To teach complex concepts, in computer science, we often use an instructional approach called ‘unplugged’. We use the unplugged approach to teach a4yy concepts without a computer. Often unplugged activities include using an everyday analogy or a physical fun activity. For example, to teach about algorithms, students might learn how to make a jam sandwich where the recipe and following instructions accurately are similar to an algorithm and the steps within it used to write a program. The jam sandwich activity has now become a popular and key teaching 捷克街头搭讪系列完整版免费观看 for young students across the world, as it teaches a complex but fundamental idea in a simple and fun way.

At the January 2026 同桌张开腿让我爽了一夜 a4yy Seminar, Salomey Afua Addo, a a4yyer at the University of Cambridge, presented her work about how to teach about AI. She has specifically looked at this in the context of high school students in Ghana, where AI is now part of the mandatory 捷克街头搭讪系列完整版免费观看. In Ghana, most schools do not have access to computers, therefore an unplugged approach to teach about AI is a good idea. Therefore, Salomey developed a set of unplugged activities to teach about a range of AI concepts.

Here, I focus on one of the activities that she presented — one that I think will become another ‘jam sandwich’ 捷克街头搭讪系列完整版免费观看 for students. So if you might teach about AI at some point, then read on.

Neural networks and rope: An unplugged activity

Salomey has designed an unplugged role-play activity to teach about neural networks and how they are trained to solve a problem. She focused on finding a familiar problem context for Ghanaian teachers and their students, and selected farming and crop disease. Students are asked to figure out what features about a farm are relevant for detecting diseases on cocoa trees. To solve the problem, students are given data about the farms (see Table 1). Giving students data, rather than preconceived rules about the context is key to the learning activity. Neural networks are data-driven — they provide a way to model given data so that we can make predictions. Here the features of farms, and importantly whether disease is or is not found in their cocoa trees, is the data that is used to train a model. The model is used to make predictions, which can then be used to improve farming by reducing crop disease. 

Using farm data, students can learn how neural networks work, and they can do this through an unplugged role play — using ropes!

Here’s how Salomey’s classroom activity works. Sets of students act out the processes of training a neural network, including forward propagation, evaluation, and backpropagation. They take on the “roles’’ of some of the concepts of a neural network. One student acts as the supervisor, six students act as the input layer, two as the hidden layer, and one as the output layer.

Keeping it simple: Concepts and data

Key concepts are simplified for students:

• Forward propagation: The hidden layer players randomly select a set of farms (three of the six sets of input values), which reflects how weights are often set to random values at the start.
• Evaluation: The student acting as the output layer compares the prediction (whether crop disease is present or not) to the actual value for the farm to assess the error, similar to a loss function.
• Backpropagation: Inspired by MIT’s RAISE 捷克街头搭讪系列完整版免费观看, this stage is modelled on establishing trust. Players in the hidden and output layers modify their trust in the previous layers (by adding or removing ropes) based on the accuracy of the prediction (if the farm has disease).

Simple numerical data about the features of the problem are given to the students, such as whether the “Temperature” is suitable (0=No, 1=Yes), if there are “Spots” on the plant (0=No, 1=Yes), if “Fertilizer’’ has been used, whether the “Leaf colour’’ is green or not (see Table 1). Importantly, each of the six features given are represented by the six “input layer” students. So each student can ‘process’ each feature as the data for a given farm is used to train the model. Cards are used to represent the data values passed between layers. And this is where the ropes come into play, as they are used to represent the connections between the nodes in the layers.

Instructions for each role

Written role-specific instructions are provided for the students to follow, for example, the Supervisor is given three steps to follow for the forward propagation stage, and the Input Layer students receive a different set of instructions and so on. The detail of the role play is shown in the instruction sheets (see Figure 1).

