If you are into tech, keeping up with the latest updates can be tough, particularly when it comes to artificial intelligence (AI) and generative AI (GenAI). Sometimes I admit to feeling this way myself, however, there was one update recently that really caught my attention. OpenAI launched their latest iteration of ChatGPT, this time adding a female-sounding voice. Their launch video demonstrated the model supporting the presenters with a maths problem and giving advice around presentation techniques, sounding friendly and jovial along the way. 

Adding a voice to these AI models was perhaps inevitable as big tech companies try to compete for market share in this space, but it got me thinking, why would they add a voice? Why does the model have to flirt with the presenter? 

Working in the field of AI, I’ve always seen AI as a really powerful problem-solving tool. But with GenAI, I often wonder what problems the creators are trying to solve and how we can help young people understand the tech. 

What problem are we trying to solve with GenAI?

The fact is that I’m really not sure. That’s not to suggest that I think that GenAI hasn’t got its benefits — it does. I’ve seen so many great examples in education alone: teachers using large language models (LLMs) to generate ideas for lessons, to help differentiate work for students with additional needs, to create example answers to exam questions for their students to assess against the mark scheme. Educators are creative people and whilst it is cool to see so many good uses of these tools, I wonder if the developers had solving specific problems in mind while creating them, or did they simply hope that society would find a good use somewhere down the line?

Whilst there are good uses of GenAI, you don’t need to dig very deeply before you start unearthing some major problems. 

Anthropomorphism

Anthropomorphism relates to assigning human characteristics to things that aren’t human. This is something that we all do, all of the time, without it having consequences. The problem with doing this with GenAI is that, unlike an inanimate object you’ve named (I call my vacuum cleaner Henry, for example), chatbots are designed to be human-like in their responses, so it’s easy for people to forget they’re not speaking to a human. 

As feared, since my last blog post on the topic, evidence has started to emerge that some young people are showing a desire to befriend these chatbots, going to them for advice and emotional support. It’s easy to see why. Here is an extract from an exchange between the presenters at the ChatGPT-4o launch and the model:

The Family Online Safety Institute (FOSI) conducted a study looking at the emerging hopes and fears that parents and teenages have around GenAI.

One quote from a teenager said:

“Some people just want to talk to somebody. Just because it’s not a real person, doesn’t mean it can’t make a person feel — because words are powerful. At the end of the day, it can always help in an emotional and mental way.”  

The prospect of teenagers seeking solace and emotional support from a generative AI tool is a concerning development. While these AI tools can mimic human-like conversations, their outputs are based on patterns and data, not genuine empathy or understanding. The ultimate concern is that this exposes vulnerable young people to be manipulated in ways we can’t predict. Relying on AI for emotional support could lead to a sense of isolation and detachment, hindering the development of healthy coping mechanisms and interpersonal relationships. 

Arguably worse is the recent news of the world’s first AI beauty pageant. The very thought of this probably elicits some kind of emotional response depending on your view of beauty pageants. There are valid concerns around misogyny and reinforcing misguided views on body norms, but it’s also important to note that the winner of “Miss AI” is being described as a lifestyle influencer. The questions we should be asking are, who are the creators trying to have influence over? What influence are they trying to gain that they couldn’t get before they created a virtual woman? 

DeepFake tools

Another use of GenAI is the ability to create DeepFakes. If you’ve watched the most recent Indiana Jones movie, you’ll have seen the technology in play, making Harrison Ford appear as a younger version of himself. This is not in itself a bad use of GenAI technology, but the application of DeepFake technology can easily become problematic. For example, recently a teacher was arrested for creating a DeepFake audio clip of the school principal making racist remarks. The recording went viral before anyone realised that AI had been used to generate the audio clip. 

Easy-to-use DeepFake tools are freely available and, as with many tools, they can be used inappropriately to cause damage or even break the law. One such instance is the rise in using the technology for pornography. This is particularly dangerous for young women, who are the more likely victims, and can cause severe and long-lasting emotional distress and harm to the individuals depicted, as well as reinforce harmful stereotypes and the objectification of women. 

Why we should focus on using AI as a problem-solving tool

Technological developments causing unforeseen negative consequences is nothing new. A lot of our job as educators is about helping young people navigate the changing world and preparing them for their futures and education has an essential role in helping people understand AI technologies to avoid the dangers. 

Our approach at the Raspberry Pi Foundation is not to focus purely on the threats and dangers, but to teach young people to be critical users of technologies and not passive consumers. Having an understanding of how these technologies work goes a long way towards achieving sufficient AI literacy skills to make informed choices and this is where our Experience AI program comes in. 

