explanations and whether they’re useful in your teaching. Onwards with the exciting work of establishing how to successfully engage young people in learning about and creating with AI technologies.

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