Tag Archives: AI literacy

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.

The post Localising AI education: Adapting Experience AI for global impact appeared first on Raspberry Pi Foundation.

Insights into students’ attitudes to using AI tools in programming education

Post Syndicated from Katharine Childs original https://www.raspberrypi.org/blog/insights-into-students-attitudes-to-using-ai-tools-in-programming-education/

Educators around the world are grappling with the problem of whether to use artificial intelligence (AI) tools in the classroom. As more and more teachers start exploring the ways to use these tools for teaching and learning computing, there is an urgent need to understand the impact of their use to make sure they do not exacerbate the digital divide and leave some students behind.

A teenager learning computer science.

Sri Yash Tadimalla from the University of North Carolina and Dr Mary Lou Maher, Director of Research Community Initiatives at the Computing Research Association, are exploring how student identities affect their interaction with AI tools and their perceptions of the use of AI tools. They presented findings from two of their research projects in our March seminar.

How students interact with AI tools 

A common approach in research is to begin with a preliminary study involving a small group of participants in order to test a hypothesis, ways of collecting data from participants, and an intervention. Yash explained that this was the approach they took with a group of 25 undergraduate students on an introductory Java programming course. The research observed the students as they performed a set of programming tasks using an AI chatbot tool (ChatGPT) or an AI code generator tool (GitHub Copilot). 

The data analysis uncovered five emergent attitudes of students using AI tools to complete programming tasks: 

  • Highly confident students rely heavily on AI tools and are confident about the quality of the code generated by the tool without verifying it
  • Cautious students are careful in their use of AI tools and verify the accuracy of the code produced
  • Curious students are interested in exploring the capabilities of the AI tool and are likely to experiment with different prompts 
  • Frustrated students struggle with using the AI tool to complete the task and are likely to give up 
  • Innovative students use the AI tool in creative ways, for example to generate code for other programming tasks

Whether these attitudes are common for other and larger groups of students requires more research. However, these preliminary groupings may be useful for educators who want to understand their students and how to support them with targeted instructional techniques. For example, highly confident students may need encouragement to check the accuracy of AI-generated code, while frustrated students may need assistance to use the AI tools to complete programming tasks.

An intersectional approach to investigating student attitudes

Yash and Mary Lou explained that their next research study took an intersectional approach to student identity. Intersectionality is a way of exploring identity using more than one defining characteristic, such as ethnicity and gender, or education and class. Intersectional approaches acknowledge that a person’s experiences are shaped by the combination of their identity characteristics, which can sometimes confer multiple privileges or lead to multiple disadvantages.

A student in a computing classroom.

In the second research study, 50 undergraduate students participated in programming tasks and their approaches and attitudes were observed. The gathered data was analysed using intersectional groupings, such as:

  • Students who were from the first generation in their family to attend university and female
  • Students who were from an underrepresented ethnic group and female 

Although the researchers observed differences amongst the groups of students, there was not enough data to determine whether these differences were statistically significant.

Who thinks using AI tools should be considered cheating? 

Participating students were also asked about their views on using AI tools, such as “Did having AI help you in the process of programming?” and “Does your experience with using this AI tool motivate you to continue learning more about programming?”

The same intersectional approach was taken towards analysing students’ answers. One surprising finding stood out: when asked whether using AI tools to help with programming tasks should be considered cheating, students from more privileged backgrounds agreed that this was true, whilst students with less privilege disagreed and said it was not cheating.

This finding is only with a very small group of students at a single university, but Yash and Mary Lou called for other researchers to replicate this study with other groups of students to investigate further. 

You can watch the full seminar here:

Acknowledging differences to prevent deepening divides

As researchers and educators, we often hear that we should educate students about the importance of making AI ethical, fair, and accessible to everyone. However, simply hearing this message isn’t the same as truly believing it. If students’ identities influence how they view the use of AI tools, it could affect how they engage with these tools for learning. Without recognising these differences, we risk continuing to create wider and deeper digital divides. 

Join our next seminar

The focus of our ongoing seminar series is on teaching programming with or without AI. 

For our next seminar on Tuesday 16 April at 17:00 to 18:30 GMT, we’re joined by Brett A. Becker (University College Dublin), who will talk about how generative AI can be used effectively in secondary school programming education and how it can be leveraged so that students can be best prepared for continuing their education or beginning their careers. To take part in the seminar, click the button below to sign up, and we will send you information about how to join. We hope to see you there.

I want to join the next seminar

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

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Using an AI code generator with school-age beginner programmers

Post Syndicated from Bobby Whyte original https://www.raspberrypi.org/blog/using-an-ai-code-generator-with-school-age-beginner-programmers/

AI models for general-purpose programming, such as OpenAI Codex, which powers the AI pair programming tool GitHub Copilot, have the potential to significantly impact how we teach and learn programming. 