Experience AI is a set of lessons developed in collaboration with Google DeepMind and, before we wrote any lessons, our team thought long and hard about what we believe are the important principles that should underpin teaching and learning about artificial intelligence. One such principle is taking a problem-first approach and emphasising that computers are tools that help us solve problems. In the Experience AI fundamentals unit, we teach students to think about the problem they want to solve before thinking about whether or not AI is the appropriate tool to use to solve it. 

Taking a problem-first approach doesn’t by default avoid an AI system causing harm — there’s still the chance it will increase bias and societal inequities — but it does focus the development on the end user and the data needed to train the models. I worry that focusing on market share and opportunity rather than the problem to be solved is more likely to lead to harm.

Another set of principles that underpins our resources is teaching about fairness, accountability, transparency, privacy, and security (Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI) and higher education, Understanding Artificial Intelligence Ethics and Safety) in relation to the development of AI systems. These principles are aimed at making sure that creators of AI models develop models ethically and responsibly. The principles also apply to consumers, as we need to get to a place in society where we expect these principles to be adhered to and consumer power means that any models that don’t, simply won’t succeed. 

Furthermore, once students have created their models in the Experience AI fundamentals unit, we teach them about model cards, an approach that promotes transparency about their models. Much like how nutritional information on food labels allows the consumer to make an informed choice about whether or not to buy the food, model cards give information about an AI model such as the purpose of the model, its accuracy, and known limitations such as what bias might be in the data. Students write their own model cards based on the AI solutions they have created. 

What else can we do?

At the Raspberry Pi Foundation, we have set up an AI literacy team with the aim to embed principles around AI safety, security, and responsibility into our resources and align them with the Foundations’ mission to help young people to:

• Be critical consumers of AI technology
• Understand the limitations of AI
• Expect fairness, accountability, transparency, privacy, and security and work toward reducing inequities caused by technology
• See AI as a problem-solving tool that can augment human capabilities, but not replace or narrow their futures 

Our call to action to educators, carers, and parents is to have conversations with your young people about GenAI. Get to know their opinions on GenAI and how they view its role in their lives, and help them to become critical thinkers when interacting with technology. 

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Developed by us and Google DeepMind, Experience AI provides teachers with free resources to help them confidently deliver lessons that inspire and educate young people about artificial intelligence (AI) and the role it could play in their lives.

Tracy Mayhead is a computer science teacher at Arthur Mellows Village College in Cambridgeshire. She recently taught Experience AI to her KS3 pupils. In this blog post, she shares 4 key learnings from this experience.

1. Preparation saves time

The Experience AI lesson plans provided a clear guide on how to structure our lessons.

Each lesson includes teacher-facing intro videos, a lesson plan, a slide deck, activity worksheets, and student-facing videos that help to introduce each new AI concept. 

It was handy to know in advance which websites needed unblocking so students could access them. 

You can find a unit overview on the Experience AI website to get an idea of what is included in each lesson.

“My favourite bit was making my own model, and choosing the training data. I enjoyed seeing how the amount of data affected the accuracy of the AI and testing the model.” – Student, Arthur Mellows Village College, UK 

2. The lessons can be adapted to meet student’s needs 

It was clear from the start that I could adapt the lessons to make them work for myself and my students.

Having estimated times and corresponding slides for activities was beneficial for adjusting the lesson duration. The balance between learning and hands-on tasks was just right.

I felt fairly comfortable with my understanding of AI basics. However, teaching it was a learning experience, especially in tailoring the lessons to cater to students with varying knowledge. Their misconceptions sometimes caught me off guard, like their belief that AI is never wrong. Adapting to their needs and expectations was a learning curve. 

“It has definitely changed my outlook on AI. I went from knowing nothing about it to understanding how it works, why it acts in certain ways, and how to actually create my own AI models and what data I would need for that.” – Student, Arthur Mellows Village College, UK 

3. Young people are curious about AI and how it works

My students enjoyed the practical aspects of the lessons, like categorising apples and tomatoes. They found it intriguing how AI could sometimes misidentify objects, sparking discussions on its limitations. They also expressed concerns about AI bias, which these lessons helped raise awareness about. I didn’t always have all the answers, but it was clear they were curious about AI’s implications for their future.

It’s important to acknowledge that as a teacher you won’t always have all the answers especially when teaching AI literacy, which is such a new area. This is something that can be explored in a class alongside students.

There is an online course you can use that can help get you started teaching about AI if you are at all nervous.

