Experience AI is a new educational programme that offers cutting-edge secondary school resources on AI and machine learning for teachers and their students. Developed in partnership by the Raspberry Pi Foundation and DeepMind, the programme aims to support teachers in the exciting and fast-moving area of AI, and get young people passionate about the subject.

The importance of AI and machine learning education

Artificial intelligence and machine learning applications are already changing many aspects of our lives. From search engines, social media content recommenders, self-driving cars, and facial recognition software, to AI chatbots and image generation, these technologies are increasingly common in our everyday world.

Young people who understand how AI works will be better equipped to engage with the changes AI applications bring to the world, to make informed decisions about using and creating AI applications, and to choose what role AI should play in their futures. They will also gain critical thinking skills and awareness of how they might use AI to come up with new, creative solutions to problems they care about.

The AI applications people are building today are predicted to affect many career paths. In 2020, the World Economic Forum estimated that AI would replace some 85 million jobs by 2025 and create 97 million new ones. Many of these future jobs will require some knowledge of AI and ML, so it’s important that young people develop a strong understanding from an early age.

Experience AI Lessons

Something we get asked a lot is: “How do I teach AI and machine learning with my class?”. To answer this question, we have developed a set of free lessons for secondary school students (age 11 to 14) that give you everything you need including lesson plans, slide decks, worksheets, and videos.

The lessons focus on relatable applications of AI and are carefully designed so that teachers in a wide range of subjects can use them. You can find out more about how we used research to shape the lessons and how we aim to avoid misconceptions about AI.

The lessons are also for you if you’re an educator or volunteer outside of a school setting, such as in a coding club.

The six lessons

1. What is AI?: Learners explore the current context of artificial intelligence (AI) and how it is used in the world around them. Looking at the differences between rule-based and data-driven approaches to programming, they consider the benefits and challenges that AI could bring to society. 
2. How computers learn: Learners focus on the role of data-driven models in AI systems. They are introduced to machine learning and find out about three common approaches to creating ML models. Finally the learners explore classification, a specific application of ML.
3. Bias in, bias out: Learners create their own machine learning model to classify images of apples and tomatoes. They discover that a limited dataset is likely to lead to a flawed ML model. Then they explore how bias can appear in a dataset, resulting in biased predictions produced by a ML model.
4. Decision trees: Learners take their first in-depth look at a specific type of machine learning model: decision trees. They see how different training datasets result in the creation of different ML models, experiencing first-hand what the term ‘data-driven’ means. 
5. Solving problems with ML models: Learners are introduced to the AI project lifecycle and use it to create a machine learning model. They apply a human-focused approach to working on their project, train a ML model, and finally test their model to find out its accuracy.
6. Model cards and careers: Learners finish the AI project lifecycle by creating a model card to explain their machine learning model. To finish off the unit, they explore a range of AI-related careers, hear from people working in AI research at DeepMind, and explore how they might apply AI and ML to their interests.

As part of this exciting first phase, we’re inviting teachers to participate in research to help us further develop the resources. All you need to do is sign up through our website, download the lessons, use them in your classroom, and give us your valuable feedback.

Support for teachers

We’ve designed the Experience AI lessons with teacher support in mind, and so that you can deliver them to your learners aged 11 to 14 no matter what your subject area is. Each of the lesson plans includes a section that explains new concepts, and the slide decks feature embedded videos in which DeepMind’s AI researchers describe and bring these concepts to life for your learners.

We will also be offering you a range of new teacher training opportunities later this year, including a free online CPD course — Introduction to AI and Machine Learning — and a series of AI-themed webinars.

Tell us your feedback

We will be inviting schools across the UK to test and improve the Experience AI lessons through feedback. We are really looking forward to working with you to shape the future of AI and machine learning education.

Visit the Experience AI website today to get started.

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It’s been hard to miss the newest generation of AI chatbots that companies have released over the last year. News articles and stories about them seem to be everywhere at the moment. So you may have heard of machine learning (ML) chatbots such as ChatGPT and LaMDA. These chatbots are advanced enough to have caused renewed discussions about the Turing Test and whether the chatbots are sentient.

Chatbots are not sentient

Without any knowledge of how people create such chatbots, it’s easy to imagine how someone might develop an incorrect mental model around these chatbots being living entities. With some awareness of Sci-Fi stories, you might even start to imagine what they could look like or associate a gender with them.

The reality is that these new chatbots are applications based on a large language model (LLM) — a type of machine learning model that has been trained with huge quantities of text, written by people and taken from places such as books and the internet, e.g. social media posts. An LLM predicts the probable order of combinations of words, a bit like the autocomplete function on a smartphone. Based on these probabilities, it can produce text outputs. LLM chatbots run on servers with huge amounts of computing power that people have built in data centres around the world.

