Tag Archives: Experience AI

Localising AI education: Adapting Experience AI for global impact

Post Syndicated from Ben Garside original https://www.raspberrypi.org/blog/localising-ai-education-adapting-experience-ai-resources/

It’s been almost a year since we launched our first set of Experience AI resources in the UK, and we’re now working with partner organisations to bring AI literacy to teachers and students all over the world.

Developed by the Raspberry Pi Foundation and Google DeepMind, Experience AI provides everything that teachers need to confidently deliver engaging lessons that will inspire and educate young people about AI and the role that it could play in their lives.

Over the past six months we have been working with partners in Canada, Kenya, Malaysia, and Romania to create bespoke localised versions of the Experience AI resources. Here is what we’ve learned in the process.

Creating culturally relevant resources

The Experience AI Lessons address a variety of real-world contexts to support the concepts being taught. Including real-world contexts in teaching is a pedagogical strategy we at the Raspberry Pi Foundation call “making concrete”. This strategy significantly enhances the learning experience for learners because it bridges the gap between theoretical knowledge and practical application. 

Three learners and an educator do a physical computing activity.

The initial aim of Experience AI was for the resources to be used in UK schools. While we put particular emphasis on using culturally relevant pedagogy to make the resources relatable to learners from backgrounds that are underrepresented in the tech industry, the contexts we included in them were for UK learners. As many of the resource writers and contributors were also based in the UK, we also unavoidably brought our own lived experiences and unintentional biases to our design thinking.

Therefore, when we began thinking about how to adapt the resources for schools in other countries, we knew we needed to make sure that we didn’t just convert what we had created into different languages. Instead we focused on localisation.

Educators doing an activity about networks using a piece of string.

Localisation goes beyond translating resources into a different language. For example in educational resources, the real-world contexts used to make concrete the concepts being taught need to be culturally relevant, accessible, and engaging for students in a specific place. In properly localised resources, these contexts have been adapted to provide educators with a more relatable and effective learning experience that resonates with the students’ everyday lives and cultural background.

Working with partners on localisation

Recognising our UK-focused design process, we made sure that we made no assumptions during localisation. We worked with partner organisations in the four countries — Digital Moment, Tech Kidz Africa, Penang Science Cluster, and Asociația Techsoup — drawing on their expertise regarding their educational context and the real-world examples that would resonate with young people in their countries.

Participants on a video call.
A video call with educators in Kenya.

We asked our partners to look through each of the Experience AI resources and point out the things that they thought needed to change. We then worked with them to find alternative contexts that would resonate with their students, whilst ensuring the resources’ intended learning objectives would still be met.

Spotlight on localisation for Kenya

Tech Kidz Africa, our partner in Kenya, challenged some of the assumptions we had made when writing the original resources.

An Experience AI lesson plan in English and Swahili.
An Experience AI resource in English and Swahili.

Relevant applications of AI technology

Tech Kidz Africa wanted the contexts in the lessons to not just be relatable to their students, but also to demonstrate real-world uses of AI applications that could make a difference in learners’ communities. They highlighted that as agriculture is the largest contributor to the Kenyan economy, there was an opportunity to use this as a key theme for making the Experience AI lessons more culturally relevant. 

This conversation with Tech Kidz Africa led us to identify a real-world use case where farmers in Kenya were using an AI application that identifies disease in crops and provides advice on which pesticides to use. This helped the farmers to increase their crop yields.

Training an AI model to classify healthy and unhealthy cassava plant photos.
Training an AI model to classify healthy and unhealthy cassava plant photos.

We included this example when we adapted an activity where students explore the use of AI for “computer vision”. A Google DeepMind research engineer, who is one of the General Chairs of the Deep Learning Indaba, recommended a data set of images of healthy and diseased cassava crops (1). We were therefore able to include an activity where students build their own machine learning models to solve this real-world problem for themselves.

Access to technology

While designing the original set of Experience AI resources, we made the assumption that the vast majority of students in UK classrooms have access to computers connected to the internet. This is not the case in Kenya; neither is it the case in many other countries across the world. Therefore, while we localised the Experience AI resources with our Kenyan partner, we made sure that the resources allow students to achieve the same learning outcomes whether or not they have access to internet-connected computers.

An AI classroom discussion activity.
An Experience AI activity related to farming.

Assuming teachers in Kenya are able to download files in advance of lessons, we added “unplugged” options to activities where needed, as well as videos that can be played offline instead of being streamed on an internet-connected device.

What we’ve learned

The work with our first four Experience AI partners has given us with lots of localisation learnings, which we will use as we continue to expand the programme with more partners across the globe:

  • Cultural specificity: We gained insight into which contexts are not appropriate for non-UK schools, and which contexts all our partners found relevant. 
  • Importance of local experts: We know we need to make sure we involve not just people who live in a country, but people who have a wealth of experience of working with learners and understand what is relevant to them. 
  • Adaptation vs standardisation: We have learned about the balance between adapting resources and maintaining the same progression of learning across the Experience AI resources. 

Throughout this process we have also reflected on the design principles for our resources and the choices we can make while we create more Experience AI materials in order to make them more amenable to localisation. 