“I learned a lot about AI and the possibilities it holds to better our futures as well as how to train it and problems that may arise when training it.” – Student, Arthur Mellows Village College, UK

4. Engaging young people with AI is important

Students are fascinated by AI and they recognise its significance in their future. It is important to equip them with the knowledge and skills to fully engage with AI.

Experience AI provides a valuable opportunity to explore these concepts and empower students to shape and question the technology that will undoubtedly impact their lives.

“It has changed my outlook on AI because I now understand it better and feel better equipped to work with AI in my working life.” – Student, Arthur Mellows Village College, UK 

What is your experience of teaching Experience AI lessons?

We completely agree with Tracy. AI literacy empowers people to critically evaluate AI applications and how they are being used. Our Experience AI resources help to foster critical thinking skills, allowing learners to use AI tools to address challenges they are passionate about. 

We’re also really interested to learn what misconceptions students have about AI and how teachers are addressing them. If you come across misconceptions that surprise you while you’re teaching with the Experience AI lesson materials, please let us know via the feedback form linked in the final lesson of the six-lesson unit.

If you would like to teach Experience AI lessons to your students, download the free resources from experience-ai.org

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As the Experience AI Challenge has closed for submissions, we would like to thank all the talented young people who participated and submitted their projects this year.

The Challenge, created by us in collaboration with Google DeepMind, guides young people under the age of 18, and their mentors, through the process of creating their own unique AI project. It encourages young people to seek out real-world problems and create possible AI-based solutions. From January to May, participants in the UK were also able to submit their projects for feedback from AI experts.

In response to the submissions, Richard Hayler, our Director of Youth Programmes commented:

“In running the Challenge, we have seen an incredible display of creativity, ingenuity, and curiosity about AI among young people. The dedication and innovation they  demonstrated in their submitted projects has been truly inspiring. The Challenge has not only showcased the immense potential of addressing problems using AI tools, but most of all the remarkable talent and dedication of the next generation of innovators.

We would also like to thank all the mentors who guided and encouraged participants throughout the Challenge for their invaluable support. Their expertise and mentorship were instrumental in the young people’s success.”

Some Challenge highlights

These are some examples of the innovative projects young people created: 

AI creation: River Water Quality Prediction App

Creator: Shreyas, age 13

What does it do:

“The model predicts how good the water quality of a river is based on several factors such as the levels of ammonium, nitrates, and dissolved oxygen.”

Who is it for:

”It can be used to tell if river water is safe to drink, or safe for life. This can also be used by authorities to decide where to deploy limited resources to purify water depending on its toxicity.”

AI creation: Coeliac Disease

Creator: Zainev, age 14–18

What does it do:

“The model aims to identify foods that contain the allergen gluten.”

Who is it for:

“It is for people with gluten allergy and/or people trying to arrange food for those with a gluten allergy, as it will easily help them identify foods that contain gluten and are not safe to eat.”

AI creation: Spacepuppy’s colour adventure

Creator: Charlotte, age 12

What does it do:

“Teaches children about colours.”

Who is it for:

“Teachers at primary schools/ nurseries.”

AI creation: Nutrify

Creator: Ishaan, age 14–18

What does it do:

“The model identifies the students’ food items through a webcam image, giving its specific nutritional information including calories, carbs, sugars and proteins.”

Who is it for:

“This model can be easily used by students to be aware of the nutritional information of their meals.”

AI creation: Flossie

Creator: Florence, age 11

What does it do:

“Identifies dressing gowns, slippers and pyjamas.”

Who is it for:

“For young children to learn different clothing.”

AI creation: Dermalyst

Creator: Vedant, age 14–18

What does it do:

“Dermalyst is an AI-based dermatologist that analyses images of your skin to check if you have any skin infection or disease and also suggests solutions.”

Who is it for:

“This app is targeted at young people but anyone could use it. It saves them from having to wait for a GP appointment.”

AI creation: Bird identifier

Creator: William, age 13

What does it do:

“It is designed to identify common garden birds native to the United Kingdom. It can identify robins, blue tits, great tits and blackbirds by their photograph.”

Who is it for:

“Bird watchers may use the app to identify the birds that they see but don’t know what they are.”

Save the date for the celebratory webinar

We would like to invite you to an online webinar on Wednesday 10 July at 4pm BST to celebrate all Experience AI Challenge participants. Click ‘notify me’ on YouTube to be notified when the webinar starts.