Our AI education resources for young people

AI applications are often described as “black boxes” or “closed boxes”: they may be relatively easy to use, but it’s not as easy to understand how they work. We believe that it’s fundamentally important to help everyone, especially young people, to understand the potential of AI technologies and to open these closed boxes to understand how they actually work.

As always, we want to demystify digital technology for young people, to empower them to be thoughtful creators of technology and to make informed choices about how they engage with technology — rather than just being passive consumers.

That’s the goal we have in mind as we’re working on lesson resources to help teachers and other educators introduce KS3 students (ages 11 to 14) to AI and ML. We will release these Experience AI lessons very soon.

Why we avoid describing AI as human-like

Our researchers at the Raspberry Pi Computing Education Research Centre have started investigating the topic of AI and ML, including thinking deeply about how AI and ML applications are described to educators and learners.

To support learners to form accurate mental models of AI and ML, we believe it is important to avoid using words that can lead to learners developing misconceptions around machines being human-like in their abilities. That’s why ‘anthropomorphism’ is a term that comes up regularly in our conversations about the Experience AI lessons we are developing.

To anthropomorphise: “to show or treat an animal, god, or object as if it is human in appearance, character, or behaviour”

https://dictionary.cambridge.org/dictionary/english/anthropomorphize

Anthropomorphising AI in teaching materials might lead to learners believing that there is sentience or intention within AI applications. That misconception would distract learners from the fact that it is people who design AI applications and decide how they are used. It also risks reducing learners’ desire to take an active role in understanding AI applications, and in the design of future applications.

Examples of how anthropomorphism is misleading

Avoiding anthropomorphism helps young people to open the closed box of AI applications. Take the example of a smart speaker. It’s easy to describe a smart speaker’s functionality in anthropomorphic terms such as “it listens” or “it understands”. However, we think it’s more accurate and empowering to explain smart speakers as systems developed by people to process sound and carry out specific tasks. Rather than telling young people that a smart speaker “listens” and “understands”, it’s more accurate to say that the speaker receives input, processes the data, and produces an output. This language helps to distinguish how the device actually works from the illusion of a persona the speaker’s voice might conjure for learners.

Another example is the use of AI in computer vision. ML models can, for example, be trained to identify when there is a dog or a cat in an image. An accurate ML model, on the surface, displays human-like behaviour. However, the model operates very differently to how a human might identify animals in images. Where humans would point to features such as whiskers and ear shapes, ML models process pixels in images to make predictions based on probabilities.

Better ways to describe AI

The Experience AI lesson resources we are developing introduce students to AI applications and teach them about the ML models that are used to power them. We have put a lot of work into thinking about the language we use in the lessons and the impact it might have on the emerging mental models of the young people (and their teachers) who will be engaging with our resources.

It’s not easy to avoid anthropomorphism while talking about AI, especially considering the industry standard language in the area: artificial intelligence, machine learning, computer vision, to name but a few examples. At the Foundation, we are still training ourselves not to anthropomorphise AI, and we take a little bit of pleasure in picking each other up on the odd slip-up.

Here are some suggestions to help you describe AI better:

The purpose of our AI education resources

If we are correct in our approach, then whether or not the young people who engage in Experience AI grow up to become AI developers, we will have helped them to become discerning users of AI technologies and to be more likely to see such products for what they are: data-driven applications and not sentient machines.

If you’d like to get involved with Experience AI and use our lessons with your class, you can start by visiting us at experience-ai.org.

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But how do you actually design lessons about AI, a technical area that is entirely new to young people? That was the question we needed to answer as we started Experience AI, our exciting collaboration with DeepMind, a leading AI company.

Our approach to developing AI education resources

As part of Experience AI, we are creating a free set of lesson resources to help teachers introduce AI and machine learning (ML) to KS3 students (ages 11 to 14). In England this area is not currently part of the national curriculum, but it’s starting to appear in all sorts of learning materials for young people. 

While developing the six Experience AI lessons, we took a research-informed approach. We built on insights from the series of research seminars on AI and data science education we had hosted in 2021 and 2022, and on research we ourselves have been conducting at the Raspberry Pi Computing Education Research Centre.

We reviewed over 500 existing resources that are used to teach AI and ML.

As part of this research, we reviewed over 500 existing resources that are used to teach AI and ML. We found that the vast majority of them were one-off activities, and many claimed to be appropriate for learners of any age. There were very few sets of lessons, or units of work, that were tailored to a specific age group. Activities often had vague learning objectives, or none at all. We rarely found associated assessment activities. These were all shortcomings we wanted to avoid in our set of lessons.

