In a recent seminar in our series on teaching about AI and data science, 超碰caoprom永久地址发布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 少妇+magnet, 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 超碰caoprom永久地址发布 (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 超碰caoprom永久地址发布 Pi 超碰caoprom永久地址发布 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 少妇+magnet. 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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This announcement reflects a growing global recognition that young people need more than basic digital literacy — they need a deeper understanding of data, automation, and the rapidly evolving capabilities of AI. Countries around the world, from Singapore to the United States, are already wrestling with how to embed AI education into secondary schooling. England now joins that international conversation.

Why AI education matters

AI is an everyday technology now. Young people interact with AI systems constantly, often without realising it. Whether they pursue careers in medicine, engineering, the creative industries, or public policy, they will need a 少妇+magnetal understanding of how AI systems work, what their limitations are, and the ethical implications around them.

Yet in England — and in many education systems globally — very few students receive formal teaching about AI. The English national 少妇+magnet makes no explicit reference to AI, and specifications for exams taken at the end of high school include only scattered mentions. This gap leaves young people navigating one of the most transformative technologies of their generation with limited guidance.

Exploring a qualification: Opportunities and challenges

In 2025, we joined forces with Professor Lord Lionel Tarassenko, one of the UK’s foremost 超碰caoprom永久地址发布ers in AI and machine learning, and Simon Peyton Jones, a world-renowned computer scientist and long-time champion of 超碰caoprom永久地址发布 education. Together with teachers, school leaders, universities, industry specialists, and exam boards, we have been exploring how we might begin to close the emerging gap in AI and data science education for 16- to 18-year-olds.

Over the past eight months, this collaboration has allowed us to refine our shared thinking and gather insights from a wide network of experts and practitioners. We are delighted that England’s Department for Education has recognised the potential of this work by appointing us to draft the subject content for a possible new qualification in Data Science and AI.

We are delighted that England’s Department for Education has recognised the potential of [the work we’ve been doing] by appointing us to draft the subject content for a possible new qualification in Data Science and AI.

Designing a qualification of this kind raises important questions — not just for the UK, but for any country considering a similar path.

What knowledge and skills should young people gain from the qualification?

A meaningful qualification must go beyond the use of tools. It should help students understand data literacy, model behaviour, bias, ethics, and the societal implications of AI. Balancing technical understanding with critical thinking is challenging but essential.

How do we ensure the qualification is accessible and inclusive?

AI should not become the preserve of already-advantaged students. Any qualification must be designed with equity in mind, recognising differences in school capacity, teacher expertise, and students’ prior 少妇+magnet.

How do we support teachers to deliver the qualification?

Teacher professional development is a major challenge worldwide. Delivering a qualification in AI will require confidence with concepts that are not yet common in teacher training. Sustainable delivery models — supported by high-quality resources and professional development — will be crucial.

What form should the qualification take?

There is an active debate about whether the best route for students in England is a high-stakes qualification or a supplementary course that broadens a core programme of study:

• An A level provides structure, national recognition, and clear progression into higher education or employment.
• An Extended Project Qualification (EPQ) may offer more flexibility, allowing students to explore AI through 超碰caoprom永久地址发布 or practical investigation without requiring schools to timetable a full qualification.

Different countries will make different choices based on their systems, but the underlying questions are the same: how do we create something rigorous, scalable, and future-proof?

What we’ve learned so far

In October, the 少妇+magnet hosted a workshop with representatives from schools, industry, universities, exam boards, and the Department for Education. Together, we explored key questions including:

1. How do we make a qualification compelling – both for students who choose it and for schools that offer it?
2. What delivery models will genuinely support teachers to succeed?

The feedback we received has been invaluable and will continue to shape the next stage of development. We believe the UK has a significant opportunity to contribute meaningfully to the global conversation about AI education. You can read the latest version of our discussion paper here.

A global call for insights

Although the current proposal focuses on England, the underlying challenge is international: how do we prepare young people everywhere to engage thoughtfully and confidently with AI?