Join us as an Experience AI partner

We are very grateful to our partners for collaborating with us to localise the Experience AI resources. Thank you to Digital Moment, Tech Kidz Africa, Penang Science Cluster, and Asociația Techsoup.

We now have the tools to create resources that support a truly global community to access Experience AI in a way that resonates with them. If you’re interested in joining us as a partner, you can register your interest here.

(1) The cassava data set was published open source by Ernest Mwebaze, Timnit Gebru, Andrea Frome, Solomon Nsumba, and Jeremy Tusubira. Read their research paper about it here.

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The Experience AI Challenge: Find out all you need to know

Post Syndicated from Liz Eaton original https://www.raspberrypi.org/blog/the-experience-ai-challenge-find-out-all-you-need-to-know/

We’re really excited to see that Experience AI Challenge mentors are starting to submit AI projects created by young people. There’s still time for you to get involved in the Challenge: the submission deadline is 24 May 2024. 

The Experience AI Challenge banner.

If you want to find out more about the Challenge, join our live webinar on Wednesday 3 April at 15:30 BST on our YouTube channel.

During the webinar, you’ll have the chance to:

  • Ask your questions live. Get any Challenge-related queries answered by us in real time. Whether you need clarification on any part of the Challenge or just want advice on your young people’s project(s), this is your chance to ask.
  • Get introduced to the submission process. Understand the steps of submitting projects to the Challenge. We’ll walk you through the requirements and offer tips for making your young people’s submission stand out.
  • Learn more about our project feedback. Find out how we will deliver our personalised feedback on submitted projects (UK only).
  • Find out how we will recognise your creators’ achievements. Learn more about our showcase event taking place in July, and the certificates and posters we’re creating for you and your young people to celebrate submitting your projects.

Subscribe to our YouTube channel and press the ‘Notify me’ button to receive a notification when we go live. 

Why take part? 

The Experience AI Challenge, created by the Raspberry Pi Foundation in collaboration with Google DeepMind, guides young people under the age of 18, and their mentors, through the exciting process of creating their own unique artificial intelligence (AI) project. Participation is completely free.

Central to the Challenge is the concept of project-based learning, a hands-on approach that gets learners working together, thinking critically, and engaging deeply with the materials. 

A teacher and three students in a classroom. The teacher is pointing at a computer screen.

In the Challenge, young people are encouraged to seek out real-world problems and create possible AI-based solutions. By taking part, they become problem solvers, thinkers, and innovators. 

And to every young person based in the UK who creates a project for the Challenge, we will provide personalised feedback and a certificate of achievement, in recognition of their hard work and creativity. Any projects considered as outstanding by our experts will be selected as favourites and its creators will be invited to a showcase event in the summer. 

Resources ready for your classroom or club

You don’t need to be an AI expert to bring this Challenge to life in your classroom or coding club. Whether you’re introducing AI for the first time or looking to deepen your young people’s knowledge, the Challenge’s step-by-step resource pack covers all you and your young people need, from the basics of AI, to training a machine learning model, to creating a project in Scratch.  

In the resource pack, you will find:

  • The mentor guide contains all you need to set up and run the Challenge with your young people 
  • The creator guide supports young people throughout the Challenge and contains talking points to help with planning and designing projects 
  • The blueprint workbook helps creators keep track of their inspiration, ideas, and plans during the Challenge 

The pack offers a safety net of scaffolding, support, and troubleshooting advice. 

Find out more about the Experience AI Challenge

By bringing the Experience AI Challenge to young people, you’re inspiring the next generation of innovators, thinkers, and creators. The Challenge encourages young people to look beyond the code, to the impact of their creations, and to the possibilities of the future.

You can find out more about the Experience AI Challenge, and download the resource pack, from the Experience AI website.

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Experience AI: Making AI relevant and accessible

Post Syndicated from Jan Ander original https://www.raspberrypi.org/blog/experience-ai-equal-access-ai-education/

Google DeepMind’s Aimee Welch discusses our partnership on the Experience AI learning programme and why equal access to AI education is key. This article also appears in issue 22 of Hello World on teaching and AI.

From AI chatbots to self-driving cars, artificial intelligence (AI) is here and rapidly transforming our world. It holds the potential to solve some of the biggest challenges humanity faces today — but it also has many serious risks and inherent challenges, like reinforcing existing patterns of bias or “hallucinating”, a term that describes AI making up false outputs that do not reflect real events or data.

A teenager learning computer science.
Young people need the knowledge and skills to navigate and shape AI.

Teachers want to build young people’s AI literacy

As AI becomes an integral part of our daily lives, it’s essential that younger generations gain the knowledge and skills to navigate and shape this technology. Young people who have a foundational understanding of AI are able to make more informed decisions about using AI applications in their daily lives, helping ensure safe and responsible use of the technology. This has been recognised for example by the UK government’s AI Council, whose AI Roadmap sets out the goal of ensuring that every child in the UK leaves school with a basic sense of how AI works.

Learner in a computing classroom.
Every young person should have access to learning AI literacy.