Learner in a computing classroom.

The basis of these tools is a ‘natural language to code’ approach, also called natural language programming. This allows users to generate code using a simple text-based prompt, such as “Write a simple Python script for a number guessing game”. Programming-specific AI models are trained on vast quantities of text data, including GitHub repositories, to enable users to quickly solve coding problems using natural language. 

As a computing educator, you might ask what the potential is for using these tools in your classroom. In our latest research seminar, Majeed Kazemitabaar (University of Toronto) shared his work in developing AI-assisted coding tools to support students during Python programming tasks.

Evaluating the benefits of natural language programming

Majeed argued that natural language programming can enable students to focus on the problem-solving aspects of computing, and support them in fixing and debugging their code. However, he cautioned that students might become overdependent on the use of ‘AI assistants’ and that they might not understand what code is being outputted. Nonetheless, Majeed and colleagues were interested in exploring the impact of these code generators on students who are starting to learn programming.

Using AI code generators to support novice programmers

In one study, the team Majeed works in investigated whether students’ task and learning performance was affected by an AI code generator. They split 69 students (aged 10–17) into two groups: one group used a code generator in an environment, Coding Steps, that enabled log data to be captured, and the other group did not use the code generator.

A group of male students at the Coding Academy in Telangana.

Learners who used the code generator completed significantly more authoring tasks — where students manually write all of the code — and spent less time completing them, as well as generating significantly more correct solutions. In multiple choice questions and modifying tasks — where students were asked to modify a working program — students performed similarly whether they had access to the code generator or not. 

A test was administered a week later to check the groups’ performance, and both groups did similarly well. However, the ‘code generator’ group made significantly more errors in authoring tasks where no starter code was given. 

Majeed’s team concluded that using the code generator significantly increased the completion rate of tasks and student performance (i.e. correctness) when authoring code, and that using code generators did not lead to decreased performance when manually modifying code. 

Finally, students in the code generator group reported feeling less stressed and more eager to continue programming at the end of the study.

Student perceptions when (not) using AI code generators

Understanding how novices use AI code generators

In a related study, Majeed and his colleagues investigated how novice programmers used the code generator and whether this usage impacted their learning. Working with data from 33 learners (aged 11–17), they analysed 45 tasks completed by students to understand:

  1. The context in which the code generator was used
  2. What learners asked for
  3. How prompts were written
  4. The nature of the outputted code
  5. How learners used the outputted code 

Their analysis found that students used the code generator for the majority of task attempts (74% of cases) with far fewer tasks attempted without the code generator (26%). Of the task attempts made using the code generator, 61% involved a single prompt while only 8% involved decomposition of the task into multiple prompts for the code generator to solve subgoals; 25% used a hybrid approach — that is, some subgoal solutions being AI-generated and others manually written.

In a comparison of students against their post-test evaluation scores, there were positive though not statistically significant trends for students who used a hybrid approach (see the image below). Conversely, negative though not statistically significant trends were found for students who used a single prompt approach.

A positive correlation between hybrid programming and post-test scores

Though not statistically significant, these results suggest that the students who actively engaged with tasks — i.e. generating some subgoal solutions, manually writing others, and debugging their own written code — performed better in coding tasks.

Majeed concluded that while the data showed evidence of self-regulation, such as students writing code manually or adding to AI-generated code, students frequently used the output from single prompts in their solutions, indicating an over-reliance on the output of AI code generators.

He suggested that teachers should support novice programmers to write better quality prompts to produce better code.  

If you want to learn more, you can watch Majeed’s seminar:

You can read more about Majeed’s work on his personal website. You can also download and use the code generator Coding Steps yourself.

Join our next seminar

The focus of our ongoing seminar series is on teaching programming with or without AI. 

For our next seminar on Tuesday 16 April at 17:00–18:30 GMT, we’re joined by Brett Becker (University College Dublin), who will discuss how generative AI may be effectively utilised in secondary school programming education and how it can be leveraged so that students can be best prepared for whatever lies ahead. To take part in the seminar, click the button below to sign up, and we will send you information about joining. We hope to see you there.

I want to join the next seminar

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

The post Using an AI code generator with school-age beginner programmers appeared first on Raspberry Pi Foundation.

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.

The post The Experience AI Challenge: Find out all you need to know appeared first on Raspberry Pi Foundation.

Teaching about AI explainability

Post Syndicated from Mac Bowley original https://www.raspberrypi.org/blog/teaching-ai-explainability/

In the rapidly evolving digital landscape, students are increasingly interacting with AI-powered applications when listening to music, writing assignments, and shopping online. As educators, it’s our responsibility to equip them with the skills to critically evaluate these technologies.