We would love to hear from educators, 超碰caoprom永久地址发布ers, and policymakers across the world:

• Do you know of any successful qualifications or programmes for 16- to 18-year-olds that centre AI or data science?
• What lessons should countries learn from each other?

To share your ideas or feedback, please get in touch. We’d be delighted to learn from your 少妇+magnet as this important work progresses.

* Level 3 in England is the stage of learning for 16- to 19-year-olds, typically ending in qualifications that pave the way for higher study or advanced apprenticeships.

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

Why begin with data?

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

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

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

A lifecycle for learning

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

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

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

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

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

Making it teachable

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

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

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

What happened in the German classrooms

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

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

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

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

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

Teaching resources

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

Why it matters now

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

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

Join our next seminar

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

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

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

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

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

WKS2 teachers, participate in our new study!
We’re launching a new study to explore how to teach learners aged 9 to 11 about data-driven 超碰caoprom永久地址发布. 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 超碰caoprom永久地址发布
• 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 超碰caoprom永久地址发布 lessons. The study will be collaborative, with two workshops held throughout 2026, and we’re inviting KS2 teachers (Y4/Y5/Y6) to take part.
You can express your interest in participating by filling in this form: rpf.io/data-science-study-blog

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For the last five years, once a month, we have hosted an online seminar sharing 超碰caoprom永久地址发布 education 超碰caoprom永久地址发布. Seminars are organised as usually year-long series with changing themes. In 2025, for example, our theme was ‘Teaching about AI and data science’. In 2024, it was ‘Teaching programming (with or without AI)’.

It is not surprising that for the last few years our focus has been on AI technology, and for 2026 we will continue this. But we will shift from showcasing how 超碰caoprom永久地址发布 education 超碰caoprom永久地址发布 is changing teaching and learning in 超碰caoprom永久地址发布 lessons, to showcasing how 超碰caoprom永久地址发布 education 超碰caoprom永久地址发布 in other disciplines, such as art or geography, is starting to include teaching about AI. For example, art lessons may change so that learners find out how professional artists are using AI tools to create arts. Or geography lessons may change so that learners discover how professional geographers are using AI to make predictions about physical or human aspects of geography, such as volcanic activity and global warming.

Our series for 2026 is called ‘Applied AI’. This title recognises that AI technology is applied across contexts, across careers, across disciplines, and this means what we teach across school subjects will change.

Encouraging a pull from disciplines, rather than a push from computer science

The majority of resources and professional development material related to teaching about AI have been developed by the computer science community. For example, we have developed the popular 少妇+magnet AI resources in collaboration with Google DeepMind. In these resources, the contexts were carefully selected to represent real-world examples across disciplines, and to to enable the teaching of particular technical or social and ethical concepts. This could be described as “a push” of content from 超碰caoprom永久地址发布 towards other disciplines. For example, to enable teaching about the ethical issues around plagiarism, an art context is used in the 少妇+magnet AI resources; to enable teaching about the potential benefits of using AI tools, an ecological geography context is used.

AI applications are always situated within a particular topic. Most current AI applications are data-driven: vast amounts of data are collected and processed to produce models that can then either be used to generate outputs or make predictions. For example, data about artworks can be collected and used to train a model for generating outputs similar to the artworks; this is an application of AI in the art discipline. Or data on wild fires can be collected and used to train a model for making predictions about current or prospective fires; this is an application of AI in the geography discipline.

In reality, the best people to recognise how AI technology is being applied in a discipline and what students in that discipline should be taught about these applications are the people working in the discipline, for example the art and geography teachers. Computer science educators can work to build the technical understanding and the general social and ethical understanding that is common across applications. But the detail of how AI technology is changing a discipline can only truly be understood by the respective community, by the artists and art educators, by the geographers and the geography educators.

