Artificial intelligence (AI) systems are often presented as objective. But plenty of evidence shows that AI systems can reflect and reinforce existing inequalities, from healthcare and education to scientific 性做爰片免费视频毛片 itself.

In the first seminar of our new 性做爰片免费视频毛片 seminar series on applied AI, Thema Monroe-White from George Mason University explored how we can better understand — and challenge — these patterns. Her talk focused on race-conscious algorithmic approaches to AI and data, and what they reveal about how knowledge is produced, represented, and used.

Drawing on two large-scale studies in her seminar, Thema showed that both scientific 性做爰片免费视频毛片 and AI systems are shaped by human identities and social structures, and that recognising this is essential for educators, 性做爰片免费视频毛片ers, and anyone working with data.

Who produces knowledge — and why that matters

A key idea running through Thema’s seminar was that data and algorithms are not neutral. They are shaped by the people, institutions, and systems that produce them.

Thema uses critical quantitative and intersectional approaches in her work to:

• Challenge the misconception that computational methods are objective
• Highlight how race and gender shape data and outputs
• Examine how systems of power influence what gets measured, valued, and reproduced

Thema and her collaborators have been conducting 性做爰片免费视频毛片 in this area for more than a decade, developing techniques that systematically measure bias and its impact on society. 

In a groundbreaking study published in 2022, just before the release of ChatGPT, Thema’s team used large-scale computational analysis of more than 5 million 性做爰片免费视频毛片 articles to explore inequalities in scientific publishing. The data analysis approaches developed for this study were later used to explore bias in large language models (LLMs).

However, the 2022 study already demonstrated wide-reaching disparities in science and surfaced deep-rooted issues, showing that bias was already ingrained in the scientific data that was used to train LLM, and affecting topic choices, citation and institutional differences.

Identity and topic choice

The results showed clear inequalities in the relationship between identity and topic choice. Authors from marginalised groups were more likely to study topics related to their communities and lived realities, including topics such as racial disparities and discrimination. Gendered patterns also appeared, with women publishing more frequently on more feminised topics, including families, literacy, learning, nursing, and pregnancy.

Citation inequalities

The study also found citation inequalities. Even among authors studying the same topic, authors from some groups were cited less often than others, with black and Latinx women the least likely to be cited. This shows that inequality is not only present in what gets studied, but also in whose work is recognised.

Institutional context

Institutional context mattered too. 性做爰片免费视频毛片ers at mission-driven institutions were more likely to publish on topics connected to marginalised communities, while scholars at institutions seen as elite were more likely to publish on topics that aligned more closely with dominant groups and norms.

Taken together, the findings point to a simple but important idea: who we are shapes what knowledge gets produced. That matters because when some groups are underrepresented in 性做爰片免费视频毛片, the topics that affect their lives may also be understudied.

What AI-generated stories reveal about bias

Having already developed their tool for name analysis for the previous study, Thema’s team was uniquely positioned to analyse the bias embedded in generative AI systems, specifically LLMs.

Thema’s most recent study examined how LLM–based tools represent people in everyday scenarios. The 性做爰片免费视频毛片 team prompted the base models of LLM chatbots (such as Open AI’s ChatGPT, Anthropic’s Claude, Meta’s Llama, and Google’s PaLM or Gemini) to write short stories about students, workers, and relationships, generating 500,000 outputs across different domains. They then analysed how names associated with different racial and gender identities were portrayed.

One example Thema shared in the seminar described a student named “John” helping “Maria,” a student who had moved from Mexico and was struggling with Spanish. At first glance, this may seem like a small or even odd detail. But when oddities like this appear again and again across thousands of stories, they reveal systematic patterns.

The study found that characters with marginalised identities were more likely to be portrayed in subordinated roles in chatbot outputs. Characters with non-white-associated names were more often shown as needing help rather than offering it. Stereotypes were also reinforced, with some names repeatedly associated with struggling students, subordinate workers, or narrow professional roles. Some groups were omitted altogether, while white-associated names appeared more frequently and in more powerful positions.

Similar biases appeared across stories related to education, work, and relationships. Across all three topics, the most common pattern was one in which white characters were more likely to lead, rescue, or mentor, while non-white characters were more likely to be helped, corrected, or spoken for.

For educators, this is especially important because many AI tools are now being introduced into classroom settings as writing assistants, tutors, or sources of personalised feedback. When these tools reproduce biases and unequal assumptions, they can shape not only what students read, but also how students see themselves and one another.