During the webinar, Mark Calleja from the Raspberry Pi Foundation and Rosemary Francis, Chief Scientist for High-Performance Computing at Altair, will highlight some young people’s AI creations, and discuss all things AI. You can share your questions about AI for Mark and Rosemary by filling in this form today.

Download the Experience AI Challenge resources

Once again thank you to everyone who participated in the Experience AI Challenge and submitted their projects.

If you’re interested in the Challenge, you can still download the resources and use them to create your own AI projects.

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Today, Laura James, Head of Computing and ICT at King Edward’s School in Bath, UK, shares how Experience AI has transformed how she teaches her students about artificial intelligence. This article will also appear in issue 24 of Hello World magazine, which will be available for free from 1 July and focuses on the impact of technology.

I recently delivered Experience AI lessons to three Year 9 (ages 13–14) classes of about 20 students each with a ratio of approximately 2:3 girls to boys. They are groups of keen pupils who have elected to study computing as an option. The Experience AI lessons are an excellent set of resources.

Everything you need

Part of the Experience AI resources is a series of six lessons that introduce the concepts behind machine learning and artificial intelligence (AI). There are full lesson plans with timings, clear PowerPoint presentations, and activity sheets. There is also an end-of-topic multiple choice assessment provided.

Accompanying these are interesting, well-produced videos that underpin the concepts, all explained by real people who work in the AI industry. Plus, there are helpful videos for the educators, which explain certain parts of the scheme of work — particularly useful for parts that might have been seen as difficult for non-specialist teachers, for example, setting up a project using the Machine Learning for Kids website.

Confidence delivering lessons

The clear and detailed resources meant I felt mostly confident in delivering lessons. The suggested timings were a good guideline, although in some lessons, this did not always go to plan. For example, when the pupils were enjoying investigating websites that produce images generated by a text prompt, they were keen to spend more time on this than was allocated in the lesson plan. In this case, I modified the timings on the fly and set the final task of this lesson as a homework task.

Learning about AI sparked the students’ curiosity, and it triggered a few questions that I could not answer immediately. However, I admitted this was a new area for me, and with some investigation, found answers to many of their extra questions. This shows that the topic of AI is such an inspiring and important one for the next generation, and how important it is to add this to the curriculum now before students make their own, potentially biased, opinions about it.

“I’ve enjoyed actually learning about what AI is and how it works because before I thought it was just a scary computer that thinks like a human.” – Student, King Edward’s School, UK 

Impact on learners

The pupils’ feedback from the series of lessons was unerringly positive. I felt the lessons on bias in data were particularly important. The lesson where they trained their own algorithm recognising tomatoes and apples was a key one as it gave students an immediate sense of how a flawed training data set created bias and can impact the answers from a supposedly intelligent AI tool. I hope this has changed their outlook on AI-generated results and reinforced their critical thinking skills.

Many students are now seeing the influence of AI appearing in more and more tools around them and have mentioned that a career in AI is now something they are interested in.

“I have enjoyed learning about how AI is actually programmed rather than just hearing about how impactful and great it could be.” – Student, King Edward’s School, UK 

Tips for other teachers

Clearly this topic is incredibly important, and the Experience AI series of lessons is an excellent introduction to this for key stage 3 students (ages 11–14). My tips for other educators would be:

• I delivered these to bright Year 9s and added a few more coding activities from the Machine Learning for Kids website. As these lessons stand, they could be delivered to Year 8s (ages 12–13), but perhaps Year 7s (ages 11–12) might struggle with some of the more esoteric concepts.

• Before each lesson, ensure you read the content and familiarise yourself with the lesson resources and tools used. The Machine Learning for Kids website can take a little getting used to, but it is a powerful tool that brings to life how machine learning works, and many pupils said this was their favourite part of the lessons.

• Before the lesson, ensure that the websites that you need to access are unblocked by your school’s firewall!

• I tried to add a hands-on activity each lesson, e.g. for Lesson 1, I showed the students Google’s Quick, Draw! game, which they enjoyed and has a good section on the training data used to train the AI tool to recognise the drawings.

• We also spent an extra lesson using the brilliant Machine Learning for Kids website and followed the ‘Shoot the bug’ worksheet, which allowed pupils to train an algorithm to learn how to play a simple video game.

• I also needed to have a weekly homework task, so I would either use part of the activity from the lesson or quickly devise something (e.g. research another use for AI we haven’t discussed/what ethical issues might occur with a certain use of AI). Next year, our department will formalise these to help other teachers who might deliver these lessons to set these tasks more easily.

• Equally, I needed to have a summative assessment at the end of the topic. I used some of the multiple choice questions that were provided but added some longer-answer questions and made an online assessment to allow me to mark students’ answers more efficiently.