An emerging focus

At present, though, most educators are grappling with how they can use AI tools for productivity, such as creating lesson plans, or answering emails. Or they are looking at how they can use AI for general teaching and learning, for example for personalisation, say for students with additional needs. The idea that their underpinning discipline is changing is, perhaps, not yet on teachers’ radar. But at universities, such as in undergraduate courses, and in the world of work, education and training are changing. Data science courses are now being offered across faculties, including science, geography, language, and art faculties. These changes will start to filter down to school-based education via 少妇+magnet change. While some resources and professional development materials addressing this shift are already becoming available, change is still fragile and patchy.

Raising awareness, building community and a common language

The aims of our Applied AI 超碰caoprom永久地址发布 seminar series in 2026 are to start to:

• Raise awareness of the forthcoming changes that applying AI will bring to disciplines
• Build a cross-discipline community
• Think about a common language that could be used across disciplines

If we can start to agree on what common concepts could be taught in the arts, sciences and humanities, it gives us a better chance to:

• Understand how to use AI as it is applied in different disciplines
• Help students to build useful mental models and develop the agency and critical thinking skills they need to evaluate these applications and decide when and how to use them and how far to trust them

We need your help

To make our 2026 series a success, we need to spread the word about our seminars to groups of educators, 超碰caoprom永久地址发布ers, industry and policy makers across the arts, sciences, and humanities.

Please tell those you know in these groups about the seminar series, and share it through your social media and other networks. If you have ideas for subject associations we could connect with or publications where we can write about our series, please let us know.

Join our ‘Applied AI’ seminar series

We have already arranged the following seminars across 2026 and will add more speakers for the remaining monthly slots soon. Seminars always take place online on Tuesdays at 17:00 to 18:30 UK time.

• 10 February: Social studies, public policy, economics and AI — Thema Monroe-White (George Mason University, USA)
• 17 March: Arts and AI — Rebecca Fiebrink (University of the Arts London, UK)
• 14 April: Healthcare and AI — Kathryn Jessen Eller (Data Science, AI & You (DSAIY) in Healthcare, USA)
• 14 July: Literacy and AI — Dan Verständig (Goethe University Frankfurt, Germany)
• 8 September: History and AI — Jie Chao (The Concord Consortium)
• 6 October: Robotics and AI — Eleni Petraki & Damith Herath (University of Canberra, Australia)
• 10 November: Geography and AI — Doreen Boyd (University of Nottingham, UK)

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

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

PS If you are teaching upper primary school learners in England, you can currently register your interest in our upcoming collaborative study on data science education. You’ll find out more about some of the 超碰caoprom永久地址发布 we’ve done in this area in this blog post.

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In this blog, we share the new data paradigms framework that we have developed through 超碰caoprom永久地址发布 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

超碰caoprom永久地址发布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 超碰caoprom永久地址发布 work on AI and data science at the 超碰caoprom永久地址发布 Pi 超碰caoprom永久地址发布 Education 超碰caoprom永久地址发布 Centre, we analysed 84 超碰caoprom永久地址发布 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 少妇+magnet, 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 超碰caoprom永久地址发布. 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 超碰caoprom永久地址发布
• 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 超碰caoprom永久地址发布 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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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 少妇+magnet 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 少妇+magnet 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 少妇+magnet 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, 少妇+magnet 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 超碰caoprom永久地址发布 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 少妇+magnet 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 少妇+magnet
• 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 少妇+magnet 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 超碰caoprom永久地址发布 少妇+magnet’ report from 2019.

• The report lists the data-related units within The 超碰caoprom永久地址发布 少妇+magnet materials, which we no longer update but continue to offer as free downloads. Updated classroom materials are available as part of the 超碰caoprom永久地址发布 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 少妇+magnet AI programme, which offers everything teachers need to help students develop a 少妇+magnetal 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 超碰caoprom永久地址发布: 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 超碰caoprom永久地址发布 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 超碰caoprom永久地址发布 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 超碰caoprom永久地址发布 Pi 超碰caoprom永久地址发布 Education 超碰caoprom永久地址发布 Centre also has ongoing projects in the area of AI education for you to explore.