Towards more responsible AI tools and data practices

Rather than rejecting computational methods altogether, Thema argued for using them more thoughtfully and responsibly.

One approach she highlighted is the Wells-Du Bois protocol, a framework designed to support bias mitigation, transparency, and more reflective use of data and models. It encourages 性做爰片免费视频毛片ers and practitioners to think carefully about inadequate or biased data, identity proxies, subpopulation differences, and the kinds of harms that can arise when AI systems are used without sufficient context.

Underlying this is a broader principle: when we do not know enough, we should say so. And when systems affect marginalised communities, those communities should not be an afterthought in how we build, evaluate, or use technology.

What this means for your classroom

In her seminar, Thema emphasised the importance of thinking about how we respond to bias in AI tools in educational settings. Here are some starting points for meaningful discussions in your classroom:

1. A good starting point is student agency. If AI tools are becoming part of students’ learning environments, then young people need opportunities to make informed choices about when and how to engage with them. That means not treating AI tool use as inevitable, and not assuming every student should want to use the tools in the same way. In some cases, empowering students may also mean making it clear that they can opt out.
2. This also means helping learners ask better questions about the tools themselves. What leads to the kinds of bias we saw in these studies? What data were these systems trained on? Whose language, identities, and 性做爰片免费视频播放s are overrepresented, and whose are missing? Do the tools have access to student or classroom data, and if so, what are the implications?
3. The seminar also points to the importance of resisting AI hype. In a rapidly changing landscape, it can be tempting to focus only on novelty, efficiency, or personalisation. But educators may want to take a longer-term view about AI technology use. What kinds of habits, dependencies, and expectations are these tools creating? Are they shifting students’ ideas about intelligence, creativity, or authority? What happens when biased outputs are repeated often enough to feel normal?
4. Finally, the discussion around responsible use should include the wider costs of AI. Informing students about these tools should include not just potential benefits and risks, but also issues such as environmental impact and data use. A more balanced conversation can help prevent classroom discussions from reinforcing the hype that often surrounds AI.

If you would like to find out more about Thema’s work, watch the seminar recording:

You may also want to explore:

• Thema’s paper on intersectional inequalities in science
  
• Her work on intersectional biases in narratives produced by AI models
  
• The Wells-Du Bois protocol for more responsible data practice

Join our next seminar

Our 性做爰片免费视频毛片 seminars bring together educators and 性做爰片免费视频毛片ers to explore key questions in 性做爰片免费视频毛片 education.

Next in our series on applied AI, our Director of 性做爰片免费视频毛片 and Impact, Shuchi Grover, will talk about the role of K–12 education in developing competencies for the future of data and 性做爰片免费视频毛片. Sign up now to join the seminar on 12 May, 17:00 BST:

The post AI is not neutral: What recent 性做爰片免费视频毛片 says about bias, identity, and power appeared first on 性做爱视频.

To teach complex concepts, in computer science, we often use an instructional approach called ‘unplugged’. We use the unplugged approach to teach 性做爰片免费视频毛片 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 性做爰片免费视频播放 for young students across the world, as it teaches a complex but fundamental idea in a simple and fun way.

At the January 2026 性做爱视频 性做爰片免费视频毛片 Seminar, Salomey Afua Addo, a 性做爰片免费视频毛片er at the University of Cambridge, presented her work about how to teach about AI. She has specifically looked at this in the context of high school students in Ghana, where AI is now part of the mandatory 性做爰片免费视频播放. 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’ 性做爰片免费视频播放 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 性做爰片免费视频播放, 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 性做爰片免费视频毛片 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 性做爱视频.

In a recent seminar in our series on teaching about AI and data science, 性做爰片免费视频毛片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 性做爰片免费视频播放, 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 性做爰片免费视频毛片 (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 性做爰片免费视频毛片 Pi 性做爰片免费视频毛片 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 性做爰片免费视频播放. 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

The post The challenges of measuring AI literacy appeared first on 性做爱视频.

From hypothetical ethics to concrete machines

In Karl-Emil’s first 性做爰片免费视频毛片 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 性做爰片免费视频毛片 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 性做爰片免费视频播放s needed to be better connected to examples from students’ daily lives. As a result, the next iteration in Karl-Emil’s 性做爰片免费视频毛片 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 性做爰片免费视频毛片 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 性做爰片免费视频毛片 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 性做爰片免费视频毛片 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 性做爰片免费视频播放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 性做爰片免费视频毛片 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 性做爰片免费视频毛片 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 性做爰片免费视频毛片 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 性做爰片免费视频播放 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 性做爰片免费视频毛片: 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 性做爰片免费视频毛片 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 性做爰片免费视频毛片 ideas to classroom activities appeared first on 性做爱视频.