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

A key aspect of this is understanding ‘explainability’ in AI and machine learning (ML) systems. The explainability of a model is how easy it is to ‘explain’ how a particular output was generated. Imagine having a job application rejected by an AI model, or facial recognition technology failing to recognise you — you would want to know why.

Two teenage girls do coding activities at their laptops in a classroom.

Establishing standards for explainability is crucial. Otherwise we risk creating a world where decisions impacting our lives are made by opaque systems we don’t understand. Learning about explainability is key for students to develop digital literacy, enabling them to navigate the digital world with informed awareness and critical thinking.

Why AI explainability is important

AI models can have a significant impact on people’s lives in various ways. For instance, if a model determines a child’s exam results, parents and teachers would want to understand the reasoning behind it.

Two learners sharing a laptop in a coding session.

Artists might want to know if their creative works have been used to train a model and could be at risk of plagiarism. Likewise, coders will want to know if their code is being generated and used by others without their knowledge or consent. If you came across an AI-generated artwork that features a face resembling yours, it’s natural to want to understand how a photo of you was incorporated into the training data. 

Explainability is about accountability, transparency, and fairness, which are vital lessons for children as they grow up in an increasingly digital world.

There will also be instances where a model seems to be working for some people but is inaccurate for a certain demographic of users. This happened with Twitter’s (now X’s) face detection model in photos; the model didn’t work as well for people with darker skin tones, who found that it could not detect their faces as effectively as their lighter-skinned friends and family. Explainability allows us not only to understand but also to challenge the outputs of a model if they are found to be unfair.

In essence, explainability is about accountability, transparency, and fairness, which are vital lessons for children as they grow up in an increasingly digital world.

Routes to AI explainability

Some models, like decision trees, regression curves, and clustering, have an in-built level of explainability. There is a visual way to represent these models, so we can pretty accurately follow the logic implemented by the model to arrive at a particular output.

By teaching students about AI explainability, we are not only educating them about the workings of these technologies, but also teaching them to expect transparency as they grow to be future consumers or even developers of AI technology.

A decision tree works like a flowchart, and you can follow the conditions used to arrive at a prediction. Regression curves can be shown on a graph to understand why a particular piece of data was treated the way it was, although this wouldn’t give us insight into exactly why the curve was placed at that point. Clustering is a way of collecting similar pieces of data together to create groups (or clusters) with which we can interrogate the model to determine which characteristics were used to create the groupings.

A decision tree that classifies animals based on their characteristics; you can follow these models like a flowchart

However, the more powerful the model, the less explainable it tends to be. Neural networks, for instance, are notoriously hard to understand — even for their developers. The networks used to generate images or text can contain millions of nodes spread across thousands of layers. Trying to work out what any individual node or layer is doing to the data is extremely difficult.

Learners in a computing classroom.

Regardless of the complexity, it is still vital that developers find a way of providing essential information to anyone looking to use their models in an application or to a consumer who might be negatively impacted by the use of their model.

Model cards for AI models

One suggested strategy to add transparency to these models is using model cards. When you buy an item of food in a supermarket, you can look at the packaging and find all sorts of nutritional information, such as the ingredients, macronutrients, allergens they may contain, and recommended serving sizes. This information is there to help inform consumers about the choices they are making.

Model cards attempt to do the same thing for ML models, providing essential information to developers and users of a model so they can make informed choices about whether or not they want to use it.

A model card mock-up from the Experience AI Lessons

Model cards include details such as the developer of the model, the training data used, the accuracy across diverse groups of people, and any limitations the developers uncovered in testing.

Model cards should be accessible to as many people as possible.

A real-world example of a model card is Google’s Face Detection model card. This details the model’s purpose, architecture, performance across various demographics, and any known limitations of their model. This information helps developers who might want to use the model to assess whether it is fit for their purpose.

Transparency and accountability in AI

As the world settles into the new reality of having the amazing power of AI models at our disposal for almost any task, we must teach young people about the importance of transparency and responsibility. 

An educator points to an image on a student's computer screen.

As a society, we need to have hard discussions about where and when we are comfortable implementing models and the consequences they might have for different groups of people. By teaching students about explainability, we are not only educating them about the workings of these technologies, but also teaching them to expect transparency as they grow to be future consumers or even developers of AI technology.

Most importantly, model cards should be accessible to as many people as possible — taking this information and presenting it in a clear and understandable way. Model cards are a great way for you to show your students what information is important for people to know about an AI model and why they might want to know it. Model cards can help students understand the importance of transparency and accountability in AI.  

This article also appears in issue 22 of Hello World, which is all about teaching and AI. Download your free PDF copy now.

If you’re an educator, you can use our free Experience AI Lessons to teach your learners the basics of how AI works, whatever your subject area.

The post Teaching about AI explainability appeared first on Raspberry Pi Foundation.