But while AI literacy is a key skill in this new era, not every young person currently has access to sufficient AI education and resources. In a recent survey by the EdWeek Research Center in the USA, only one in 10 teachers said they knew enough about AI to teach its basics, and very few reported receiving any professional development related to the topic. Similarly, our work with the Raspberry Pi Computing Education Research Centre has suggested that UK-based teachers are eager to understand more about AI and how to engage their students in the topic.

Bringing AI education into classrooms

Ensuring broad access to AI education is also important to improve diversity in the field of AI to ensure safe and responsible development of the technology. There are currently stark disparities in the field and these start already early on, with school-level barriers contributing to underrepresentation of certain groups of people. By increasing diversity in AI, we bring diverse values, hopes, and concerns into the design and deployment of the technology — something that’s critical for AI to benefit everyone.

Kenyan children work on a physical computing project.
Bringing diverse values into AI is critical.

By focusing on AI education from a young age, there is an opportunity to break down some of these long-standing barriers. That’s why we partnered with the Raspberry Pi Foundation to co-create Experience AI, a new learning programme with free lesson plans, slide decks, worksheets and videos, to address gaps in AI education and support teachers in engaging and inspiring young people in the subject.

The programme aims to help young people aged 11–14 take their first steps in understanding the technology, making it relevant to diverse learners, and encouraging future careers in the field. All Experience AI resources are freely available to every school across the UK and beyond.

A woman teacher helps a young person with a coding project.
The Experience AI resources are free for every school.

The partnership is built on a shared vision to make AI education more inclusive and accessible. Bringing together the Foundation’s expertise in computing education and our cutting-edge technical knowledge and industry insights has allowed us to create a holistic learning experience that connects theoretical concepts and practical applications.

Experience AI: Informed by AI experts

A group of 15 research scientists and engineers at Google DeepMind contributed to the development of the lessons. From drafting definitions for key concepts, to brainstorming interesting research areas to highlight, and even featuring in the videos included in the lessons, the group played a key role in shaping the programme in close collaboration with the Foundation’s educators and education researchers.

Interview for Experience AI at Google DeepMind.
Interviews with AI scientists and engineers at Google DeepMind are part of Experience AI.

To bring AI concepts to life, the lessons include interactive activities as well as real-life examples, such as a project where Google DeepMind collaborated with ecologists and conservationists to develop machine learning methods to study the behaviour of an entire animal community in the Serengeti National Park and Grumeti Reserve in Tanzania.

Elephants in the Serengeti.
One of the Experience AI lessons focuses on an AI-enabled research project in the Serengeti.

Member of the working group, Google DeepMind Research Scientist Petar Veličković, shares: “AI is a technology that is going to impact us all, and therefore educating young people on how to interact with this technology is likely going to be a core part of school education going forward. The project was eye-opening and humbling for me, as I learned of the challenges associated with making such a complex topic accessible — not only to every pupil, but also to every teacher! Observing the thoughtful approach undertaken by the Raspberry Pi Foundation left me deeply impressed, and I’m taking home many useful ideas that I hope to incorporate in my own AI teaching efforts going forward.”

The lessons have been carefully developed to:

  • Follow a clear learning journey, underpinned by the SEAME framework which guides learners sequentially through key concepts and acts as a progression framework.
  • Build foundational knowledge and provide support for teachers. Focus on teacher training and support is at the core of the programme.
  • Embed ethics and responsibility. Crucially, key concepts in AI ethics and responsibility are woven into each lesson and progressively built on. Students are introduced to concepts like data bias, user-focused approaches, model cards, and how AI can be used for social good. 
  • Ensure cultural relevance and inclusion. Experience AI was designed with diverse learners in mind and includes a variety of activities to enable young people to pick topics that most interest them. 

What teachers say about the Experience AI lessons

To date, we estimate the resources have reached 200,000+ students in the UK and beyond. We’re thrilled to hear from teachers already using the resources about the impact they are having in the classroom, such as Mrs J Green from Waldegrave School in London, who says: “I thought that the lessons covered a really important topic. Giving the pupils an understanding of what AI is and how it works will become increasingly important as it becomes more ubiquitous in all areas of society. The lessons that we trialled took some of the ‘magic’ out of AI and started to give the students an understanding that AI is only as good as the data that is used to build it. It also started some really interesting discussions with the students around areas such as bias.”

An educator points to an image on a student's computer screen.
Experience AI offers support for teachers.

At North Liverpool Academy, teacher Dave Cross tells us: “AI is such a current and relevant topic in society that [these lessons] will enable Key Stage 3 computing students [ages 11–14] to gain a solid foundation in something that will become more prevalent within the curriculum, and wider subjects too as more sectors adopt AI and machine learning as standard. Our Key Stage 3 computing students now feel immensely more knowledgeable about the importance and place that AI has in their wider lives. These lessons and activities are engaging and accessible to students and educators alike, whatever their specialism may be.”

A stronger global AI community

Our hope is that the Experience AI programme instils confidence in both teachers and students, helping to address some of the critical school-level barriers leading to underrepresentation in AI and playing a role in building a stronger, more inclusive AI community where everyone can participate irrespective of their background. 

Children in a Code Club in India.

Today’s young people are tomorrow’s leaders — and as such, educating and inspiring them about AI is valuable for everybody.

Teachers can visit experience-ai.org to download all Experience AI resources for free.