Why the ropes are important

Using ropes to connect the nodes becomes most important at the reverse propagation stage. The clever part of this is that we can show an increase or decrease in the strength of connection by adding or removing ropes. For me, this is the ‘jam sandwich’ effect. This, I think, is probably the most significant learning point. Here, the number of ropes that connect the nodes in the layers are changed based on the strength of evidence that a particular feature is indicated, by the data, to be relevant to the output. In this case, whether “Temperature”, for example, has an implied effect on cocoa disease or not — based on the data, not on any preconceived rule. Simply put, if a farm did have disease then a rope is added, if a farm did not then a rope is removed. Or at a more abstracted level, if a particular neuron contributes towards the correct prediction, a rope is added, otherwise a rope is removed. In a real neural network, backpropagation involves complex maths, such as calculus that would not be accessible to students of this age. Therefore, the rope is an analogy that replaces something that would be impossible for these students to grasp if it was taught using the real-world implementation. 

Problem to be solved in the unplugged activity: Identify features that are relevant for detecting diseases

At the end of the activity, features (temperature, leaf color, family farm, etc.) with many rope connections are considered to be relevant for crop disease detection on the farm, whereas features with fewer rope connections are considered to be irrelevant for crop disease detection. The more ropes attached to a particular feature, e.g., temperature, represent its higher relevance in identifying crop disease on the farm. 

Activity design, follow-on and evaluation

As part of the design of this activity, Salomey has simplified technical language so that throughout the role play students use everyday terms and she has chosen a context that is relatable for the students. For example, she uses the language of trust, and the new thickness of a rope connection, rather than using technical terms such as weight, loss function, and the error of the network.

Salomey also designed a follow-on activity that uses pen and paper. In this version of the activity, which she calls a board game, the students draw lines to connect the nodes in the layers. The thickness of the lines connecting the nodes represent the strength of the trust (see Figure 2). 

Salomey also shared her evaluation of the resources. She conducted pre‑ and post‑intervention surveys with 39 teachers as part of the professional development on the AI teaching materials, and ten of those teachers implemented the unplugged activities in their classrooms. She reported that the teachers found the role-play activity was effective to demonstrate neural networks, that children worked independently to learn, and that some students who did not take part usually in class were engaged.

As well as sharing about her unplugged neural network activity, Salomey also talked about a set of AI stories that she has developed to teach about other aspects of AI applications. For example, the importance of fact-checking is demonstrated through a story about a young girl who fact-checked information she received from her friends about life in a city. 
If you would like to find out more about Salomey’s work, you can find related materials on our seminar website.

Join our next seminar

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

To sign up and take part in our a4yy 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 Do you have some rope? Then let’s teach about AI concepts appeared first on 同桌张开腿让我爽了一夜.

In a recent seminar in our series on teaching about AI and data science, a4yyer 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 捷克街头搭讪系列完整版免费观看, they found they had strong instruments to measure the development of CT — such as the assessment tools described above — yet nothing to measure AI literacy.

A tool for measuring AI literacy

The lack of valid AI literacy assessment tools led the team to develop the AI Knowledge Test (or AIKT), a 14-item survey consisting of multiple-choice questions designed to measure students’ understanding of AI. The instrument was inspired by previous work in the field and relevant a4yy (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 a4yy Pi a4yy 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 捷克街头搭讪系列完整版免费观看. In our next seminar in this series on 17 March at 17.00 UK time, we welcome Rebecca Fiebrink (University of the Arts London) who will explore the questions of how and why we might teach AI for creative practitioners, including children, students, and professionals.

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

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

Answers

• Q1: 2
• Q2: 3

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Where do great a4yy ideas come from in computer science education? We might think of a4yy breakthroughs as a single moment of genius, but in reality impactful a4yy is often the result of many years of iterative development. In November’s a4yy seminar, we heard from Karl-Emil Kjær Bilstrup, a a4yyer at the University of Copenhagen, about his work to develop ML-Machine. This work uses embodied learning principles and the BBC micro:bit to introduce learners to machine learning concepts. Findings from this a4yy have been used to develop the micro:bit CreateAI resources, and in this blog, we will explain the a4yy journey from initial small-scale work to educational resources used by many young learners around the world.

