How do you best teach programming in school? It’s one of the core questions for primary and secondary computing teachers. That’s why we’re making it the focus of our free online seminars in 2024. You’re invited to attend and hear about the newest research about the teaching and learning of programming, with or without AI tools.
Building on the success and the friendly, accessible session format of our previous seminars, this coming year we will delve into the latest trends and innovative approaches to programming education in school.
Our online seminars are for everyone interested in computing education
Our monthly online seminars are not only for computing educators but also for everyone else who is passionate about teaching young people to program computers. The seminar participants are a diverse community of teachers, technology enthusiasts, industry professionals, coding club volunteers, and researchers.
With the seminars we aim to bridge the gap between the newest research and practical teaching. Whether you are an educator in a traditional classroom setting or a mentor guiding learners in a CoderDojo or Code Club, you will gain insights from leading researchers about how school-age learners engage with programming.
What to expect from the seminars
Each online seminar begins with an expert presenter delivering their latest research findings in an accessible way. We then move into small groups to encourage discussion and idea exchange. Finally, we come back together for a Q&A session with the presenter.
Here’s what attendees had to say about our previous seminars:
“As a first-time attendee of your seminars, I was impressed by the welcoming atmosphere.”
“[…] several seminars (including this one) provided valuable insights into different approaches to teaching computing and technology.”
“I plan to use what I have learned in the creation of curriculum […] and will pass on what I learned to my team.”
“I enjoyed the fact that there were people from different countries and we had a chance to see what happens elsewhere and how that may be similar and different to what we do here.”
January seminar: AI-generated Parson’s Problems
Computing teachers know that, for some students, learning about the syntax of programming languages is very challenging. Working through Parson’s Problem activities can be a way for students to learn to make sense of the order of lines of code and how syntax is organised. But for teachers it can be hard to precisely diagnose their students’ misunderstandings, which in turn makes it hard to create activities that address these misunderstandings.
At our first 2024 seminar on 9 January, Dr Barbara Ericson and Xinying Hou (University of Michigan) will present a promising new approach to helping teachers solve this difficulty. In one of their studies, they combined Parsons Problems and generative AI to create targeted activities for students based on the errors students had made in previous tasks. Thus they were able to provide personalised activities that directly addressed gaps in the students’ learning.
Sign up now to join our seminars
All our seminars start at 17:00 UK time (18:00 CET / 12:00 noon ET / 9:00 PT) and are held online on Zoom. To ensure you don’t miss out, sign up now to receive calendar invitations, and access links for each seminar on the day.
Since launching our free online courses about computing on the edX platform back in August, we’ve been training course facilitators and analysing the needs of educators around the world. We want every course participant to have a great experience learning with us — read on to find out what we’re doing right now and into 2024 to ensure this.
Online courses for all adults who support young people
Educators of all kinds are key for supporting children and young people to engage with computing technology and develop digital skills. You might be a professional teacher, or a parent, volunteer, youth worker, librarian… there are so many roles in which people share knowledge with young learners.
That’s why our online courses are designed to support any kind of educator to:
Understand the full breadth of topics within computing
Discover how to introduce computing to young people in clear and exciting ways that are grounded in the latest research
We are constantly improving our online courses based on your feedback, the latest education research, and the insights our team members gain through supporting you on your course learning journeys. Three principles guide these improvements: accessibility, scalability, and sustainability.
Making our courses more relevant and accessible
Our online courses are used by people who live around the world and bring various knowledge and experiences. Some participants are classroom teachers, others have computing experience from their job and want to volunteer at a kids’ coding club, and some may be parents who want to support their children. It’s important to us that our courses are relevant and accessible to all kinds of adult learners.
We’re currently working to:
Simplify the English in the courses for participants who speak it as a second language
Adapt the course activities for specific settings where participants help young people learn so that e.g. teachers see how the activities work in the classroom, and volunteers who run coding clubs see how they work in club sessions
Ensure our course facilitators have experience in a range of different settings including coding clubs, and in a variety of different contexts around the world
Making our courses useful for more groups of people
When we think about the scalability of our courses, we think about how to best support as many educators around the world as possible. If we can make the jobs of all educators easier, whatever their setting is like, then we are making the right choices.
We’re currently working to:
Talk with the global network of educators we’re a part of to better understand what works for them so we can reflect that in the courses
Include a wider range of examples for settings beyond the classroom in the courses
Adapt our courses so they are relevant to participants with various needs while sustaining the high quality of the overall learning experience
Making the learning from our courses sustainable
The educators who take our courses work to achieve amazing things, and this means they are often busy. That they take the time to complete one of our courses to learn new things is a commitment we want to make sure is rewarded. The learning you get from participating in our online courses should continue to benefit you far beyond the time you spend completing it. This is what we mean by sustainability.