AI isn’t just robots: How to talk to young children about AI

Post Syndicated from Sway Grantham original https://www.raspberrypi.org/blog/how-to-talk-to-young-children-about-ai/

Young children have a unique perspective on the world they live in. They often seem oblivious to what’s going on around them, but then they will ask a question that makes you realise they did get some insight from a news story or a conversation they overheard. This happened to me with a class of ten-year-olds when one boy asked, with complete sincerity and curiosity, “And is that when the zombie apocalypse happened?” He had unknowingly conflated the Great Plague with television depictions of zombies taking over the world.

Child with tablet.
Photo by Patricia Prudente.

How to talk to young people about AI

Absorbing media and assimilating it into your existing knowledge is a challenge, and this is a concern when the media is full of big, scary headlines about artificial intelligence (AI) taking over the world, stealing jobs, and being sentient. As teachers and parents, you don’t need to know all the details about AI to answer young people’s questions, but you can avoid accidentally introducing alternate conceptions. This article offers some top tips to help you point those inquisitive minds in the right direction.

Child with tablet.
Photo by Kelly Sikkema.

AI is not a person

Technology companies like to anthropomorphise their products and give them friendly names. Why? Because it makes their products seem more endearing and less scary, and makes you more likely to include them in your lives. However, when you think of AI as a human with a name who needs you to say ‘please’ or is ‘there to help you’, you start to make presumptions about how it works, what it ‘knows’, and its morality. This changes what we ask, how much we trust an AI device’s responses, and how we behave when using the device. The device, though, does not ‘see’ or ‘know’ anything; instead, it uses lots of data to make predictions. Think of word association: if I say “bread”, I predict that a lot of people in the UK will think “butter”. Here, I’ve used the data I’ve collected from years of living in this country to predict a reasonable answer. This is all AI devices are doing. 

Child with phone.
Photo by bruce mars.

[AI] does not ‘see’ or ‘know’ anything; instead, it uses lots of data to make predictions.

When talking to young children about AI, try to avoid using pronouns such as ‘she’ or ‘he’. Where possible, avoid giving devices human names, and instead call them “computer”, to reinforce the idea that humans and computers are very different. Let’s imagine that a child in your class says, “Alexa told me a joke at the weekend — she’s funny!” You could respond, “I love using computers to find new jokes! What was it?” This is just a micro-conversation, but with it, you are helping to surreptitiously challenge the child’s perception of Alexa and the role of AI in it.

Where possible, avoid giving devices human names, and instead call them ‘computer’, to reinforce the idea that humans and computers are very different.

Another good approach is to remember to keep your emotions separate from computers, so as not to give them human-like characteristics: don’t say that the computer ‘hates’ you, or is ‘deliberately ignoring’ you, and remember that it’s only ‘helpful’ because it was told to be. Language is important, and we need to continually practise avoiding anthropomorphism.

AI isn’t just robots (actually, it rarely is)

The media plays a huge role in what we imagine when we talk about AI. For the media, the challenge is how to make lines of code and data inside a computer look exciting and recognisable to their audiences. The answer? Robots! When learners hear about AI taking over the world, it’s easy for them to imagine robots like those you’d find in a Marvel movie. Yet the majority of AI exists within systems they’re already aware of and are using — you might just need to help draw their attention to it.

Even better than just calling out uses of AI: try to have conversations about when things go wrong and AI systems suggest silly options.

For example, when using a word processor, you can highlight to learners that the software sometimes predicts what word you want to type next, and that this is an example of the computer using AI. When learners are using streaming services for music or TV and the service predicts something that they might want to watch or listen to next, point out that this is using AI technology. When they see their parents planning a route using a satnav, explain that the satnav system uses data and AI to plan the best route.

Even better than just calling out uses of AI: try to have conversations about when things go wrong and AI systems suggest silly options. This is a great way to build young people’s critical thinking around the use of computers. AI systems don’t always know best, because they’re just making predictions, and predictions can always be wrong.

AI complements humans

There’s a delicate balance between acknowledging the limitations of AI and portraying it as a problematic tool that we shouldn’t use. AI offers us great opportunities to improve the way we work, to get us started on a creative project, or to complete mundane tasks. However, it is just a tool, and tools complement the range of skills that humans already have. For example, if you gave an AI chatbot app the prompt, ‘Write a setting description using these four phrases: dark, scary, forest, fairy tale’, the first output from the app probably wouldn’t make much sense. As a human, though, you’d probably have to do far less work to edit the output than if you had had to write the setting description from scratch. Now, say you had the perfect example of a setting description, but you wanted 29 more examples, a different version for each learner in your class. This is where AI can help: completing a repetitive task and saving time for humans. 

Child with phone.
Photo by zhenzhong liu.