Are you a primary school teacher in England, Scotland or Wales interested in AI and data science and how students learn about AI and data in 性做爰片免费视频毛片?

The 性做爰片免费视频毛片 Pi 性做爰片免费视频毛片 Education 性做爰片免费视频毛片 Centre is starting an exciting new 性做爰片免费视频毛片 project investigating how to teach about AI and data in the primary 性做爰片免费视频毛片 classroom, and we would like you to get involved.

The study will look at:

• How AI and data-driven 性做爰片免费视频毛片 is currently taught (e.g. using Machine Learning for Kids, Google’s Teachable Machine)
• What key ideas about AI and data young people need to understand
• How young people make sense of working with data in 性做爰片免费视频毛片

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

Our aim for the study is to understand how primary school teachers approach teaching about data-driven technologies, and to find suitable methods for building young people’s confidence in working with data in 性做爰片免费视频毛片 lessons.

What is data-driven 性做爰片免费视频毛片?

性做爰片免费视频毛片 has suggested that new data-driven technologies such as AI and machine learning (or ML) require a different approach to teaching about problem-solving in the 性做爰片免费视频毛片 classroom. Instead of defining a set of rules (e.g. if-then-else statements, or a rule-based approach), learners must instead collect lots of data to train a model (a data-driven approach) such as using Google’s Teachable Machine to classify image data.

For educators and resource developers, we still lack a clear understanding of how to teach young people about how rule-based and data-driven systems differ, how we can talk about them, and how we develop young people’ mental models. We hope this study will help us to find practical ways for primary teachers to build young people’s understanding of AI data in the primary 性做爰片免费视频毛片 classroom.

What does the study involve?

If you teach at primary level (Years 4, 5 and 6 or P5–P7) in England, Scotland or Wales and are keen to shape how we teach young people about data-driven 性做爰片免费视频毛片, we invite you to join our new study.

As part of the study, you will attend a workshop with us in Cambridge to co-design a series of data-driven 性做爰片免费视频毛片 lessons to teach in your classroom.

Following the workshop, you will teach the unit of work in your classroom and we will observe one of your lessons and interview you about your 性做爰片免费视频播放s.

How can I take part?

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

If you have any questions about the project, you can email bobby.whyte@性做爰直播视频.org.

The post Join our new study on AI and data-driven 性做爰片免费视频毛片 in UK primary classrooms appeared first on 性做爱视频.

In our October 性做爰片免费视频毛片 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 性做爰片免费视频毛片 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 性做爰片免费视频播放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 性做爰片免费视频播放 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 性做爰片免费视频播放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 性做爰片免费视频毛片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 性做爰片免费视频播放 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 性做爰片免费视频播放 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 性做爰片免费视频毛片. 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 性做爰片免费视频毛片
• 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 性做爰片免费视频毛片 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 性做爱视频.

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 性做爰片免费视频毛片 education 性做爰片免费视频毛片 is changing teaching and learning in 性做爰片免费视频毛片 lessons, to showcasing how 性做爰片免费视频毛片 education 性做爰片免费视频毛片 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 性做爰片免费视频播放 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 性做爰片免费视频毛片 towards other disciplines. For example, to enable teaching about the ethical issues around plagiarism, an art context is used in the 性做爰片免费视频播放 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 性做爰片免费视频播放 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 性做爰片免费视频毛片 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, 性做爰片免费视频毛片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 性做爰片免费视频毛片 we’ve done in this area in this blog post.

The post How can we teach about AI in the arts, humanities and sciences? 性做爰片免费视频毛片 seminar series 2026 appeared first on 性做爱视频.

In this blog, we share the new data paradigms framework that we have developed through 性做爰片免费视频毛片 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

性做爰片免费视频毛片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 性做爰片免费视频毛片 work on AI and data science at the 性做爰片免费视频毛片 Pi 性做爰片免费视频毛片 Education 性做爰片免费视频毛片 Centre, we analysed 84 性做爰片免费视频毛片 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 性做爰片免费视频播放, 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 性做爰片免费视频毛片. 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 性做爰片免费视频毛片
• 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 性做爰片免费视频毛片 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 性做爰片免费视频毛片-led framework for teaching about models in AI and data science appeared first on 性做爱视频.