We’re currently working to:
Lay out clear learning pathways so you can build on the knowledge you gain in one course in the next course
Offer course resources that are easy to access after you’ve completed the course
Explore ways to build communities around our courses where you can share successes and learning outcomes with your fellow participants
Learn with us, and help us design better courses for you
Our work to improve the accessibility, scalability, and sustainability of our courses will continue into 2024, and these three principles will likely be part of our online training strategy for the following year too.
If you’d like to support young people in your life to learn about computing and digital technologies, take one of our free courses now and learn something new. We have twenty courses available right now and they are totally free.
We are also looking for adult testers for new course content. So if you’re any kind of educator and would like to test upcoming online course content and share your feedback and experiences, please send us a message with the subject ‘Educator training’.
We all know that learning to program, and specifically learning how to debug or fix code, can be frustrating and leave beginners overwhelmed and disheartened. In a recent blog article, our PhD student Lauria at the Raspberry Pi Computing Education Research Centre highlighted the pivotal role that teachers play in shaping students’ attitudes towards debugging. But what about teachers who are coding novices themselves?
In many countries, primary school teachers are holistic educators and often find themselves teaching computing despite having little or no experience in the field. In a recent seminar of our series on computing education for primary-aged children, Luisa Greifenstein told attendees that struggling with debugging and negative attitudes towards programming were among the top ten challenges mentioned by teachers.
Luisa is a researcher at the University of Passau, Germany, and has been working closely with both teacher trainees and experienced primary school teachers in Germany. She’s found that giving feedback to students can be difficult for primary school teachers, and especially for teacher trainees, as programming is still new to them. Luisa’s seminar introduced a tool to help.
A unique approach: Visualising debugging with LitterBox
To address this issue, the University of Passau has initiated the primary::programming project. One of its flagship tools, LitterBox, offers a unique solution to debugging and is specifically designed for Scratch, a beginners’ programming language widely used in primary schools.
You can upload Scratch program files to LitterBox to analyse them. Click to enlarge.
LitterBox serves as a static code debugging tool that transforms code examination into an engaging experience. With a nod to the Scratch cat, the tool visualises the debugging of Scratch code as checking the ‘litterbox’, categorising issues into ‘bugs’ and ‘smells’:
Bugs represent code patterns that have gone wrong, such as missing loops or specific blocks
Smells indicate that the code couldn’t be processed correctly because of duplications or unnecessary elements
The code patterns LitterBox recognises. Click to enlarge.
What sets LitterBox apart is that it also rewards correct code by displaying ‘perfumes’. For instance, it will praise correct broadcasting or the use of custom blocks. For every identified problem or achievement, the tool provides short and direct feedback.
LitterBox also identifies good programming practice. Click to enlarge.
Luisa and her team conducted a study to gauge the effectiveness of LitterBox. In the study, teachers were given fictitious student code with bugs and were asked to first debug the code themselves and then explain in a manner appropriate to a student how to do the debugging.
The results were promising: teachers using LitterBox outperformed a control group with no access to the tool. However, the team also found that not all hints proved equally helpful. When hints lacked direct relevance to the code at hand, teachers found them confusing, which highlighted the importance of refining the tool’s feedback mechanisms.
Despite its limitations, LitterBox proved helpful in another important aspect of the teachers’ work: coding task creation. Novice students require structured tasks and help sheets when learning to code, and teachers often invest substantial time in developing these resources. While LitterBox does not guide educators in generating new tasks or adapting them to their students’ needs, in a second study conducted by Luisa’s team, teachers who had access to LitterBox not only received support in debugging their own code but also provided more scaffolding in task instructions they created for their students compared to teachers without LitterBox.
How to maximise the impact of new tools: use existing frameworks and materials
One important realisation that we had in the Q&A phase of Luisa’s seminar was that many different research teams are working on solutions for similar challenges, and that the impact of this research can be maximised by integrating new findings and resources. For instance, what the LitterBox tool cannot offer could be filled by:
Pedagogical frameworks to enhance teachers’ lessons and feedback structures. Frameworks such as PRIMM (Predict, Run, Investigate, Modify, and Make) or TIPP&SEE for Scratch projects (Title, Instructions, Purpose, Play & Sprites, Events, Explore) can serve as valuable resources. These frameworks provide a structured approach to lesson design and teaching methodologies, making it easier for teachers to create engaging and effective programming tasks. Additionally, by adopting semantic waves in the feedback for teachers and students, a deeper understanding of programming concepts can be fostered.
Existing courses and materials to aid task creation and adaptation. Our expert educators at the Raspberry Pi Foundation have not only created free lesson plans and courses for teachers and educators, but also dedicated non-formal learning paths for Scratch, Python, Unity, web design, and physical computing that can serve as a starting point for classroom tasks.
Exploring innovative ideas in computing education
As we navigate the evolving landscape of programming education, it’s clear that innovative tools like LitterBox can make a significant difference in the journey of both educators and students. By equipping educators with effective debugging and task creation solutions, we can create a more positive and engaging learning experience for students.