To help children understand how AI and humans complement each other, ask them the question, ‘What can’t a computer do?’ Answers that I have received before include, ‘Give me a hug’, ‘Make me laugh’, and ‘Paint a picture’, and these are all true. Can Alexa tell you a joke that makes you laugh? Yes — but a human created that joke. The computer is just the way in which it is being shared. Even with AI ‘creating’ new artwork, it is really only using data from something that someone else created. Humans are required. 

Overall, we must remember that young children are part of a world that uses AI, and that it is likely to be ever more present in the future. We need to ensure that they know how to use AI responsibly, by minimising their alternate conceptions. With our youngest learners, this means taking care with the language you choose and the examples you use, and explaining AI’s role as a tool.

To help children understand how AI and humans complement each other, ask them the question, ‘What can’t a computer do?’

These simple approaches are the first steps to empowering children to go on to harness this technology. They also pave the way for you to simply introduce the core concepts of AI in later computing lessons without first having to untangle a web of alternate conceptions.

This article also appears in issue 22 of Hello World, which is all about teaching and AI. Download your free PDF copy now.

If you’re an educator, you can use our free Experience AI Lessons to teach your learners the basics of how AI works, whatever your subject area.

The post AI isn’t just robots: How to talk to young children about AI appeared first on Raspberry Pi Foundation.

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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Hello World #22 out now: Teaching and AI

Post Syndicated from Meg Wang original https://www.raspberrypi.org/blog/hello-world-22-ai-education/

Recent developments in artificial intelligence are changing how the world sees computing and challenging computing educators to rethink their approach to teaching. In the brand-new issue of Hello World, out today for free, we tackle some big questions about AI and computing education. We also get practical with resources for your classroom.

Cover of Hello World issue 22.

Teaching and AI

In their articles for issue 22, educators explore a range of topics related to teaching and AI, including what is AI literacy and how do we teach it; gender bias in AI and what we can do about it; how to speak to young children about AI; and why anthropomorphism hinders learners’ understanding of AI.

Download your free digital copy

Our feature articles also include a research digest on AI ethics for children, and of course hands-on examples of AI lessons for your learners.

A snapshot of AI education

Hello World issue 22 is a comprehensive snapshot of the current landscape of AI education. Ben Garside, Learning Manager for our Experience AI programme and guest editor of this issue, says:

“When I was teaching in the classroom, I used to enjoy getting to grips with new technological advances and finding ways in which I could bring them into school and excite the students I taught. Occasionally, during the busiest of times, I’d also look longingly at other subjects and be jealous that their curriculum appeared to be more static than ours (probably a huge misconception on my behalf).”

It’s inspiring for me to see how the education community is reacting to the opportunities that AI can provide.

Ben Garside

“It’s inspiring for me to see how the education community is reacting to the opportunities that AI can provide. Of course, there are elements of AI where we need to tread carefully and be very cautious in our approach, but what you’ll see in this magazine is educators who are thinking creatively in this space.”

Download Hello World issue 22 for free

AI is a topic we’ve addressed before in Hello World, and we’ll keep covering this rapidly evolving area in future. We hope this issue gives you plenty of ideas to take away and build upon.

Also in issue 22:

  • Vocational training for young people
  • Making the most of online educator training
  • News about BBC micro:bit
  • An insight into the WiPSCE 2023 conference for teachers and educators
  • And much, much more

You can download your free PDF issue now, or purchase a print copy from our store. UK-based subscribers for a free print edition can expect their copies to arrive in the mail this week.

Download issue 22 now

Send us a message or tag us on social media to let us know which articles have made you think and, most importantly, which will help you with your teaching.

The post Hello World #22 out now: Teaching and AI appeared first on Raspberry Pi Foundation.

What does AI mean for computing education?

Post Syndicated from Philip Colligan original https://www.raspberrypi.org/blog/what-does-ai-mean-for-computing-education/

It’s been less than a year since ChatGPT catapulted generative artificial intelligence (AI) into mainstream public consciousness, reigniting the debate about the role that these powerful new technologies will play in all of our futures.

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

‘Will AI save or destroy humanity?’ might seem like an extreme title for a podcast, particularly if you’ve played with these products and enjoyed some of their obvious limitations. The reality is that we are still at the foothills of what AI technology can achieve (think World Wide Web in the 1990s), and lots of credible people are predicting an astonishing pace of progress over the next few years, promising the radical transformation of almost every aspect of our lives. Comparisons with the Industrial Revolution abound.

At the same time, there are those saying it’s all moving too fast; that regulation isn’t keeping pace with innovation. One of the UK’s leading AI entrepreneurs, Mustafa Suleyman, said recently: “If you don’t start from a position of fear, you probably aren’t paying attention.”

In a computing classroom, a girl looks at a computer screen.
What is AI literacy for young people?

What does all this mean for education, and particularly for computing education? Is there any point trying to teach children about AI when it is all changing so fast? Does anyone need to learn to code anymore? Will teachers be replaced by chatbots? Is assessment as we know it broken?

