An AI literacy framework

From her research, Shuchi has developed a framework for teaching about AI that is structured as four interlocking components, each representing a key area of understanding:

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

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

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

Computational thinking and AI

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

Example activities from research studies

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

Shuchi shared examples of activities from two research projects that she has led:

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

Closing thoughts

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

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

Join our next seminar

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

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

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

The post Teaching about AI in K–12 education: Thoughts from the USA appeared first on Raspberry Pi Foundation.

Whether you’re from the UK or not, it’s a good read, setting out the opportunities and challenges facing any country that aspires to lead the world in the development and application of AI technologies. 

In terms of skills, the Action Plan highlights the need for the UK to train tens of thousands more AI professionals by 2030 and sets out important goals to expand education pathways into AI, invest in new undergraduate and master’s scholarships, tackle the lack of diversity in the sector, and ensure that the lifelong skills agenda focuses on AI skills. 

This is all very important, but the Action Plan fails to mention what I think is one of the most important investments we need to make, which is in schools. 

“Most people overestimate what they can achieve in a year and underestimate what they can achieve in ten years.”

While reading the section of the Action Plan that dealt with AI skills, I was reminded of this quote attributed to Bill Gates, which was adapted from Roy Amara’s law of technology. We tend to overestimate what we can achieve in the short term and underestimate what we can achieve in the long term. 

In focusing on the immediate AI gold rush, there is a risk that the government overlooks the investments we need to make right now in schools, which will yield huge returns — for individuals, communities, and economies — over the long term. Realising the full potential of a future where AI technologies are ubiquitous requires genuinely long-term thinking, which isn’t always easy for political systems that are designed around short-term results. 

But what are those investments? The Action Plan rightly points out that the first step for the government is to accurately assess the size of the skills gap. As part of that work, we need to figure out what needs to change in the school system to build a genuinely diverse and broad pipeline of young people with AI skills. The good news is that we’ve already made a lot of progress. 

AI literacy

Over the past three years, the Raspberry Pi Foundation and our colleagues in the Raspberry Pi Computing Education Research Centre at the University of Cambridge have been working to understand and define what AI literacy means. That led us to create a research-informed model for AI literacy that unpacks the concepts and knowledge that constitute a foundational understanding of AI. 

In partnership with one of the leading UK-based AI companies, Google DeepMind, we used that model to create Experience AI. This suite of classroom resources, teacher professional development, and hands-on practical activities enables non-specialist teachers to deliver engaging lessons that help young people build that foundational understanding of AI technologies. 

We’ve seen huge demand from UK schools already, with thousands of lessons taught in UK schools, and we’re delighted to be working with Parent Zone to support a wider roll out in the UK, along with free teacher professional development.  

With the generous support of Google.org, we are working with a global network of education partners — from Nigeria to Nepal — to localise and translate these resources, and deliver locally organised teacher professional development. With over 1 million young people reached already, Experience AI can plausibly claim to be the most widely used AI literacy curriculum in the world, and we’re improving it all the time. 

All of the materials are available for anyone to use and can be found on the Experience AI website.

There is no AI without CS

With the CEO of GitHub claiming that it won’t be long before 80% of code is written by AI, it’s perhaps not surprising that some people are questioning whether we still need to teach kids how to code.

I’ll have much more to say on this in a future blog post, but the short answer is that computer science and programming is set to become more — not less — important in the age of AI. This is particularly important if we want to tackle the lack of diversity in the tech sector and ensure that young people from all backgrounds have the opportunity to shape the AI-enabled future that they will be living in. 

The simple truth is that there is no artificial intelligence without computer science. The rapid advances in AI are likely to increase the range of problems that can be solved by technology, creating demand for more complex software, which in turn will create demand for more programmers with increasingly sophisticated and complex skills. 

That’s why we’ve set ourselves the ambition that we will inspire 10 million more young people to learn how to get creative with technology over the next 10 years through Code Club. 

Curriculum reform 

But we also need to think about what needs to change in the curriculum to ensure that schools are equipping young people with the skills and knowledge they need to thrive in an AI-powered world. 

That will mean changes to the computer science curriculum, providing different pathways that reflect young people’s interests and passions, but ensuring that every child leaves school with a qualification in computer science or applied digital skills. 

It’s not just computer science courses. We need to modernise mathematics and figure out what a data science curriculum looks like (and where it fits). We also need to recognise that AI skills are just as relevant to biology, geography, and languages as they are to computer science. 