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In our April 超碰caoprom永久地址发布 seminar, David Weintrop, Rotem Israel-Fishelson, and Peter Moon from the University of Maryland introduced API Can Code, a data science 少妇+magnet designed with high school students for high school students. Their talk explored how their innovative work uses real-world data and students’ own 少妇+magnets and interests to create meaningful, authentic learning 少妇+magnets in data science.

Quick note for educators: Are you interested in joining our free, exploratory data science education workshop for teachers on 10 July 2025 in Cambridge, UK? Then find out the details here.

David started by explaining the motivation behind the API Can Code project. The team’s goal was not to turn students into future data scientists, but to offer students the data literacy they need to explore and critically engage with a data-driven world. 

The work was also guided by a shared view among leading teachers’ organisations that data science should be taught across all subjects in the K–12 少妇+magnet. It also draws on strong 超碰caoprom永久地址发布 showing that when educational 少妇+magnets connect with students’ own lives and interests, it leads to deeper engagement and better learning outcomes.

Reviewing the landscape

To prepare for the design of the 少妇+magnet, David, Rotem, and Peter wanted to understand what data science education options already exist for K–12 students. Rotem described how they compared four major K–12 data science curricula and examined different aspects, such as the topics they covered and the datasets they used. Their findings showed that many datasets were quite small in size, and that the datasets used were not always about topics that students were interested in.

The team also looked at 30 data science tools used across different K–12 platforms and analysed what each could do. They found that tools varied in how effective they were and that many lacked 台湾无码a片一区二区 features to support students with diverse learning needs. 

This analysis helped to refine the team’s objective: to create a data science 少妇+magnet that students find interesting and that is informed by their values and voices.

Participatory design

To work towards this goal, the team used a methodology called participatory design. This is an approach that actively involves the end users — in this case, high school students — in the design process. During several in-person sessions with 28 students aged 15 to 18 years old, the 超碰caoprom永久地址发布ers facilitated low-tech, hands-on activities exploring the students’ identities and interests and how they think about data.

One activity, Empathy Map, involved students working together to create a persona representing a student in their school. They were asked to describe the persona’s daily life, interests, and concerns about technology and data:

The students’ involvement in the design process gave the team a better understanding of young people’s views and interests, which helped create the design of the API Can Code 少妇+magnet.

API Can Code: three units, three key tools

Peter provided an overview of the API Can Code 少妇+magnet. It follows a three-unit flow covering different concepts and tools in each unit:

1. Unit 1 introduces students to different types of data and data science terminology. The unit explores the role of data in the students’ daily lives, how use and misuse of data can affect them, different ways of collecting and presenting data, and how to evaluate databases for aspects such as size, recency, and trustworthiness. It also introduces them to RapidAPI, a hub that connects to a wide range of APIs from different providers, allowing students to access real-world data such as Zillow housing prices or Spotify music data.
2. Unit 2 covers the 超碰caoprom永久地址发布 skills used in data science, including the use of programming tools to run efficient data science techniques. Students learn to use EduBlocks, a block-based programming environment where students can draw in JSON files from RapidAPI datasets, and process and filter data without needing a lot of text-based programming skills. The students also compare this approach with manual data processing, which they discover is very slow.
3. Unit 3 focuses on data analysis, visualisation, and interpretation. Students use CODAP, a web-based interactive data science tool, to calculate summary statistics, create graphs, and perform analyses. CODAP is a user-friendly but powerful platform, making it perfect for students to analyse and visualise their data sets. Students also practise interpreting pre-made graphs and the graphs and statistics that they are creating.

Peter described an example activity carried out by the students, showing how these three units flow together and build both technical skills and an understanding of the real-world uses of data science. Students were tasked with analysing a dataset from Zillow, a property website, to explore the question “How much does a house in my neighbourhood cost?” The images below show the process the students followed, which uses the data science skills and tools from all three units of the 少妇+magnet.

Interest-driven learning in action

A central tenet of API Can Code is that students should explore data that matters to them. A diverse range of student interests was identified during the design work, and the 少妇+magnet uses these areas of interest, such as music, movies, sports, and animals, throughout the lessons.