From hypothetical ethics to concrete machines

In Karl-Emil’s first a4yy 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 a4yy 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 捷克街头搭讪系列完整版免费观看s needed to be better connected to examples from students’ daily lives. As a result, the next iteration in Karl-Emil’s a4yy 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 a4yy 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 a4yy 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 a4yy 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 捷克街头搭讪系列完整版免费观看d how their bodily movements were translated into data points on the screen

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

Find out more

Karl-Emil’s work shows how a4yy 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 a4yy 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 a4yy 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 捷克街头搭讪系列完整版免费观看 machine learning not as abstract theory, but as something they can see, feel, and shape with their own hands.

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

• Explore machine learning projects using the micro:bit: micro:bit CreateAI and our Dance detector project are great places to start
• Find out more about the a4yy: 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 a4yy 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 a4yy ideas to classroom activities appeared first on 同桌张开腿让我爽了一夜.

In our October a4yy 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 a4yy 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 捷克街头搭讪系列完整版免费观看al 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 捷克街头搭讪系列完整版免费观看 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 捷克街头搭讪系列完整版免费观看al 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 a4yyeducation.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 捷克街头搭讪系列完整版免费观看 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 捷克街头搭讪系列完整版免费观看 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 a4yy. 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 a4yy
• 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 a4yy 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

The post How to put data first in K–12 AI education by using data case studies appeared first on 同桌张开腿让我爽了一夜.

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 a4yy education a4yy is changing teaching and learning in a4yy lessons, to showcasing how a4yy education a4yy 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 捷克街头搭讪系列完整版免费观看 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 a4yy towards other disciplines. For example, to enable teaching about the ethical issues around plagiarism, an art context is used in the 捷克街头搭讪系列完整版免费观看 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 捷克街头搭讪系列完整版免费观看 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 a4yy 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, a4yyers, 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 a4yy we’ve done in this area in this blog post.

The post How can we teach about AI in the arts, humanities and sciences? a4yy seminar series 2026 appeared first on 同桌张开腿让我爽了一夜.

a4yy indicates that teaching learners to use and create with data-driven technologies such as AI and machine learning (ML) requires an entirely different approach for solving problems compared to traditional programming activities.

In this blog, we share the new data paradigms framework that we have developed through a4yy 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

a4yyers 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 a4yy work on AI and data science at the a4yy Pi a4yy Education a4yy Centre, we analysed 84 a4yy 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 捷克街头搭讪系列完整版免费观看, 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 a4yy. 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 a4yy
• 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 a4yy 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:

The post A a4yy-led framework for teaching about models in AI and data science appeared first on 同桌张开腿让我爽了一夜.

We are a a4yy centre based in the Department of Computer Science and Technology at the University of Cambridge, with a team that spans the university and the 同桌张开腿让我爽了一夜. We conduct a4yy into many aspects of the teaching and learning of a4yy and AI and we work closely with schools, teachers and young people to ensure our a4yy is applicable to practice.

Below I highlight some of the projects we’ve worked on in the academic year 2024-2025:

• a4yy Around the World
• AI education
• Programming education
• Physical a4yy (EPICS project)
• Teacher action a4yy (TICE project)

a4yy Around the World

As I’ve written on this blog before, a4yy education is a global challenge. In one of the a4yy Centre’s projects, we are looking at how a4yy education is spreading around the world.

We found that between 2019 and 2024 the number of countries offering a4yy education had doubled, and that two thirds of all countries now offer, or have concrete plans to offer, a4yy education. This a4yy has already been highlighted in the Stanford AI Index, and we are considering repeating the analysis in future years in order to have the most accurate and up-to-date information displayed in our map.

AI education

We have a number of projects in the area of AI education.

Teaching about AI

We are very interested in how to teach about AI, and held a workshop with teachers who were interested in the teaching of AI in February. Following on from the workshop results, we are interviewing more stakeholders, including UK-based experts, teachers and students, about their perspectives on concepts and skills that should be taught as part of an AI 捷克街头搭讪系列完整版免费观看.