We are now building a network of educational organisations around the world to tailor and translate the Experience AI resources so that more teachers and students can engage with them and learn key AI literacy skills. Find out more.

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AI literacy for teachers and students all over the world

Post Syndicated from Philip Colligan original https://www.raspberrypi.org/blog/experience-ai-canada-kenya-romania/

I am delighted to announce that the Raspberry Pi Foundation and Google DeepMind are building a global network of educational organisations to bring AI literacy to teachers and students all over the world, starting with Canada, Kenya, and Romania.

Learners in a classroom in Kenya.
Learners around the world will gain AI literacy skills through Experience AI.

Experience AI 

We launched Experience AI in September 2022 to help teachers and students learn about AI technologies and how they are changing the world. 

Developed by the Raspberry Pi Foundation and Google DeepMind, Experience AI provides everything that teachers need to confidently deliver engaging lessons that will inspire and educate young people about AI and the role that it could play in their lives.

A group of young people investigate computer hardware together.
Experience AI is designed to inspire learners about AI through real-world contexts.

We provide lesson plans, classroom resources, worksheets, hands-on activities, and videos that introduce a wide range of AI applications and the underlying technologies that make them work. The materials are designed to be relatable to young people and can be taught by any teacher, whether or not they have a technical background. Alongside the classroom resources, we provide teacher professional development, including an online course that provides an introduction to machine learning and AI. 

Part of Experience AI are video interviews with AI developers at Google DeepMind.

The materials are grounded in real-world contexts and emphasise the potential for young people to positively change the world through a mastery of AI technologies. 

Since launching the first resources, we have seen significant demand from teachers and students all over the world, with over 200,000 students already learning with Experience AI. 

Experience AI network

Building on that initial success and in response to huge demand, we are now building a global network of educational organisations to expand the reach and impact of Experience AI by translating and localising the materials, promoting them to schools, and supporting teacher professional development.

Obum Ekeke OBE, Head of Education Partnerships at Google DeepMind, says:

“We have been blown away by the interest we have seen in Experience AI since its launch and are thrilled to be working with the Raspberry Pi Foundation and local partners to expand the reach of the programme. AI literacy is a critical skill in today’s world, but not every young person currently has access to relevant education and resources. By making AI education more inclusive, we can help young people make more informed decisions about using AI applications in their daily lives, and encourage safe and responsible use of the technology.”

Learner in a computing classroom.
Experience AI helps learners understand how they might use AI to positively change the world.

Today we are announcing the first three organisations that we are working with, each of which is already doing fantastic work to democratise digital skills in their part of the world. All three are already working in partnership with the Raspberry Pi Foundation and we are excited to be deepening and expanding our collaboration to include AI literacy.

Digital Moment, Canada

Digital Moment is a Montreal-based nonprofit focused on empowering young changemakers through digital skills. Founded in 2013, Digital Moment has a track record of supporting teachers and students across Canada to learn about computing, coding, and AI literacy, including through supporting one of the world’s largest networks of Code Clubs. 

Digital Moment logo.

“We’re excited to be working with the Raspberry Pi Foundation and Google DeepMind to bring Experience AI to teachers across Canada. Since 2018, Digital Moment has been introducing rich training experiences and educational resources to make sure that Canadian teachers have the support to navigate the impacts of AI in education for their students. Through this partnership, we will be able to reach more teachers and with more resources, to keep up with the incredible pace and disruption of AI.”

Indra Kubicek, President, Digital Moment

Tech Kidz Africa, Kenya

Tech Kidz Africa is a Mobasa-based social enterprise that nurtures creativity in young people across Kenya through digital skills including coding, robotics, app and web development, and creative design thinking.

Tech Kidz Africa logo.

“With the retooling of teachers as a key objective of Tech Kidz Africa, working with Google DeepMind and the Raspberry Pi Foundation will enable us to build the capacity of educators to empower the 21st century learner, enhancing the teaching and learning experience to encourage innovation and  prepare the next generation for the future of work.”

Grace Irungu, CEO, Tech Kidz Africa

Asociația Techsoup, Romania

Asociația Techsoup works with teachers and students across Romania and Moldova, training Computer Science, ICT, and primary school teachers to build their competencies around coding and technology. A longstanding partner of the Raspberry Pi Foundation, they foster a vibrant community of CoderDojos and support young people to participate in Coolest Projects and the European Astro Pi Challenge. 

Asociata Techsoup logo.

“We are enthusiastic about participating in this global partnership to bring high-quality AI education to all students, regardless of their background. Given the current exponential growth of AI tools and instruments in our daily lives, it is crucial to ensure that students and teachers everywhere comprehend and effectively utilise these tools to enhance their human, civic, and professional potential. Experience AI is the best available method for AI education for middle school students. We couldn’t be more thrilled to work with the Raspberry Pi Foundation and Google DeepMind to make it accessible in Romanian for teachers in Romania and the Republic of Moldova, and to assist teachers in fully integrating it into their classes.”

Elena Coman, Director of Development, Asociația Techsoup

Get involved

These are the first of what will become a global network of organisations supporting tens of thousands of teachers to equip millions of students with a foundational understanding of AI technologies through Experience AI. If you want to get involved in inspiring the next generation of AI leaders, we would love to hear from you.