To be clear, I am not talking about how AI technologies will save teachers time, transform assessments, or be used by students to write essays. I am talking about the fundamentals of the subjects themselves and how AI technologies are revolutionising the sciences and humanities in practice in the real world. 

These are all areas where the Raspberry Pi Foundation is engaged in original research and experimentation. Stay tuned. 

Supporting teachers

All of this needs to be underpinned by a commitment to supporting teachers, including through funding and time to engage in meaningful professional development. This is probably the biggest challenge for policy makers at a time when budgets are under so much pressure. 

For any nation to plausibly claim that it has an Action Plan to be an AI superpower, it needs to recognise the importance of making the long-term investment in supporting our teachers to develop the skills and confidence to teach students about AI and the role that it will play in their lives. 

I’d love to hear what you think and if you want to get involved, please get in touch.

The post The need to invest in AI skills in schools appeared first on Raspberry Pi Foundation.

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

As AI and data science are incorporated into school curricula and teaching and learning materials worldwide, we ask: What’s the research basis for these curricula, pedagogy, and resource choices?

In 2024, we showcased researchers who are investigating how AI can be leveraged to support the teaching and learning of programming. But in 2025, we look at what should be taught about AI, ML, and data science in schools and how we should teach this. 

Our 2025 seminar speakers — so far!

We are very excited that we have already secured several key researchers in the field. 

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

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

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

Our April seminar will see David Weintrop from the University of Maryland introduce, with his colleagues, a data science curriculum called API Can Code, aimed at high-school students. The group will highlight the strategies needed for integrating data science learning within students’ lived experiences and fostering authentic engagement.

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

What to expect from the seminars

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

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

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

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

How to sign up

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

We hope to see you at a seminar soon!

The post How can we teach students about AI and data science? Join our 2025 seminar series to learn more about the topic appeared first on Raspberry Pi Foundation.

In the fifth seminar of this series, we heard from Rose Luckin, Professor of Learner Centred Design at the University College London (UCL) Knowledge Lab. Rose is Founder of EDUCATE Ventures Research Ltd., a London consultancy service working with start-ups, researchers, and educators to develop evidence-based educational technology.

Based on her experience at EDUCATE, Rose spoke about how AI-based analysis could help educators gain a deeper understanding of their students, and how educators could work with AI systems to provide better learning resources to their students. This provided us with a different angle to the first four seminars in our current series, where we’ve been thinking about how young people learn to understand AI systems.

Education and AI systems

AI systems have the potential to impact education in a number of different ways, which Rose distilled into three areas: 

1. Using AI in education to tackle some of the big educational challenges
2. Educating teachers about AI so that they can use it safely and effectively 
3. Changing education so that we focus on human intelligence and prepare people for an AI world

It is clear that the three areas are interconnected, meaning developments in one area will affect the others. Rose’s focus during the seminar was the second area: educating people about AI.

What can AI systems do in education? 

Through giving examples of existing AI-based systems used for education, Rose described what in particular it is about AI systems that can be useful in an education setting. The first point she raised was that AI systems can adapt based on learning from data. Her main example was the AI-based platform ENSKILLS, which detects the user’s level of competency with spoken English through the user’s interactions with a virtual character, and gradually adapts the character to the user’s level. Other examples of adaptive AI systems for education include Carnegie Learning and Century Intelligent Learning.

We know that AI systems can respond to different forms of data. Rose introduced the example of OyaLabs to demonstrate how AI systems can gather and process real-time sensory data. This is an app that parents can use in a young child’s room to monitor the child’s interactions with others. The app analyses the data it gathers and produces advice for parents on how they can support their child’s language development.

AI system creators can also combine adaptivity and real-time sensory data processing  in their systems. One example Rosa gave of this was SimSensei from the University of Southern California. This is a simulated coach, which a student can interact with and which gathers real-time data about how the student is speaking, including their tone, speed of speech, and facial expressions. The system adapts its coaching advice based on these interactions and on what it learns from interactions with other students.

Getting ready for AI systems in education

For the remainder of her presentation, Rose focused on the framework she is involved in developing, as part of the EDUCATE service, to support organisations to prepare for implementing AI systems, including educators within these organisations. The aim of this ETHICAI framework is to enable organisations and educators to understand:

• What AI systems are capable of doing
• The strengths and weaknesses of AI systems
• How data is used by AI systems to learn

Rose described the seven steps of the framework as:

1. Educate, enthuse, excite – about building an AI mindset within your community 
2. Tailor and Hone – the particular challenges you want to focus on
3. Identify – identify (wisely), collate and …
4. Collect – new data relevant to your focus
5. Apply – AI techniques to the relevant data you have brought together
6. Learn – understand what the data is telling you about your focus and return to step 5 until you are AI ready
7. Iterate

She then went on to demonstrate how the framework is applied using the example of online teaching. Online teaching has been a key part of education throughout the coronavirus pandemic; AI systems could be used to analyse datasets generated during online teaching sessions, in order to make decisions for and recommendations to educators.