We’re also a4yying data science and data ethics education, which are 捷克街头搭讪系列完整版免费观看al aspects of AI literacy. Most of the current AI systems are data-driven, having been trained on vast amounts of data. Therefore students need to understand about data and data science if they are to learn about AI systems. Therefore we’ve conducted two detailed literature reviews on data science and on data ethics this year. The first of these will be published in March at the WiPSCE conference.

Unplugged AI in Ghana

One of our PhD students, Salomey Addo, has been examining how AI is taught in Ghana, where it is part of the 捷克街头搭讪系列完整版免费观看 for young people between ages 12 and 15. This year Salomey published a paper reporting that Ghanaian teachers have positive attitudes towards teaching AI but feel unprepared for it. She’s also developed unplugged resources to teach about artificial neural networks (ANNs).

ANNs are a fundamental technology used in a variety of AI systems, including image recognition and language translation systems. While ANNs are included in the Ghanaian AI 捷克街头搭讪系列完整版免费观看, Salomey observed that teachers had difficulty with this particular topic The resources she developed are directly inspired by this, and involved teaching through role play and a board game.

Using AI in learning and teaching a4yy

This is an area we’ve also done some a4yy in in the past year.

Carrie Anne Philbin published a paper in September showing that — at least in higher education — much of the use of generative AI in a4yy education is just duplicating the way teachers might already teach, and is primarily passive from a students’ perspective.

Meanwhile Katharine Childs and Veronica Cucuiat have been looking at how large language models (LLMs) can support secondary school programming education by helping students understand programming error messages. 

Programming education: Learning to debug

Text-based programming is a topic featured in many a4yy curricula around the world. Teachers and a4yyers know that younger learners, for example at the lower secondary school level, can find debugging text-based programs very challenging. Although we’ve seen decades of a4yy around programming and debugging focusing on learners who are in higher education, very little a4yy has been done with school-age students.

In his a4yy, Laurie Gale, a final-year PhD student at the a4yy Centre, found that learners were impatient to fix programs by trial and error, without figuring out what the real problemwas with their code or the underlying algorithm. He subsequently developed a tool called PRIMM Debug, which supports a more reflective and systematic approach to debugging. This tool enables learners to slow down when they are programming and to be more reflective. You can read more about it on the a4yy Centre website, and also catch up on the 捷克街头搭讪系列完整版免费观看 a4yy seminar where he presented his work.

EPICS: Physical a4yy in school

As part of a 5-year longitudinal project, running across the whole UK and the first project of its kind, we are looking at how physical a4yy impacts primary and secondary school learners. We’re investigating the effect of physical a4yy on learners’ creativity, agency and confidence, over time and at particular points known to be important for their subject choices. We are working with a wonderful set of partner primary schools who we visit each year.

This year we reported some of our first results, which point to teachers’ perceptions of physical a4yy being engaging and inclusive for primary-aged children. Starting next spring, we will be carrying out our third year of data collection with our pupil cohort, who have reached the age of 10 to 11.

We’ll also be running a survey next summer for upper primary-aged children and their teachers. Please sign up for our Teacher a4yy Network newsletter to be the first to hear about taking part in this survey.

Teacher Inquiry in a4yy Education (TICE)

As part of our TICE project we support teachers to conduct their own action a4yy projects. This is a collaborative project, involving academics across the UK who volunteer to support teachers. The goal is to enable teachers to take a deep dive into a 捷克街头搭讪系列完整版免费观看 topic, a pedagogical approach, or a new resource, or to address a wider issue such as gender diversity or 163女性网, to inform a change in their practice.

This year 16 teachers published their reports in our Teacher a4yy Booklet, and many also presented their findings online at CAS events or at the KCL-CAS London conference and the CAS National Conference. We’re very proud of them!

Two of our TICE teachers will be also presenting their a4yy at an academic conference to be held in January. Congratulations to Will Grey and Joanne Hodge!