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The Experience AI Challenge: Make your own AI project

Post Syndicated from Dan Fisher original https://www.raspberrypi.org/blog/experience-ai-challenge-announcement/

We are pleased to announce a new AI-themed challenge for young people: the Experience AI Challenge invites and supports young people aged up to 18 to design and make their own AI applications. This is their chance to have a taste of getting creative with the powerful technology of machine learning. And equally exciting: every young creator will get feedback and encouragement from us at the Raspberry Pi Foundation.

As you may have heard, we recently launched a series of classroom lessons called Experience AI in partnership with Google DeepMind. The lesson materials make it easy for teachers of all subjects to teach their learners aged up to 18 about artificial intelligence and machine learning. Now the Experience AI Challenge gives young people the opportunity to develop their skills further and build their own AI applications.

Key information

  • Starts on 08 January 2024
  • Free to take part in
  • Designed for beginners, based on the tools Scratch and Machine Learning for Kids
  • Open for official submissions made by UK-based young people aged up to 18 and their mentors 
  • Young people and their mentors around the world are welcome to access the Challenge resources and make AI projects
  • Tailored resources for young people and mentors to support you to take part
  • Register your interest and we’ll send you a reminder email on the launch day

The Experience AI Challenge

For the Experience AI Challenge, you and the young people you work with will learn how to make a machine learning (ML) classifier that organises data types such as audio, text, or images into different groupings that you specify.

A girl points excitedly at a project on the Raspberry Pi Foundation's projects site.

The Challenge resources show young people the basic principles of using the tools and training ML models. Then they will use these new skills to create their own projects, and it’s a chance for their imaginations to run free. Here are some examples of projects your young tech creators could make:

  • An instrument classifier to identify the type of musical instrument being played in pieces of music
  • An animal sound identifier to determine which animal is making a particular sound
  • A voice command recogniser to detect voice commands like ‘stop’, ‘go’, ‘left’, and ‘right’
  • A photo classifier to identify what kind of food is shown in a photograph

All creators will receive expert feedback on their projects.

To make the Experience AI Challenge as familiar and accessible as possible for young people who may be new to coding, we designed it for beginners. We chose the free, easy-to-use, online tool Machine Learning for Kids for young people to train their machine learning models, and Scratch as the programming environment for creators to code their projects. If you haven’t used these tools before, don’t worry. The Challenge resources will provide all the support you need to get up to speed.

Training an ML model and creating a project with it teaches many skills beyond coding, including computational thinking, ethical programming, data literacy, and developing a broader understanding of the influence of AI on society.

The three Challenge stages

Our resources for creators and mentors walk you through the three stages of the Experience AI Challenge.

Stage 1: Explore and discover

The first stage of the Challenge is designed to ignite young people’s curiosity. Through our resources, mentors let participants explore the world of AI and ML and discover how these technologies are revolutionising industries like healthcare and entertainment.

Stage 2: Get hands-on

In the second stage, young people choose a data type and embark on a guided example project. They create a training dataset, train an ML model, and develop a Scratch application as the user interface for their model. 

Stage 3: Design and create

In the final stage, mentors support young people to apply what they’ve learned to create their own ML project that addresses a problem they’re passionate about. They submit their projects to us online and receive feedback from our expert panel.

Things to do today

  1. Visit our new Experience AI Challenge homepage to find out more details
  2. Register your interest so you receive a reminder email on launch day, 8 January
  3. Get your young people excited and thinking about what kind of AI project they might like to create

We can’t wait to see how you and your young creators choose to engage with the Experience AI Challenge!

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Experience AI: Teach about AI, chatbots, and biology

Post Syndicated from Ben Garside original https://www.raspberrypi.org/blog/experience-ai-new-updated-lessons/

New artificial intelligence (AI) tools have had a profound impact on many areas of our lives in the past twelve months, including on education. Teachers and schools have been exploring how AI tools can transform their work, and how they can teach their learners about this rapidly developing technology. As enabling all schools and teachers to help their learners understand computing and digital technologies is part of our mission, we’ve been working hard to support educators with high-quality, free teaching resources about AI through Experience AI, our learning programme in partnership with Google DeepMind.

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In this article, we take you through the updates we’ve made to the Experience AI Lessons based on teachers’ feedback, reveal two new lessons on large language models (LLMs) and biology, and give you the chance to shape the future of the Experience AI programme. 

Updated lessons based on your feedback

In April we launched the first Experience AI Lessons as a unit of six lessons for secondary school students (ages 11 to 14, Key Stage 3) that gives you everything you need to teach AI, including lesson plans, slide decks, worksheets, and videos. Since the launch, we’ve worked closely with teachers and learners to make improvements to the lesson materials.

Access the Experience AI Lessons

The first big update you’ll see now is an additional project for students to do across Lesson 5 and Lesson 6. Before, students could choose between two projects to create their own machine learning model, either to classify data from the world’s oceans or to identify fake news. The new project we’ve added gives students the chance to use images to train a machine learning model to identify whether or not an item is biodegradable and therefore suitable to be put in a food waste bin.