If we’re going to seriously engage with these questions, we need to understand that we’re talking about three different things:

  1. AI literacy: What it is and how we teach it
  2. Rethinking computer science (and possibly some other subjects)
  3. Enhancing teaching and learning through AI-powered technologies

AI literacy: What it is and how we teach it

For young people to thrive in a world that is being transformed by AI systems, they need to understand these technologies and the role they could play in their lives.

In a computing classroom, a smiling girl raises her hand.
Our SEAME model articulates the concepts, knowledge, and skills that are essential ingredients of any AI literacy curriculum.

The first problem is defining what AI literacy actually means. What are the concepts, knowledge, and skills that it would be useful for a young person to learn?

The reality is that — with a few notable exceptions — the vast majority of AI literacy resources available today are probably doing more harm than good.

In the past couple of years there has been a huge explosion in resources that claim to help young people develop AI literacy. Our research team mapped and categorised over 500 resources, and undertaken a systematic literature review to understand what research has been done on K–12 AI classroom interventions (spoiler: not much). 

The reality is that — with a few notable exceptions — the vast majority of AI literacy resources available today are probably doing more harm than good. For example, in an attempt to be accessible and fun, many materials anthropomorphise AI systems, using human terms to describe them and their functions and thereby perpetuating misconceptions about what AI systems are and how they work.

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

What emerged from this work at the Raspberry Pi Foundation is the SEAME model, which articulates the concepts, knowledge, and skills that are essential ingredients of any AI literacy curriculum. It separates out the social and ethical, application, model, and engine levels of AI systems — all of which are important — and gets specific about age-appropriate learning outcomes for each. 

This research has formed the basis of Experience AI (experience-ai.org), a suite of resources, lessons plans, videos, and interactive learning experiences created by the Raspberry Pi Foundation in partnership with Google DeepMind, which is already being used in thousands of classrooms.

If we’re serious about AI literacy for young people, we have to get serious about AI literacy for teachers.

Defining AI literacy and developing resources is part of the challenge, but that doesn’t solve the problem of how we get them into the hands and minds of every young person. This will require policy change. We need governments and education system leaders to grasp that a foundational understanding of AI technologies is essential for creating economic opportunity, ensuring that young people have the mindsets to engage positively with technological change, and avoiding a widening of the digital divide. We’ve messed this up before with digital skills. Let’s not do it again.

Two smiling adults learn about computing at desktop computers.
Teacher professional development is key to AI literacy for young people.

More than anything, we need to invest in teachers and their professional development. While there are some fantastic computing teachers with computer science qualifications, the reality is that most of the computing lessons taught anywhere on the planet are taught by a non-specialist teacher. That is even more so the case for anything related to AI. If we’re serious about AI literacy for young people, we have to get serious about AI literacy for teachers. 

Rethinking computer science 

Alongside introducing AI literacy, we also need to take a hard look at computer science. At the very least, we need to make sure that computer science curricula include machine learning models, explaining how they constitute a new paradigm for computing, and give more emphasis to the role that data will play in the future of computing. Adding anything new to an already packed computer science curriculum means tough choices about what to deprioritise to make space.

Elephants in the Serengeti.
One of our Experience AI Lessons revolves around the us of AI technology to study the Serengeti ecosystem.

And, while we’re reviewing curricula, what about biology, geography, or any of the other subjects that are just as likely to be revolutionised by big data and AI? As part of Experience AI, we are launching some of the first lessons focusing on ecosystems and AI, which we think should be at the heart of any modern biology curriculum. 

Some are saying young people don’t need to learn how to code. It’s an easy political soundbite, but it just doesn’t stand up to serious scrutiny.

There is already a lively debate about the extent to which the new generation of AI technologies will make programming as we know it obsolete. In January, the prestigious ACM journal ran an opinion piece from Matt Welsh, founder of an AI-powered programming start-up, in which he said: “I believe the conventional idea of ‘writing a program’ is headed for extinction, and indeed, for all but very specialised applications, most software, as we know it, will be replaced by AI systems that are trained rather than programmed.”

Computer science students at a desktop computer in a classroom.
Writing computer programs is an essential part of learning how to analyse problems in computational terms.

With GitHub (now part of Microsoft) claiming that their pair programming technology, Copilot, is now writing 46 percent of developers’ code, it’s perhaps not surprising that some are saying young people don’t need to learn how to code. It’s an easy political soundbite, but it just doesn’t stand up to serious scrutiny. 

Even if AI systems can improve to the point where they generate consistently reliable code, it seems to me that it is just as likely that this will increase the demand for more complex software, leading to greater demand for more programmers. There is historical precedent for this: the invention of abstract programming languages such as Python dramatically simplified the act of humans providing instructions to computers, leading to more complex software and a much greater demand for developers. 