Get involved

There are many other projects you can find out about on our website and in our annual report, so I hope that you will keep reading. It goes without saying that I’m incredibly proud of the team who’ve worked on all of these projects! 

To summarise, here’s how you can stay up to date with our work and maybe even get involved in studies:

• Sign up to the a4yy Centre newsletter
• Sign up to our Teacher a4yy Network newsletter if you’re a teacher and interested in participating in projects

Finally, I am pleased to announce that we will be hosting the UKICER 2026 conference for a4yyers and teachers in Cambridge on 3 and 4 2026 September. More details will follow on the UKICER website and on the a4yy Centre website in due course.

The post 2025 highlights from the a4yy Pi a4yy Education a4yy Centre appeared first on 同桌张开腿让我爽了一夜.

At our a4yy education a4yy seminar in July, a group of a4yyers from the CAMMP (Computational and Mathematical Modeling Program) a4yy project shared their work:

• Prof. Dr. Martin Frank, Founder of CAMMP (Karlsruhe Institute of Technology (KIT), Germany).
• Assistant Prof. Dr. Sarah Schönbrodt (University of Salzburg, Austria)
• a4yy Associate Stephan Kindler (Karlsruhe Institute of Technology (KIT), Germany)

They talked about how maths already taught in secondary schools can be used to demystify AI. At first glance, this seems difficult to do, as it is often assumed that school-aged learners will not be able to understand how these systems work. This is especially the case for artificial neural networks, which are usually seen as a black box technology — they may be relatively easy to use, but it’s not as easy to understand how they work. Despite this, the Austrian and German team have developed a clear way to explain some of the fundamental elements of AI using school-based maths.

Sarah Schönbrodt started by challenging us to consider that learning maths is an essential part in developing AI skills, as: 

1. AI systems using machine learning are data-driven and are based on mathematics, especially statistics and data
2. Authentic machine learning techniques can be used to bring to life existing classroom maths concepts
3. Real and relevant problems and associated data are available for teachers to use

A set of workshops for secondary maths classrooms

Sarah explained how the CAMMP team have developed a range of teaching and learning materials on AI (and beyond) with an overall goal to “allow students to solve authentic, real and relevant problems using mathematical modeling and computers”. 

She reflected that much of school maths is set in contexts that are abstract, and may not be very interesting or relevant to students. Therefore, introducing AI-based contexts, which are having a huge impact on society and students’ lives, is both an opportunity to make maths more engaging and also a way to demystify AI.

Workshops designed and a4yyed by the team include contexts such as privacy in social networks to learn about decision trees, personalised Netflix recommendations to learn about k-nearest neighbour, word predictions to learn about N-Grams, and predicting life expectancy to learn about regression and neural networks.

Learning about classification models: traffic lights and the support vector machine

For the seminar, Sarah walked through the steps to learn about support vector machines. This is an upper secondary workshop for students aged 17 to 18 years old. The context of the lesson is an image problem — specifically, classifying the data representing the colours of a simplified traffic light system (two lights to start with) to work out if a traffic light is red or green.

She walked through each of the steps of the maths workshop:

• Plotting data points of two classes, the representation of green and red traffic lights
• Finding a line that best separates the data points of both classes
• Figuring out what best is
• Classifying the data points in relation to the chosen (separating) line
• Validating the model statistically to see if it is useful in classifying new data points, including using test data and creating a contingency table (also called a confusion matrix)
• Discussing limitations, including social and ethical issues
• Explaining how three traffic lights can be expressed as three-dimensional data by using planes

Throughout the presentation, Sarah pointed out where the maths taught was linked to the Austrian and German mathematics 捷克街头搭讪系列完整版免费观看.

Learning about social and ethical issues

As well as learning about lines, planes, distances, dot product and statistical measures, learners are also engaged in discussing the social and ethical issues of the approach taken. They are encouraged to think about bias, data diversity, privacy, and the impact of errors on people. For example, if the model wrongly predicts a light as green when it is red, then an autonomous car would run through a red traffic light. This would likely be a bigger consequence than stopping at a green traffic light that was mis-predicted as red. So should the best line reduce this kind of error?