To analyse the content of AI education resources, we use a simple framework called SEAME. This framework is based on work I did in 2018 with Professor Paul Curzon at Queen Mary University of London, running professional development for educators on teaching machine learning.

The SEAME framework gives you a simple way to group learning objectives and resources related to teaching AI and ML, based on whether they focus on social and ethical aspects (SE), applications (A), models (M), or engines (E, i.e. how AI works). We hope that it will be a useful tool for anyone who is interested in looking at resources to teach AI. 

What do AI education resources focus on?

The four levels of the SEAME framework do not indicate a hierarchy or sequence. Instead, they offer a way for teachers, resource developers, and researchers to talk about the focus of AI learning activities.

Social and ethical aspects (SE)

The SE level covers activities that relate to the impact of AI on everyday life, and to its implications for society. Learning objectives and their related resources categorised at this level introduce students to issues such as privacy or bias concerns, the impact of AI on employment, misinformation, and the potential benefits of AI applications.

Applications (A)

The A level refers to activities related to applications and systems that use AI or ML models. At this level, learners do not learn how to train models themselves, or how such models work. Learning objectives at this level include knowing a range of AI applications and starting to understand the difference between rule-based and data-driven approaches to developing applications.

Models (M)

The M level concerns the models underlying AI and ML applications. Learning objectives at this level include learners understanding the processes used to train and test models. For example, through resources focused on the M level, students could learn about the different learning paradigms of ML (i.e., supervised, unsupervised, or reinforcement learning).

Engines (E)

The E level is related to the engines that make AI models work. This is the most hidden and complex level, and for school-aged learners may need to be taught using unplugged activities and visualisations. Learning objectives could include understanding the basic workings of systems such as data-driven decision trees and artificial neural networks.

Covering the four levels

Some learning activities may focus on a single level, but activities can also span more than one level. For example, an activity may start with learners trying out an existing ‘rock-paper-scissors’ application that uses an ML model to recognise hand shapes. This would cover the applications level. If learners then move on to train the model to improve its accuracy by adding more image data, they work at the models level.

Other activities cover several SEAME levels to address a specific concept. For example, an activity focussed on bias might start with an example of the societal impact of bias (SE level). Learners could then discuss the AI applications they use and reflect on how bias impacts them personally (A level). The activity could finish with learners exploring related data in a simple ML model and thinking about how representative the data is of all potential application users (M level).

The set of lessons on AI we are developing in collaboration with DeepMind covers all four levels of SEAME.

The set of Experience AI lessons we are developing in collaboration with DeepMind covers all four levels of SEAME. The lessons are based on carefully designed learning objectives and specifically targeted to KS3 students. Lesson materials include presentations, videos, student activities, and assessment questions.

The SEAME framework as a tool for research on AI education

For researchers, we think the SEAME framework will, for example, be useful to analyse school curriculum material to see whether some age groups have more learning activities available at one level than another, and whether this changes over time. We may find that primary school learners work mostly at the SE and A levels, and secondary school learners move between the levels with increasing clarity as they develop their knowledge. It may also be the case that some learners or teachers prefer activities focused on one level rather than another. However, we can’t be sure: research is needed to investigate the teaching and learning of AI and ML across all year groups.

That’s why we’re excited to welcome Salomey Afua Addo to the Raspberry Pi Computing Education Research Centre. Salomey joined the Centre as a PhD student in January, and her research will focus on approaches to the teaching and learning of AI. We’re looking forward to seeing the results of her work.

If you’d like to get involved with Experience AI as an educator and use our lessons with your class, you can start by visiting us at experience-ai.org.

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Whether you’re a teacher, a mentor, or a young person, if it’s computer science, coding, or digital skills that you’re looking for, we’ve got you covered. 

Your code in space 

Through our collaboration with the European Space Agency, Astro Pi, young people can write computer programs that are guaranteed to run on the Raspberry Pi computers on the International Space Station (terms and conditions apply).

Astro Pi Mission Zero is open to participants until 17 March 2023 and is a perfect introduction to programming in Python for beginners. It takes about an hour to complete and we provide step-by-step guides for teachers, mentors, and young people. 

Make a cool project and share it with the world 

Kids all over the world are already working on their entries to Coolest Projects Global 2023, our international online showcase that will see thousands of young people share their brilliant tech creations with the world. Registration opens on 6 February and it’s super simple to get involved. If you’re looking for inspiration, why not explore the judges’ favourite projects from 2022?