Two teenagers sit at laptops and do coding activities.

Our second big update is a new set of teacher-focused videos that summarise each lesson and highlight possible talking points. We hope these videos will help you feel confident and ready to deliver the Experience AI Lessons to your learners.

A new lesson on large language models

As well as updating the six existing lessons, we’ve just released a new seventh lesson consisting of a set of activities to help students learn about the capabilities, opportunities, and downsides of LLMs, the models that AI chatbots are based on.

With the LLM lesson’s activities you can help your learners to:

  • Explore the purpose and functionality of LLMs and examine the critical aspect of trustworthiness of these models’ outputs
  • Examine the reasons why the output of LLMs may not always be reliable and understand that LLMs are machines that make predictions
  • Compare LLMs to other technologies to assess their suitability for different purposes
  • Evaluate the appropriateness of using LLMs in a variety of authentic scenarios
A slide from an Experience AI Lesson about large language models.
An example activity in our new LLM unit.

All Experience AI Lessons are designed to be cross-curricular, and for England-based teachers, the LLM lesson is particularly useful for teaching PSHE (Personal, Social, Health and Economic education).

The LLM lesson is designed as a set of five 10-minute activities, so you have the flexibility to teach the material as a single lesson or over a number of sessions. While we recommend that you teach the activities in the order they come, you can easily adapt them for your learners’ interests and needs. Feel free to take longer than our recommended time and have fun with them.

A new lesson on biology: AI for the Serengeti

We have also been working on an exciting new lesson to introduce AI to secondary school students (ages 11 to 14, Key Stage 3) in the biology classroom. This stand-alone lesson focuses on how AI can help conservationists with monitoring an ecosystem in the Serengeti.

Elephants in the Serengeti.

We worked alongside members of the Biology Education Research Group (BERG) at the UK’s Royal Society of Biology to make sure the lesson is relevant and accessible for Key Stage 3 teachers and their learners.

Register your interest if you would like to be one of the first teachers to try out this thought-provoking lesson.  

Webinars to support your teaching

If you want to use the Experience AI materials but would like more support, our new webinar series will help you. You will get your questions answered by the people who created the lessons. Our first webinar covered the six-lesson unit and you can watch the recording now:

September’s webinar: How to use Machine Learning for Kids

Join us to learn how to use Machine Learning for Kids (ML4K), a child-friendly tool for training AI models that is used for project work throughout the Experience AI Lessons. The September webinar will be with Dale Lane, who has spent his career developing AI technology and is the creator of ML4K.

Help shape the future of AI education

We need your feedback like a machine learning model needs data. Here are two ways you can share your thoughts:

  1. Fill in our form to tell us how you’ve used the Experience AI materials.
  2. Become part of our teacher feedback panel. We meet every half term, and our first session will be held mid-October. Email us to register your interest and we’ll be in touch.

To find out more about how you can use Experience AI to teach AI and machine learning to your learners this school year, visit the Experience AI website.

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How we’re learning to explain AI terms for young people and educators

Post Syndicated from Veronica Cucuiat original https://www.raspberrypi.org/blog/explaining-ai-terms-young-people-educators/

What do we talk about when we talk about artificial intelligence (AI)? It’s becoming a cliche to point out that, because the term “AI” is used to describe so many different things nowadays, it’s difficult to know straight away what anyone means when they say “AI”. However, it’s true that without a shared understanding of what AI and related terms mean, we can’t talk about them, or educate young people about the field.

A group of young people demonstrate a project at Coolest Projects.

So when we started designing materials for the Experience AI learning programme in partnership with leading AI unit Google DeepMind, we decided to create short explanations of key AI and machine learning (ML) terms. The explanations are doubly useful:

  1. They ensure that we give learners and teachers a consistent and clear understanding of the key terms across all our Experience AI resources. Within the Experience AI Lessons for Key Stage 3 (age 11–14), these key terms are also correlated to the target concepts and learning objectives presented in the learning graph. 
  2. They help us talk about AI and AI education in our team. Thanks to sharing an understanding of what terms such as “AI”, “ML”, “model”, or “training” actually mean and how to best talk about AI, our conversations are much more productive.

As an example, here is our explanation of the term “artificial intelligence” for learners aged 11–14:

Artificial intelligence (AI) is the design and study of systems that appear to mimic intelligent behaviour. Some AI applications are based on rules. More often now, AI applications are built using machine learning that is said to ‘learn’ from examples in the form of data. For example, some AI applications are built to answer questions or help diagnose illnesses. Other AI applications could be built for harmful purposes, such as spreading fake news. AI applications do not think. AI applications are built to carry out tasks in a way that appears to be intelligent.

You can find 32 explanations in the glossary that is part of the Experience AI Lessons. Here’s an insight into how we arrived at the explanations.

Reliable sources

In order to ensure the explanations are as precise as possible, we first identified reliable sources. These included among many others:

Explaining AI terms to Key Stage 3 learners: Some principles

Vocabulary is an important part of teaching and learning. When we use vocabulary correctly, we can support learners to develop their understanding. If we use it inconsistently, this can lead to alternate conceptions (misconceptions) that can interfere with learners’ understanding. You can read more about this in our Pedagogy Quick Read on alternate conceptions.