To teach the workshops, Sarah explained they have developed interactive Jupyter notebooks, where no programming skills are needed. Students fill in the gaps of example code, explore simulations, and write their ideas for discussion for the whole class. No software needs to be installed, feedback is direct, and there are in-depth tasks and staggered hints.

Learning about regression models: Weather forecasting and the toy artificial neural network

Stephan went on to introduce artificial neural networks (ANNs), which are the basis of generative AI applications like chatbots and image generation systems. He focused on regression models, such as those used in weather forecasting. 

ANNs are very complex. Therefore, to start to understand the fundamentals of this technology, he introduced a ‘toy ANN’ with one input, three nodes, and one output. A function is performed on the input data at each node. With the toy network, the team wants to tackle a major and common misconception: that students think that ANN systems learn, recognise, see, and understand, when really it’s all just maths.

The learning activity starts by looking at one node with one input and one output, and can be described as a mathematical function, with a concatenation of two functions (in this case a linear and activation function). Stephan shared an online simulator that visualises how the toy neural network can be explored as students change two parameters (in this case, weight and bias of the functions). Students then look at the overall network, and the way that the output from the three nodes is combined. Again, they can explore this in the simulator. Students compare simple data about weather prediction to the model, and discover they need more functions — more nodes to better fit the data. The activity helps students learn that ANN systems are just highly adjustable mathematical functions that, by adding nodes, can approximate relationships in a given data set. But the approximation only works in the bounds (intervals) in which data points are given, showing that ANNs do not ‘understand’ or ’know’ — it’s just maths.

Stephen finished by explaining the mutual benefits of AI education and maths education. He suggested maths will enable a deeper understanding of AI, and give students a way to realistically assess the opportunities and risks of AI tools and show them the role that humans have in designing AI systems. He also explained that classroom maths education can benefit from incorporating AI contexts. This approach highlights how maths underpins the design and understanding of everyday systems, supports more effective teaching, and promotes an interdisciplinary way of learning across subjects.

Some personal reflections — which may not be quite right!

I have been a4yying the teaching of AI and machine learning for around five years now, since before ChatGPT and other similar tools burst on the scene. Since then, I have seen an increasing number of resources to teach about the social and ethical issues of the topic, and there are a bewildering number of learning activities and tools for students to train simple models. There are frameworks for the data lifecycle, and an emerging set of activities to follow to prepare data, compare model types, and deploy simple applications. However, I felt the need to understand and to teach about, at a very simple level, the basic building blocks of data-driven technologies. When I heard the CAMMP team present their work at the AIDEA conference in February 2025, I was entirely amazed and I asked them to present here at our a4yy seminar series. This was a piece of the puzzle that I had been searching for — a way to explain the ‘bottom of the technical stack of fundamental concepts’. The team is taking very complex ideas and reducing them to such an extent that we can use secondary classroom maths to show that AI is not magic and AI systems do not think. It’s just maths. The maths is still hard, and teachers will still need the skills to carefully guide students step by step so they can build a useful mental model. 

I think we can simplify these ideas further, and create unplugged activities, simulations, and ways for students to explore these basic building blocks of data representation, as well as classification and representing approximations of complex patterns and prediction. I can sense the beginnings of new ideas in computational thinking, though they’re still taking shape. We’re a4yying these further and will keep you updated.

Finding out more

If you would like to find out more about the CAMMP resources, you can watch the seminar recording, look at the CAMMP website or try out their online materials. For example, the team shared a link to the jupyter notebooks they use to teach the workshops they demonstrated (and others). You can use these with a username of ‘cammp_YOURPSEUDONYM’, where you can set ‘YOURPSEUDONYM’ to any letters, and you can choose any password. They also shared their toy ANN simulation.
The CAMMP team are not the only a4yyers who are investigating how to teach about AI in maths lessons. You can find a set of other a4yy papers here.