A child codes a Spiderman project at a laptop during a Code Club session.
Learning to program will help young people understand how the world around them is being transformed by AI systems.

However these AI-powered tools develop, it will still be essential for young people to learn the fundamentals of programming and to get hands-on experience of writing code as part of any credible computer science course. Practical experience of writing computer programs is an essential part of learning how to analyse problems in computational terms; it brings the subject to life; it will help young people understand how the world around them is being transformed by AI systems; and it will ensure that they are able to shape that future, rather than it being something that is done to them.

Enhancing teaching and learning through AI-powered technologies

Technology has already transformed learning. YouTube is probably the most important educational innovation of the past 20 years, democratising both the creation and consumption of learning resources. Khan Academy, meanwhile, integrated video instruction into a learning experience that gamified formative assessment. Our own edtech platform, Ada Computer Science, combines comprehensive instructional materials, a huge bank of questions designed to help learning, and automated marking and feedback to make computer science easier to teach and learn. Brilliant though these are, none of them have even begun to harness the potential of AI systems like large language models (LLMs).

The challenge for all of us working in education is how we ensure that ethics and privacy are at the centre of the development of [AI-powered edtech].

One area where I think we’ll see huge progress is feedback. It’s well-established that good-quality feedback makes a huge difference to learning, but a teacher’s ability to provide feedback is limited by their time. No one is seriously claiming that chatbots will replace teachers, but — if we can get the quality right — LLM applications could provide every child with unlimited, on-demand feedback. AI-powered feedback — not giving students the answers, but coaching, suggesting, and encouraging in the way that great teachers already do — could be transformational.

Two adults learn about computing at desktop computers.
The challenge for all of us working in education is how we ensure that ethics and privacy are at the centre of the development of AI-powered edtech.

We are already seeing edtech companies racing to bring new products and features to market that leverage LLMs, and my prediction is that the pace of that innovation is going to increase exponentially over the coming years. The challenge for all of us working in education is how we ensure that ethics and privacy are at the centre of the development of these technologies. That’s important for all applications of AI, but especially so in education, where these systems will be unleashed directly on young people. How much data from students will an AI system need to access? Can that data — aggregated from millions of students — be used to train new models? How can we communicate transparently the limitations of the information provided back to students?

Ultimately, we need to think about how parents, teachers, and education systems (the purchasers of edtech products) will be able to make informed choices about what to put in front of students. Standards will have an important role to play here, and I think we should be exploring ideas such as an AI kitemark for edtech products that communicate whether they meet a set of standards around bias, transparency, and privacy. 

Realising potential in a brave new world

We may very well be entering an era in which AI systems dramatically enhance the creativity and productivity of humanity as a species. Whether the reality lives up to the hype or not, AI systems are undoubtedly going to be a big part of all of our futures, and we urgently need to figure out what that means for education, and what skills, knowledge, and mindsets young people need to develop in order to realise their full potential in that brave new world. 

That’s the work we’re engaged in at the Raspberry Pi Foundation, working in partnership with individuals and organisations from across industry, government, education, and civil society.

If you have ideas and want to get involved in shaping the future of computing education, we’d love to hear from you.

This article will also appear in issue 22 of Hello World magazine, which focuses on teaching and AI. We are publishing this new issue on Monday 23 October. Sign up for a free digital subscription to get the PDF straight to your inbox on the day.

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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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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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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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AI literacy research: Children and families working together around smart devices

Post Syndicated from Sue Sentance original https://www.raspberrypi.org/blog/ai-literacy-children-families-working-together-ai-education-research/

Between September 2021 and March 2022, we’ve been partnering with The Alan Turing Institute to host a series of free research seminars about how to young people about AI and data science.

In the final seminar of the series, we were excited to hear from Stefania Druga from the University of Washington, who presented on the topic of AI literacy for families. Stefania’s talk highlighted the importance of families in supporting children to develop AI literacy. Her talk was a perfect conclusion to the series and very well-received by our audience.

Stefania Druga.
Stefania Druga, University of Washington

Stefania is a third-year PhD student who has been working on AI literacy in families, and since 2017 she has conducted a series of studies that she presented in her seminar talk. She presented some new work to us that was to be formally shared at the HCI conference in April, and we were very pleased to have a sneak preview of these results. It was a fascinating talk about the ways in which the interactions between parents and children using AI-based devices in the home, and the discussions they have while learning together, can facilitate an appreciation of the affordances of AI systems. You’ll find my summary as well as the seminar recording below.

“AI literacy practices and skills led some families to consider making meaningful use of AI devices they already have in their homes and redesign their interactions with them. These findings suggest that family has the potential to act as a third space for AI learning.”