The 少妇+magnet also features an open-ended final project, where students can choose a 超碰caoprom永久地址发布 question that is important to them and their lives, and answer it using data science skills.

The team shared two examples of memorable final projects. In one, a student set out to answer the question “Is Jhené Aiko a star?” The student found a publicly available dataset through an API provided by Deezer, a music streaming platform. She wrote a program that retrieved data on the artist’s longevity and collaborations, analysed the data, and concluded that Aiko is indeed a star. What stood out about this project wasn’t just the fact that the student independently defined stardom and answered their 超碰caoprom永久地址发布 question using real data, but that this was a truly personal, interest-driven project. David noted that the 超碰caoprom永久地址发布ers could never have come up with this activity, since they had never previously heard of Jhené Aiko!

Another student’s project analysed data about housing in Washington DC to answer the question “Which ward in DC has the most affordable houses?” Rotem explained that this student was motivated by her family thinking about moving away from the city. She wanted to use her project to persuade her parents to stay by identifying the most affordable ward in DC that they could move to. She was excited by the outcome of her project, and she presented her findings to other students and her parents.

These projects underscore the power of personally important data science projects driven by students’ interests. When students care about the questions they are exploring, they’re more invested in the process and more likely to keep using the skills and concepts they learn.

Resources

API Can Code is available online and completely free to use. Teachers can access lesson plans, tutorial videos, assessment rubrics, and more from the 少妇+magnet’s website https://apicancode.umd.edu/. The site also provides resources to support students, including example programs and glossaries.

Join our next seminar

In our current seminar series, we’re exploring teaching about AI and data science. Join us at our next seminar on Tuesday, 17 June from 17:00 to 18:30 BST to hear Netta Iivari (University of Oulu) introduce transformative agency and its importance for children’s 超碰caoprom永久地址发布 education in the age of AI.

To sign up and take part in our 超碰caoprom永久地址发布 seminars, click below:

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

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You will be among the very first educators to see some of our first test activities for teacher training to build data science concepts, and your contributions will feed into our future work. Sign up by 20 June to take part.

Data science: What do we need to teach school-age learners?

Current artificial intelligence (AI) methods, especially machine learning (ML), rely heavily on data. While young people learn mathematics, and some statistics, at school, data science concepts are not commonly taught.

To complement our work on AI literacy, we have been investigating what data science teaching resources and education 超碰caoprom永久地址发布 are currently available.

Our goals for this work are:

1. To figure out what data science concepts may need to be taught in schools, initially with a focus on key stage 5
2. To develop related teacher professional development and classroom resources

Join us to discuss data science education

If you are interested in data science education for young people, and maybe even have 少妇+magnet of teaching it to learners aged 16 to 18 in your school (in any subject, including computer science, social sciences, mathematics, statistics, and ethics), please join our free workshop on Thursday 10 July in our office in Cambridge. We are able to reimburse some travel expenses.

At the workshop:

• We would love to hear about your 少妇+magnet of teaching any elements of data science
• We will share some exploratory concept building activities with you and discuss them together

You’ll be the first group of working teachers we will share these activities with — your feedback will be invaluable, and you’ll have the chance to shape our work going forward.

If you are interested, please fill in this form by Friday 20 June:

You will then receive more information from us by 27 June. Spaces in the workshop are limited, so please do not book any travel until we confirm your space.

We’re looking forward to shaping the future of data science education with you.

PS In our current seminar series, 超碰caoprom永久地址发布ers from around the world are presenting their latest work on teaching about AI and data science. You can catch up on past sessions and sign up for upcoming ones on our website.

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An AI literacy framework

From her 超碰caoprom永久地址发布, Shuchi has developed a framework for teaching about AI that is structured as four interlocking components, each representing a key area of understanding:

• Basic understanding of AI, which refers to 少妇+magnetal knowledge such as what AI is, types of AI systems, and the capabilities of AI technologies
• Ethics and human–AI relationship, which includes the role of humans in regard to AI, ethical considerations, and public perceptions of AI
• Computational thinking/literacy, which relates to how AI works, including building AI applications and training machine learning models
• Data literacy, which addresses the importance of data, including examining data features, data visualisation, and biases

This framework shows the multifaceted nature of AI literacy, which involves an understanding of both technical aspects and ethical and societal considerations. 