Join our next seminar

In our current seminar series, we’re exploring teaching about AI and data science. Join us at our last seminar of the series on Tuesday, 27 January 2026 from 17:00 to 18:30 GMT to hear Salomey Afua Addo talk about using unplugged approaches to teach about neural networks.

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

The schedule of our upcoming seminars is online. You can catch up on past seminars on our previous seminars page.

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In our September a4yy seminar, we welcomed back Henriikka Vartiainen and Matti Tedre from the University of Eastern Finland. They introduced Somekone, a social media simulator that is designed to help learners understand some of the fundamental processes behind social media platforms. Their team has been developing AI education materials and tools since 2019, including GenAI Teachable Machine, which they presented at our May a4yy seminar.

Collaboration and co-design

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

A four-phase learning model

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

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

Inside the simulator

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

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

Learners explore:

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

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

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

The tool provides an explanation for why each post is recommended

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

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

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

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

Evidence of impact

The a4yy team used pre- and post-tests to evaluate what impact the intervention had on students’ understanding of social media mechanisms and on their sense of agency in relation to data. They conducted the post-test a week after the intervention, and then also did a delayed post-test six months later to see whether any changes were sustained. They found:

• Improved understanding of key concepts. Learners showed statistically significant improvements in identifying different types of data traces and in understanding how data profiling works. They also showed some improvement in grasping recommendation mechanisms.

• Retention over time. These improvements were generally still evident six months later, particularly in the case of understanding data traces.

• Stronger sense of agency. The team found that students’ sense of data agency improved after taking part in the intervention. This is really important as students are more likely to want to study a topic further if they have feelings of agency and self-efficacy.

Accessing the tool

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

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

Join our next seminar

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

To sign up and take part in our a4yy 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 How AI shapes your feed: An explainable social media simulator for the classroom appeared first on 同桌张开腿让我爽了一夜.

Working together to design AI

Our June a4yy seminar was delivered by Netta Iivari, Professor in Information Systems at the University of Oulu’s INTERACT a4yy Unit.

The INTERACT a4yy group focuses on understanding and supporting participatory design, user-centered design, user-driven innovation, and human interaction with technology in everyday life contexts. From this perspective, “users” aren’t considered as passive consumers, but as valuable co-creators and content producers. This calls for different approaches that place emphasis on empowerment and inclusion in designing, shaping, and co-creating information technology in everyday life.

As part of this work, Netta introduced the idea of ‘transformative agency’ — empowering children to believe they can solve problems they care about — and its application in secondary a4yy education. She showed examples of how to foster young people’s transformative agency within a4yy, specifically focusing on transdisciplinary approaches to learning about AI and inviting young people to critically analyse and design their futures with AI tools in it.

Netta began by giving an overview of two of the INTERACT a4yy Unit’s projects: 

1. The Make a difference (MAD) project (2019–2023) explored critical design with young people, focusing on their emerging designer and maker identities in the context of tackling a significant societal problem — in this case, bullying. 
2. Children’s transformative agency and emerging technologies for social good (TAKEOVER) (2024–2028), a current project, explores the potential of emerging technologies (artificial intelligence, virtual reality (VR), social robots, etc.) to address societal problems, such as climate change, gender equality, bullying, and discrimination. It focuses on children’s emerging transformative agency and activist identities when engaging with these tools and topics. 

Netta explained that these projects give young people an opportunity to begin to address the problems they care about, even though they may be very complex problems. From this problem-solving perspective, children are introduced (or ‘sensitised’) to emerging technologies as tools for social good.