– Stefania Druga

AI literacy: Growing up with AI systems, growing used to them

Back in 2017, interest in Alexa and other so-called ‘smart’, AI-based devices was just developing in the public, and such devices would have been very novel to most people. That year, Stefania and colleagues conducted a first pilot study of children’s and their parents’ interactions with ‘smart’ devices, including robots, talking dolls, and the sort of voice assistants we are used to now.

A slide from Stefania Druga's AI literacy seminar. Content is described in the blog text.
A slide from Stefania’s AI literacy seminar. Click to enlarge.

Working directly with families, the researchers explored the level of understanding that children had about ‘smart’ devices, and were surprised by the level of insight very young children had into the potential of this type of technology.

In this AI literacy pilot study, Stefania and her colleagues found that:

  • Children perceived AI-based agents (i.e. ‘smart’ devices) as friendly and truthful
  • They treated different devices (e.g. two different Alexas) as completely independent
  • How ‘smart’ they found the device was dependent on age, with older children more likely to describe devices as ‘smart’

AI literacy: Influence of parents’ perceptions, influence of talking dolls

Stefania’s next study, undertaken in 2018, showed that parents’ perceptions of the implications and potential of ‘smart’ devices shaped what their children thought. Even when parents and children were interviewed separately, if the parent thought that, for example, robots were smarter than humans, then the child did too.

A slide from Stefania Druga's AI literacy seminar.
A slide from Stefania’s AI literacy seminar. Click to enlarge.

Another part of this study showed that talking dolls could influence children’s moral decisions (e.g. “Should I give a child a pillow?”). In some cases, these ‘smart’ toys would influence the child more than another human. Some ‘smart’ dolls have been banned in some European countries because of security concerns. In the light of these concerns, Stefania pointed out how important it is to help children develop a critical understanding of the potential of AI-based technology, and what its fallibility and the limits of its guidance are.

A slide from Stefania Druga's AI literacy seminar.
A slide from Stefania’s AI literacy seminar. Click to enlarge.

AI literacy: Programming ‘smart’ devices, algorithmic bias

Another study Stefania discussed involved children who programmed ‘smart’ devices. She used the children’s drawings to find out about their mental models of how the technology worked.

She found that when children had the opportunity to train machine learning models or ‘smart’ devices, they became more sceptical about the appropriate use of these technologies and asked better questions about when and for what they should be used. Another finding was that children and adults had different ideas about algorithmic bias, particularly relating to the meaning of fairness.

A parent and child work together at a Raspberry Pi computer.

AI literacy: Kinaesthetic activities, sharing discussions

The final study Stefania talked about was conducted with families online during the pandemic, when children were learning at home. 15 families, with in total 18 children (ages 5 to 11) and 16 parents, participated in five weekly sessions. A number of learning activities to demonstrate features of AI made up each of the sessions. These are all available at aiplayground.me.

A slide from Stefania Druga's AI literacy seminar, describing two research questions about how children and parents learn about AI together, and about how to design learning supports for family AI literacies.
A slide from Stefania’s AI literacy seminar. Click to enlarge.

The fact that children and parents, or other family members, worked through the activities together seemed to generate fruitful discussions about the usefulness of AI-based technology. Many families were concerned about privacy and what was happening to their personal data when they were using ‘smart’ devices, and also expressed frustration with voice assistants that couldn’t always understand the way they spoke.

A slide from Stefania Druga's AI literacy seminar. Content described in the blog text.
A slide from Stefania’s AI literacy seminar. Click to enlarge.

In one of the sessions, with a focus on machine learning, families were introduced to a kinaesthetic activity involving moving around their home to train a model. Through this activity, parents and children had more insight into the constraints facing machine learning. They used props in the home to experiment and find out ways of training the model better. In another session, families were encouraged to design their own devices on paper, and Stefania showed some examples of designs children had drawn.

A slide from Stefania Druga's AI literacy seminar. Content described in the blog text.
A slide from Stefania’s AI literacy seminar. Click to enlarge.

This study identified a number of different roles that parents or other adults played in supporting children’s learning about AI, and found that embodied and tangible activities worked well for encouraging joint work between children and their families.

Find out more

You can catch up with Stefania’s seminar below in the video, and download her presentation slides.

More about Stefania’s work can be learned in her paper on children’s training of ML models and also in her latest paper about the five weekly AI literacy sessions with families.

Recordings and slides of all our previous seminars on AI education are available online for you, and you can see the list of AI education resources we’ve put together based on recommendations from seminar speakers and participants.

Join our next free research seminar

We are delighted to start a new seminar series on cross-disciplinary computing, with seminars in May, June, July, and September to look forward to. It’s not long now before we begin: Mark Guzdial will speak to us about task-specific programming languages (TSP) in history and mathematics classes on 3 May, 17.00 to 18.30pm local UK time. I can’t wait!

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