Some of our principles for writing explanations of AI terms were that the explanations need to: 

  • Be accurate
  • Be grounded in education research best practice
  • Be suitable for our target audience (Key Stage 3 learners, i.e. 11- to 14-year-olds)
  • Be free of terms that have alternative meanings in computer science, such as “algorithm”

We engaged in an iterative process of writing explanations, gathering feedback from our team and our Experience AI project partners at Google DeepMind, and adapting the explanations. Then we went through the feedback and adaptation cycle until we all agreed that the explanations met our principles.

A real banana and an image of a banana shown on the screen of a laptop are both labelled "Banana".
Image: Max Gruber / Better Images of AI / Ceci n’est pas une banane / CC-BY 4.0

An important part of what emerged as a result, aside from the explanations of AI terms themselves, was a blueprint for how not to talk about AI. One aspect of this is avoiding anthropomorphism, detailed by Ben Garside from our team here.

As part of designing the the Experience AI Lessons, creating the explanations helped us to:

  • Decide which technical details we needed to include when introducing AI concepts in the lessons
  • Figure out how to best present these technical details
  • Settle debates about where it would be appropriate, given our understanding and our learners’ age group, to abstract or leave out details

Using education research to explain AI terms

One of the ways education research informed the explanations was that we used semantic waves to structure each term’s explanation in three parts: 

  1. Top of the wave: The first one or two sentences are a high-level abstract explanation of the term, kept as short as possible, while introducing key words and concepts.
  2. Bottom of the wave: The middle part of the explanation unpacks the meaning of the term using a common example, in a context that’s familiar to a young audience. 
  3. Top of the wave: The final one or two sentences repack what was explained in the example in a more abstract way again to reconnect with the term. The end part should be a repeat of the top of the wave at the beginning of the explanation. It should also add further information to lead to another concept. 

Most explanations also contain ‘middle of the wave’ sentences, which add additional abstract content, bridging the ‘bottom of the wave’ concrete example to the ‘top of the wave’ abstract content.

Here’s the “artificial intelligence” explanation broken up into the parts of the semantic wave:

  • Artificial intelligence (AI) is the design and study of systems that appear to mimic intelligent behaviour. (top of the wave)
  • Some AI applications are based on rules. More often now, AI applications are built using machine learning that is said to ‘learn’ from examples in the form of data. (middle of the wave)
  • For example, some AI applications are built to answer questions or help diagnose illnesses. Other AI applications could be built for harmful purposes, such as spreading fake news (bottom of the wave)
  • AI applications do not think. (middle of the wave)
  • AI applications are built to carry out tasks in a way that appears to be intelligent. (top of the wave)
Our "artificial intelligence" explanation broken up into the parts of the semantic wave.
Our “artificial intelligence” explanation broken up into the parts of the semantic wave. Red = top of the wave; yellow = middle of the wave; green = bottom of the wave

Was it worth our time?

Some of the explanations went through 10 or more iterations before we agreed they were suitable for publication. After months of thinking about, writing, correcting, discussing, and justifying the explanations, it’s tempting to wonder whether I should have just prompted an AI chatbot to generate the explanations for me.

A window of three images. On the right is a photo of a big tree in a green field in a field of grass and a bright blue sky. The two on the left are simplifications created based on a decision tree algorithm. The work illustrates a popular type of machine learning model: the decision tree. Decision trees work by splitting the population into ever smaller segments. I try to give people an intuitive understanding of the algorithm. I also want to show that models are simplifications of reality, but can still be useful, or in this case visually pleasing. To create this I trained a model to predict pixel colour values, based on an original photograph of a tree.
Rens Dimmendaal & Johann Siemens / Better Images of AI / Decision Tree reversed / CC-BY 4.0

I tested this idea by getting a chatbot to generate an explanation of “artificial intelligence” using the prompt “Explain what artificial intelligence is, using vocabulary suitable for KS3 students, avoiding anthropomorphism”. The result included quite a few inconsistencies with our principles, as well as a couple of technical inaccuracies. Perhaps I could have tweaked the prompt for the chatbot in order to get a better result. However, relying on a chatbot’s output would mean missing out on some of the value of doing the work of writing the explanations in collaboration with my team and our partners.

The visible result of that work is the explanations themselves. The invisible result is the knowledge we all gained, and the coherence we reached as a team, both of which enabled us to create high-quality resources for Experience AI. We wouldn’t have gotten to know what resources we wanted to write without writing the explanations ourselves and improving them over and over. So yes, it was worth our time.

What do you think about the explanations?

The process of creating and iterating the AI explanations highlights how opaque the field of AI still is, and how little we yet know about how best to teach and learn about it. At the Raspberry Pi Foundation, we now know just a bit more about that and are excited to share the results with teachers and young people.

You can access the Experience AI Lessons and the glossary with all our explanations at experience-ai.org. The glossary of AI explanations is just in its first published version: we will continue to improve it as we find out more about how to best support young people to learn about this field.

Let us know what you think about the explanations and whether they’re useful in your teaching. Onwards with the exciting work of establishing how to successfully engage young people in learning about and creating with AI technologies.