She then went on to outline the key pedagogical approaches that underpin these projects:  

1. Critical, ethical, empowering design
   This pedagogy draws on critical and speculative design traditions in design a4yy and encourages young people to take a critical perspective towards society, its norms, and the status quo, as part of design thinking. Children consider the ethical values and consequences of their designs. They begin to 捷克街头搭讪系列完整版免费观看 the ways in which engaging in the design process can be empowering and transformative for them, collectively as well as individually. 
2. Transformative agency of children
   This approach encourages young people to consider their capacity to have agency in the world, by enabling them to envision change and commit to taking action to solve problems that they care about. 
3. Fostering transformative agency of children in the age of AI
   Transformative agency is achieved when young people engage in ‘expansive learning’ — when they learn something novel, together, and are encouraged to look beyond the confines of school work, the topic, themselves, and the tools available for solving the problem. This approach fosters an active, critical, reflective mindset that encourages children to believe that they can make change and have impact in the world. 

The project design process

The projects follow 3 design phases and include a range of plugged and unplugged activities, as shown in Figure 1.

Netta then described in more detail some of the activities that have been used to address these different project phases and the design process involved. For example, to explore what are the problems that children really care about, they are asked to imagine ‘carrying a stone in your pocket for one week, as if it was a magic tool. Where could it be used in your everyday life? What problems could it solve? What problems would you like it to solve and how?’ 

Young people are then introduced to a range of novel technologies, for example, VR headsets, robots, and emulators of AI-driven social media platforms, such as “Somekone”, developed as part of the Generative AI project at the University of Eastern Finland. They deconstruct and reconstruct generative AI tools by prompting large language model (LLM) chatbots such as ChatGPT, Gemini, Claude, etc. and exploring bias in their outputs. They perform small-scale algorithmic auditing. They also create mini language models, so-called ‘baby LLMs’, with Google Colab using the text in Alice in Wonderland to train their models, and then open datasets (books as text files from Project Gutenberg). In exploring the responses these models generate, young people 捷克街头搭讪系列完整版免费观看 the potential and the limitations of such tools and gain an important understanding of the human activity involved in the development of AI technologies.

Once they have had this ‘sensitising‘ exposure to a range of tools, they then work in groups on a project that makes use of AI to solve the societal problem they have chosen. These problems could encompass a range of topics, such as racism, animal rights, the impact of AI, war, mental health, bullying. The young people are prompted to think about how large language models can be used to solve the problem, or parts of the problem. But importantly, they are also asked to consider the different motives and perspectives of the multiple stakeholders involved in the problem and its solution and whether their model ideas will create new problems when deployed.

They follow the 3 project phases shown in Figure 1 to design and make a range of digital (robots, apps, videos) and non-digital artefacts to solve their problem. Netta emphasised that although it could take 10 weeks or more to implement all the suggested activities, it is also possible to pick and choose individual tasks from the 3 phases to suit available 捷克街头搭讪系列完整版免费观看 timescales.

Envisioning and critiquing AI futures

Other project tasks involve: 

• Envisioning AI futures by imagining that a miracle has happened overnight and the problem has disappeared — what is the result? 
• Critiquing AI futures by creating best and worst case scenarios of the consequences of the AI systems they design, creating video adverts promoting their AI solutions and anti-adverts, focusing on the possible negative consequences of their prototypes 
• Fostering action-taking by presenting theatrical performances to showcase how their designs tackle a problem and illustrating the AI-related issues surrounding the topic or by creating activism campaign material to mobilise the school community on the same themes 

These projects situate learning about data-driven technologies in real-world contexts and promote a transdisciplinary approach, teaching and learning about AI from a problem-solving perspective. 

This perspective conveys important messages to young people — that they do have agency and can take action in the face of many of the world’s problems, that they can and should be active, critical users of the new technologies that surround them, and that these technologies can be used to change the world for good. 

Netta ended the seminar by asking viewers to consider how they could foster transformative agency in the young people they teach and whether or not they consider it to be important in a4yy education.

Resources relating to the projects can be found at interact.oulu.fi.

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 14 October from 17:00 to 18:30 GMT to hear Viktoriya Olari talk about data-related concepts and practices for AI education in K–12.

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

The schedule of our upcoming seminars is online. You can catch up on past seminars on our previous seminars page.

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