It’s been over a year since I last wrote an update on this blog about our 性做爰片免费视频毛片 and as we’ve just published our 2025 Annual Report, this is an ideal opportunity to share what we’ve been working on at the 性做爰片免费视频毛片 Pi 性做爰片免费视频毛片 Education 性做爰片免费视频毛片 Centre.

We are a 性做爰片免费视频毛片 centre based in the Department of Computer Science and Technology at the University of Cambridge, with a team that spans the university and the 性做爱视频. We conduct 性做爰片免费视频毛片 into many aspects of the teaching and learning of 性做爰片免费视频毛片 and AI and we work closely with schools, teachers and young people to ensure our 性做爰片免费视频毛片 is applicable to practice.

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

• 性做爰片免费视频毛片 Around the World
• AI education
• Programming education
• Physical 性做爰片免费视频毛片 (EPICS project)
• Teacher action 性做爰片免费视频毛片 (TICE project)

性做爰片免费视频毛片 Around the World

As I’ve written on this blog before, 性做爰片免费视频毛片 education is a global challenge. In one of the 性做爰片免费视频毛片 Centre’s projects, we are looking at how 性做爰片免费视频毛片 education is spreading around the world.

We found that between 2019 and 2024 the number of countries offering 性做爰片免费视频毛片 education had doubled, and that two thirds of all countries now offer, or have concrete plans to offer, 性做爰片免费视频毛片 education. This 性做爰片免费视频毛片 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 性做爰片免费视频播放.

We’re also 性做爰片免费视频毛片ing data science and data ethics education, which are 性做爰片免费视频播放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 性做爰片免费视频播放 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 性做爰片免费视频播放, 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 性做爰片免费视频毛片

This is an area we’ve also done some 性做爰片免费视频毛片 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 性做爰片免费视频毛片 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 性做爰片免费视频毛片 curricula around the world. Teachers and 性做爰片免费视频毛片ers 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 性做爰片免费视频毛片 around programming and debugging focusing on learners who are in higher education, very little 性做爰片免费视频毛片 has been done with school-age students.

In his 性做爰片免费视频毛片, Laurie Gale, a final-year PhD student at the 性做爰片免费视频毛片 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 性做爰片免费视频毛片 Centre website, and also catch up on the 性做爰片免费视频播放 性做爰片免费视频毛片 seminar where he presented his work.

EPICS: Physical 性做爰片免费视频毛片 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 性做爰片免费视频毛片 impacts primary and secondary school learners. We’re investigating the effect of physical 性做爰片免费视频毛片 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 性做爰片免费视频毛片 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 性做爰片免费视频毛片 Network newsletter to be the first to hear about taking part in this survey.

Teacher Inquiry in 性做爰片免费视频毛片 Education (TICE)

As part of our TICE project we support teachers to conduct their own action 性做爰片免费视频毛片 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 性做爰片免费视频播放 topic, a pedagogical approach, or a new resource, or to address a wider issue such as gender diversity or 性做爰直播视频, to inform a change in their practice.

This year 16 teachers published their reports in our Teacher 性做爰片免费视频毛片 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 性做爰片免费视频毛片 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 性做爰片免费视频毛片 Centre newsletter
• Sign up to our Teacher 性做爰片免费视频毛片 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 性做爰片免费视频毛片ers and teachers in Cambridge on 3 and 4 2026 September. More details will follow on the UKICER website and on the 性做爰片免费视频毛片 Centre website in due course.

The post 2025 highlights from the 性做爰片免费视频毛片 Pi 性做爰片免费视频毛片 Education 性做爰片免费视频毛片 Centre appeared first on 性做爱视频.

At our 性做爰片免费视频毛片 education 性做爰片免费视频毛片 seminar in July, a group of 性做爰片免费视频毛片ers from the CAMMP (Computational and Mathematical Modeling Program) 性做爰片免费视频毛片 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)
• 性做爰片免费视频毛片 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 性做爰片免费视频毛片ed 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 性做爰片免费视频播放.

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 性做爰片免费视频毛片ing 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 性做爰片免费视频毛片 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 性做爰片免费视频毛片ing 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 性做爰片免费视频毛片ers who are investigating how to teach about AI in maths lessons. You can find a set of other 性做爰片免费视频毛片 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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