“I have always been fascinated by AI applications and finally finding out how they work and make the decisions they do has been a really cool experience.” – Student, King Edward’s School, UK 

From comments I have had from the students, they really engaged with the lessons and appreciated the opportunity to discuss and explore the topic, which is often associated with ‘deception’ within school. It allowed them to understand the benefits and the risks of AI and, most importantly, to begin to understand how it works ‘under the hood’, rather than see AI as a magical, anthropomorphised entity that is guessing their next move.

“The best part about learning about AI was knowing the dangers and benefits associated and how we can safely use it in our day-to-day life.” – Student, King Edward’s School, UK 

As for my perspective, I really enjoyed teaching this topic, and it has earned its place in the Year 9 scheme of work for next year. 

If you’re interested in teaching the Experience AI Lessons to your students, download the resources for free today at experience-ai.org.

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Today’s blog is from Aimy Lee, Chief Operating Officer at Penang Science Cluster, part of our global partner network for Experience AI.

Artificial intelligence (AI) is transforming the world at an incredible pace, and at Penang Science Cluster, we are determined to be at the forefront of this fast-changing landscape.

The Malaysian government is actively promoting AI literacy among citizens, demonstrating a commitment to the nation’s technological advancement. This dedication is further demonstrated by the Ministry of Education’s recent announcement to introduce AI basics into the primary school curriculum, starting in 2027. 

Why we chose Experience AI

At Penang Science Cluster, we firmly believe that AI is already an essential part of everybody’s future, especially for young people, for whom technologies such as search engines, AI chatbots, image generation, and facial recognition are already deeply ingrained in their daily experiences. It is vital that we equip young people with the knowledge to understand, harness, and even create AI solutions, rather than view AI with trepidation.

With this in mind, we’re excited to be one of the first of many organisations to join the Experience AI global partner network. Experience AI is a free educational programme  offering cutting-edge resources on artificial intelligence and machine learning for teachers and students. Developed in collaboration between the Raspberry Pi Foundation and Google DeepMind, as a global partner we hope the programme will bring AI literacy to thousands of students across Malaysia.

Our goal is to demystify AI and highlight its potential for positive change. The Experience AI programme resonated with our mission to provide accessible and engaging resources tailored for our beneficiaries, making it a natural fit for our efforts.

Experience AI pilot: Results and student voices

At the start of this year, we ran an Experience AI pilot with 56 students to discover how the programme resonated with young people. The positive feedback we received was incredibly encouraging! Students expressed excitement and a genuine shift in their understanding of AI. 

Their comments, such as discovering the fun of learning about AI and seeing how AI can lead to diverse career paths, validated the effectiveness of the programme’s approach.  

One student’s changed perspective — from fearing AI to recognising its potential — underscores the importance of addressing misconceptions. Providing accessible AI education empowers students to develop a balanced and informed outlook.

“I learnt new things and it changed my mindset that AI is not going to take over the world.” – Student who took part in the Experience AI pilot

Launching Experience AI in Malaysia

The successful pilot paved the way for our official Experience AI launch in early April. Students who participated in the pilot were proud to be a part of the launch event, sharing their AI knowledge and experience with esteemed guests, including the Chief Minister of Penang, the Deputy Finance Minister of Malaysia, and the Director of the Penang State Education Department. The presence of these leaders highlights the growing recognition of the significance of AI education.

Building a vibrant AI education community

Following the launch, our immediate focus has shifted to empowering teachers. With the help of the Raspberry Pi Foundation, we’ll conduct teacher workshops to equip them with the knowledge and tools to bring Experience AI into their classrooms. Collaborating with education departments in Penang, Kedah, Perlis, Perak, and Selangor will be vital in teacher recruitment and building a vibrant AI education community.

Inspiring the next generation of AI creators

Experience AI marks an exciting start to integrating AI education within Malaysia, for both students and teachers. Our hope is to inspire a generation of young people empowered to shape the future of AI — not merely as consumers of the technology, but as active creators and innovators.

We envision a future where AI education is as fundamental as mathematics education, providing students with the tools they need to thrive in an AI-driven world. The journey of AI exploration in Malaysia has only just begun, and we’re thrilled to play a part in shaping its trajectory.

If you’re interested in partnering with us to bring Experience AI to students and teachers in your country, you can register your interest here.

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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. 

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.

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.

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.

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.

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.

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

What we’ve learned

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

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

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

Join us as an Experience AI partner

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

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

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

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