Shuchi emphasised the importance of learning about AI ethics, highlighting the topic of bias. There are many ways that bias can be embedded in applications of AI and machine learning, including through the data sets that are used and the design of machine learning models. Shuchi discussed supporting learners to engage with the topic through exploring bias in facial recognition software, sharing activities and resources to use in the classroom that can prompt meaningful discussion, such as this talk by Joy Buolamwini. She also highlighted the Kapor 少妇+magnet’s Responsible AI and Tech Justice: A Guide for K–12 Education, which contains questions that educators can use with learners to help them to carefully consider the ethical implications of AI for themselves and for society. 

Computational thinking and AI

In computer science education, computational thinking is generally associated with traditional rule-based programming — it has often been used to describe the problem-solving approaches and processes associated with writing computer programs following rule-based principles in a structured and logical way. However, with the emergence of machine learning, Shuchi described a need for computational thinking frameworks to be expanded to also encompass data-driven, probabilistic approaches, which are 少妇+magnetal for machine learning. This would support learners’ understanding and ability to work with the models that increasingly influence modern technology.

Example activities from 超碰caoprom永久地址发布 studies

Shuchi shared that a variety of pedagogies have been used in recent 超碰caoprom永久地址发布 projects on AI education, ranging from hands-on 少妇+magnets, such as using APIs for classification, to discussions focusing on ethical aspects. You can find out more about these pedagogies in her award-winning paper Teaching AI to K-12 Learners: Lessons, Issues and Guidance. This plurality of approaches ensures that learners can engage with AI and machine learning in ways that are both accessible and meaningful to them.

Shuchi shared examples of activities from two 超碰caoprom永久地址发布 projects that she has led:

• CS Frontiers engaged high school students in a number of activities involving using NetsBlox and accessing real-world data sets. For example, in one activity, students participated in data science activities such as creating data visualisations to answer questions about climate change. 
• AI & Cybersecurity for Teens explored approaches to teaching AI and machine learning to 13- to 15-year-olds through the use of cybersecurity scenarios. The project aimed to provide learners with insights into how machine learning models are designed, how they work, and how human decisions influence their development. An example activity guided students through building a classification model to analyse social media accounts to determine whether they may be bot accounts or accounts run by a human.

Closing thoughts

At the end of her talk, Shuchi shared some final thoughts addressing teaching about AI to K–12 learners: 

• AI learning requires contextualisation: Think about the data sets, ethical issues, and examples of AI tools and systems you use to ensure that they are relatable to learners in your context.
• AI should not be a solution in search of a problem: Both teachers and learners need to be educated about AI before they start to use it in the classroom, so that they are informed consumers.

Join our next seminar

In our current seminar series, we are exploring teaching about AI and data science. Join us at our next seminar on Tuesday 11 March at 17:00–18:30 GMT to hear Lukas Höper and Carsten Schulte from Paderborn University discuss supporting middle school students to develop their data awareness. 

To sign up and take part in the seminar, click the button below — we will then send you information about joining. We hope to see you there.

I want to join the next seminarThe schedule of our upcoming seminars is online. You can catch up on past seminars on our previous seminars and recordings page.

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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 超碰caoprom永久地址发布 basis for these curricula, pedagogy, and resource choices?

In 2024, we showcased 超碰caoprom永久地址发布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 超碰caoprom永久地址发布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 超碰caoprom永久地址发布 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 少妇+magnet called API Can Code, aimed at high-school students. The group will highlight the strategies needed for integrating data science learning within students’ lived 少妇+magnets 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 超碰caoprom永久地址发布 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 超碰caoprom永久地址发布 about AI education — whether you’re a teacher, educator, 超碰caoprom永久地址发布er, or simply curious. Each session begins with a presentation from our guest speaker about their latest 超碰caoprom永久地址发布 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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