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Experience AI: The excitement of AI in your classroom

Post Syndicated from Duncan Maidens original https://www.raspberrypi.org/blog/experience-ai-launch-lessons/

We are delighted to announce that we’ve launched Experience AI, our new learning programme to help educators to teach, inspire, and engage young people in the subject of artificial intelligence (AI) and machine learning (ML).

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.

A group of young people investigate computer hardware together.
 Develop a strong understanding of the concepts of AI and machine learning with your learners.

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.

I want to access the Experience AI lessons
An educator points to an image on a student's computer screen.
 Ben Garside, one of our lead educators working on Experience AI, takes a group of students through one of the new lessons.

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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How anthropomorphism hinders AI education

Post Syndicated from Ben Garside original https://www.raspberrypi.org/blog/ai-education-anthropomorphism/

In the 1950s, Alan Turing explored the central question of artificial intelligence (AI). He thought that the original question, “Can machines think?”, would not provide useful answers because the terms “machine” and “think” are hard to define. Instead, he proposed changing the question to something more provable: “Can a computer imitate intelligent behaviour well enough to convince someone they are talking to a human?” This is commonly referred to as the Turing test.

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.

A person in front of a cloudy sky, seen through a refractive glass grid. Parts of the image are overlaid with a diagram of a neural network.
Image: Alan Warburton / © BBC / Better Images of AI / Quantified Human / CC BY 4.0

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.

Eight photos of the same tree taken at different times of the year, displayed in a grid. The final photo is highly pixelated. Groups of white blocks run across the grid from left to right, gradually becoming aligned.
Image: David Man & Tristan Ferne / Better Images of AI / Trees / CC BY 4.0

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:

Avoid using Instead use
Avoid using phrases such as “AI learns” or “AI/ML does” Use phrases such as “AI applications are designed to…” or “AI developers build applications that…”
Avoid words that describe the behaviour of people (e.g. see, look, recognise, create, make) Use system type words (e.g. detect, input, pattern match, generate, produce)
Avoid using AI/ML as a countable noun, e.g. “new artificial intelligences emerged in 2022” Refer to ‘AI/ML’ as a scientific discipline, similarly to how you use the term “biology”

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 want to use the Experience AI lessons to teach your learners, please sign up to be the first to hear when we launch these resources.

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AI education resources: What do we teach young people?

Post Syndicated from Jane Waite original https://www.raspberrypi.org/blog/ai-education-resources-what-to-teach-seame-framework/

People have many different reasons to think that children and teenagers need to learn about artificial intelligence (AI) technologies. Whether it’s that AI impacts young people’s lives today, or that understanding these technologies may open up careers in their future — there is broad agreement that school-level education about AI is important.

A young person writes Python code.

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. 

Two learners and a teacher in a physical computing lesson.

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).
Click to enlarge.

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.

A slide from a lesson about AI that describes an AI application related to timetables.
An example activity in the Experience AI lessons where learners think about the social and ethical issues of an AI application that predicts what subjects they might want to study. This activity is mostly focused on the social and ethical level of the SEAME framework, but also links to the applications and models levels.

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).

A slide from a lesson about AI that describes an ML model to classify animals.
An example activity in the Experience AI lessons where students learn about classification. This activity is mostly focused on the models level of the SEAME framework, but also links to the social and ethical and the applications levels.

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.

A teacher helps a young person with a coding project.

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.

We’re releasing the Experience AI lessons very soon — if you want to be the first to hear news about them, please sign up here.

I want to hear more about Experience AI

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.

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What to expect from the Raspberry Pi Foundation in 2023

Post Syndicated from Philip Colligan original https://www.raspberrypi.org/blog/raspberry-pi-foundation-plans-2023/

Welcome to 2023.  I hope that you had a fantastic 2022 and that you’re looking forward to an even better year ahead. To help get the year off to a great start, I thought it might be fun to share a few of the things that we’ve got planned for 2023.

A teacher and learner at a laptop doing coding.

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, theAstro 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).

Two Astro Pi units on board the International Space Station.
The Raspberry Pi computers on board the ISS (Image: ESA/NASA)

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?

Five young coders show off their robotic garden tech project for Coolest Projects.

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.

A group of young people investigate computer hardware together.

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.

Workshop attendees at a table.

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.

Two Kenyan educators work on a physical computing project.

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.

A teacher and a learner at a laptop doing coding.

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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Experience AI with the Raspberry Pi Foundation and DeepMind

Post Syndicated from Philip Colligan original https://www.raspberrypi.org/blog/experience-ai-deepmind-ai-education/

I am delighted to announce a new collaboration between the Raspberry Pi Foundation and a leading AI company, DeepMind, to inspire the next generation of AI leaders.

Young people work together to investigate computer hardware.

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. 

DeepMind logo.

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 program 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.

In a computing classroom, two girls concentrate on their programming task.

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.

A woman teacher helps a young person with a coding project.

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 Inspire learning materials is underway now, and we will release the whole set of resources early in 2023. Throughout 2023, we will design and pilot the Experiment challenge.

If you want to stay up to date with Experience AI, or if you’d like to be involved in testing the materials, fill in this form to register your interest.

I want to register my interest in Experience AI

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