While we all love the Coolest Projects online showcase, I’m also looking forward to attending more in-person Coolest Projects events in 2023. The word on the street is that members of the Raspberry Pi team have been spotted scouting venues in Ireland… Watch this space. 

Experience AI 

I am sure I wasn’t alone in disappearing down a ChatGPT rabbit hole at the end of last year after OpenAI made their latest AI chatbot available for free. The internet exploded with both incredible examples of what the chatbot can do and furious debates about the limitations and ethics of AI systems.

With the rapid advances being made in AI technology, it’s increasingly important that young people are able to understand how AI is affecting their lives now and the role that it can play in their future. This year we’ll be building on our research into the future of AI and data science education and launching Experience AI in partnership with leading AI company DeepMind. The first wave of resources and learning experiences will be available in March. 

The big Code Club and CoderDojo meetup

With pandemic restrictions now almost completely unwound, we’ve seen a huge resurgence in Code Clubs and CoderDojos meeting all over the world. To build on this momentum, we are delighted to be welcoming Code Club and CoderDojo mentors and educators to a big Clubs Conference in Churchill College in Cambridge on 24 and 25 March.

This will be the first time we’re holding a community get-together since 2019 and a great opportunity to share learning and make new connections. 

Building partnerships in India, Kenya, and South Africa 

As part of our global mission to ensure that every young person is able to learn how to create with digital technologies, we have been focused on building partnerships in India, Kenya, and South Africa, and that work will be expanding in 2023.

In India we will significantly scale up our work with established partners Mo School and Pratham Education Foundation, training 2000 more teachers in government schools in Odisha, and running 2200 Code Clubs across four states. We will also be launching new partnerships with community-based organisations in Kenya and South Africa, helping them set up networks of Code Clubs and co-designing learning experiences that help them bring computing education to their communities of young people. 

Exploring computing education for 5- to 11-year-olds 

Over the past few years, our research seminar series has covered computing education topics from diversity and inclusion, to AI and data science. This year, we’re focusing on current questions and research in primary computing education for 5- to 11-year-olds.

As ever, we’re providing a platform for some of the world’s leading researchers to share their insights, and convening a community of educators, researchers, and policy makers to engage in the discussion. The first seminar takes place today (Tuesday 10 January) and it’s not too late to sign up.

And much, much more… 

That’s just a few of the super cool things that we’ve got planned for 2023. I haven’t even mentioned the new online projects we’re developing with our friends at Unity, the fun we’ve got planned with our very own online text editor, or what’s next for our curriculum and professional development offer for computing teachers.

You can sign up to our monthly newsletter to always stay up to date with what we’re working on.

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The Raspberry Pi Foundation’s mission is to enable young people to realise their full potential through the power of computing and digital technologies. Our vision is that every young person — whatever their background — should have the opportunity to learn how to create and solve problems with computers.

With the rapid advances in artificial intelligence — from machine learning and robotics, to computer vision and natural language processing — it’s increasingly important that young people understand how AI is affecting their lives now and the role that it can play in their future. 

Experience AI is a new collaboration between the Raspberry Pi Foundation and DeepMind that aims to help young people understand how AI works and how it is changing the world. We want to inspire young people about the careers in AI and help them understand how to access those opportunities, including through their subject choices. 

Experience AI 

More than anything, we want to make AI relevant and accessible to young people from all backgrounds, and to make sure that we engage young people from backgrounds that are underrepresented in AI careers. 

The collaboration has two strands: Inspire and Experiment. 

Inspire: To engage and inspire students about AI and its impact on the world, we are developing a set of free learning resources and materials including lesson plans, assembly packs, videos, and webinars, alongside training and support for educators. This will include an introduction to the technologies that enable AI; how AI models are trained; how to frame problems for AI to solve; the societal and ethical implications of AI; and career opportunities. All of this will be designed around real-world and relatable applications of AI, engaging a wide range of diverse interests and useful to teachers from different subjects.

Experiment: Building on the excitement generated through Inspire, we are also designing an AI challenge that will support young people to experiment with AI technologies and explore how these can be used to solve real-world problems. This will provide an opportunity for students to get hands-on with technology and data, along with support for educators. 

Our initial focus is learners aged 11 to 14 in the UK. We are working with teachers, students, and DeepMind engineers to ensure that the materials and learning experiences are engaging and accessible to all, and that they reflect the latest AI technologies and their application.

As with all of our work, we want to be research-led and the Raspberry Pi Foundation research team has been working over the past year to understand the latest research on what works in AI education.

Next steps 

Development of the learning materials is underway now, and we have released the full set of resources on experience-ai.org. We are currently piloting the challenge for release in September 2023.

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