The first step of the ETHICAI framework is educate, enthuse, excite. In Rose’s example, this step consisted of choosing online teaching as a scenario, because it is very pertinent to a teacher’s practice. The second step is to tailor and hone in on particular challenges that are to be the focus, capitalising on what AI systems can do. In Rose’s example, the challenge is assessing the quality of online lessons in a way that would be useful to educators. The third step of the framework is to identify what data is required to perform this quality assessment.

The fourth step is the collection of new data relevant to the focus of the project. The aim is to gain an increased understanding of what happens in online learning across thousands of schools. Walking through the online learning example, Rose suggested we might be able to collect the following types of data:

• Log data
• Audio data
• Performance data
• Video data, which includes eye-movement data
• Historical data from tests and interviews
• Behavioural data from surveying teachers and parents about how they felt about online learning

It is important to consider the ethical implications of gathering all this data about students, something that was a recurrent theme in both Rose’s presentation and the Q&A at the end.

Step five of the ETHICAI framework focuses on applying AI techniques to the relevant data to combine and process it. The figure below shows that in preparation, the various data sets need to be collated, cleaned, organised, and transformed.

From the correctly prepared data, interaction profiles can be produced in order to put characteristics from different lessons into groups/profiles. Rose described how cluster analysis using a combination of both AI and human intelligence could be used to sort lessons into groups based on common features.

The sixth step in Rose’s example focused on what may be learned from analysing collected data linked to the particular challenge of online teaching and learning. Rose said that applying an AI system to students’ behavioural data could, for example, give indications about students’ focus and confidence, and make or recommend interventions to educators accordingly.

Where might we take applications of AI systems in education in the future?

Rose described that AI systems can possess some types of intelligence humans have or can develop: interdisciplinary academic intelligence, meta-knowing intelligence, and potentially social intelligence. However, there are types such as meta-contextual intelligence and perceived self-efficacy that AI systems are not able to demonstrate in the way humans can.

The use of AI systems in education can cause ethical issues. As an example, Rose pointed out the use of virtual glasses to identify when students need help, even if they do not realise it themselves. A system like this could help educators with assessing who in their class needs more help, and could link this back to student performance. However, using such a system like this has obvious ethical implications, and some of these were the focus of the Q&A that followed Rose’s presentation.

It’s clear that, in the education domain as in all other domains, both positive and negative outcomes of integrating AI are possible. In a recent paper written by Wayne Holmes (also from the UCL Knowledge Lab) and co-authors, ‘Ethics of AI in Education: Towards a Community Wide Framework’ [1], the authors suggest that the interpretation of data, consent and privacy, data management, surveillance, and power relations are all ethical issues that should be taken into consideration. Finding consensus for a practical ethical framework or set of principles, with all stakeholders, at the very start of an AI-related project is the only way to ensure ethics are built into the project and the AI system itself from the ground up.

Ethical issues of AI systems more broadly, and how to involve young people in discussions of AI ethics, were the focus of our seminar with Dr Mhairi Aitken back in September. You can revisit the seminar recording, presentation slides, and summary blog post.

I really enjoyed both the focus and content of Rose’s talk: educators understanding how AI systems may be applied to education in order to help them make more informed decisions about how to best support their students. This is an important factor to consider in the context of the bigger picture of what young people should be learning about AI. The work that Rose and her colleagues are doing also makes an important contribution to translating research into practical models that teachers can use.

Join our next free seminars

You may still have time to sign up for our Tuesday 11 January seminar, today at 17:00–18:30 GMT, where we will welcome Dave Touretzky and Fred Martin, founders of the influential AI4K12 framework, which identifies the five big ideas of AI and how they can be integrated into education.

Next month, on 1 February at 17:00–18:30 GMT, Tara Chklovski (CEO of Technovation) will give a presentation called Teaching youth to use AI to tackle the Sustainable Development Goals at our seminar series.

If you want to join any of our seminars, click the button below to sign up and we will send you information on how to join. We look forward to seeing you there!

You’ll always find our schedule of upcoming seminars on this page. For previous seminars, you can visit our past seminars and recordings page.

The post How can AI-based analysis help educators support students? appeared first on Raspberry Pi Foundation.