If you’re an educator, consider exploring how such tools can enhance your teaching and empower your students in their coding endeavours.
You can watch the recording of Luisa’s seminar here:
If you’re interested in the latest developments in computing education, join us at one of our free, monthly seminars. In these sessions, researchers from all over the world share their innovative ideas and are eager to discuss them with educators and students. In our December seminar, Anaclara Gerosa (University of Edinburgh) will share her findings about how to design and structure early-years computing activities.
This will be the final seminar in our series about primary computing education. Look out for news about the theme of our 2024 seminar series, which are coming soon.
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 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.
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.”
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.
“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.
“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.
“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.
On September 29 2023, amidst much excitement and enthusiasm, a significant event took place at a unique school in Moinabad, Telangana: the teams of the Raspberry Pi Foundation and Telangana Social Welfare Residential Educational Institutions Society (TSWREIS) gathered to celebrate our partnership on the esteemed Coding Academy of TSWREIS.
This event marked a special project for us where we are piloting a distinctive, progression-based computing curriculum in a government school and a degree college in India.
Partnering with TSWREIS to bring computing education to Telangana
At the Foundation, our goal is to work closely with schools, tailoring our offerings to their contexts. Our objective is to design and evaluate unique learning experiences by integrating content from our diverse range of high-quality educational products. Through these efforts, we aim to drive significant advancements in education and technology, benefiting both students and education systems across the world.
TSWREIS manages 268 residential educational institutions in Telangana, with a primary focus on delivering quality education to under-resourced young people, particularly children from scheduled castes and tribes in rural areas. Among these institutions is the Coding Academy school, located in Moinabad, which operates as a fully residential co-ed school for grades 6 to 12, accommodating around 800 students. Additionally, TSWREIS oversees another centre of excellence, the Coding Academy degree college in Shamirpet catering to 600 undergraduate female students.
We joined forces with TSWREIS to form a collaborative partnership with their Coding Academy units at both high school and college. We’re committed to sharing our expertise in computing and coding curriculum for students from Grade 6 to intermediate at the school, and across all courses at the college.
The exciting model for our partnership with TSWREIS
We took on the challenge of directly delivering a comprehensive curriculum at the Coding Academy school and college through our own educators, exclusively hired and trained for this project. This is an exciting new approach for us, because up to this point, we have never directly delivered a curriculum anywhere in the world. However, we know we have created a world-class computing curriculum for educators in formal (and non-formal) settings, and we have many years’ experience of training teachers, so we are well-prepared to face this project and its potential challenges head-on and make it a success.
To begin the project, our team members based in India conducted a thorough study of the Coding Academy students’ interests and learning levels. Based on this, our Curriculum team in the UK and India customised and localised the content in our curriculum. We will be observing the curriculum’s delivery in classrooms and collecting students’ responses, and based on this data we’ll further refine the localised curriculum.
Throughout the project’s lifespan, we’ll measure the effectiveness of our curriculum and the impact of learning on the students. To do this, we’ll collect data from classroom observations, periodic assessments, and focused group discussions with students and educators.
Starting from the second year of the project, we will build capacity within the system. In collaboration with TSWREIS, we’ll select teachers from within the organisation based on their interest and competence, and initiate their training. Our objective is that by the project’s fifth year, TSWREIS will have achieved self-sufficiency in delivering computing education to students at the Coding Academy as well as other institutions in its purview.
The promise of this project for our work in India
We began delivering lessons at the Coding Academy college and school in July, and it’s worth mentioning that it’s been a rollercoaster ride so far. We’ve been working closely with the TSWREIS team to equip both the academic units with the resources needed for seamless implementation of the project. Our India-based team has been able to ensure continuity in the project’s momentum and plug every gap, and is working tirelessly to make this big, challenging, and exciting project blossom and succeed. When it comes to the students’ energy, enthusiasm, and the sparkle in their eyes for their learning, it’s unmatched, and everyone feels proud of their achievements so far.
This work with TSWREIS holds immense importance for us, representing our dedication to shaping a brighter educational landscape especially for young people from under-resourced communities. We hope to replicate similar initiatives across various regions in India, enabling widespread access to quality education. We also aspire to take forward our initiatives in much larger dimensions for the entirety of India.
In addition to our partnership with TSWREIS, we are actively engaged in several other impactful projects in India, such as our partnership with Mo School Abhiyan in Odisha to serve the government’s schools across Odisha state, and our collaboration with Pratham Foundation, which is helping us reach under-resourced communities and furthering our commitment to enhancing educational experiences.
We look towards the future
In reflection, the voices at the launch event on September 29 echoed the anticipation and optimism that filled the air on that memorable day. Chief guests who graciously attended the event were Shri. E Naveen Nicholas, IAS, Secretary at TSWREIS & TTWREIS, and Rachel Bennett, our Managing Director at the Raspberry Pi Foundation. Heartfelt gratitude to them for their presence and blessings. We also extend our thanks to our funding partner in this work, Ezrah Charitable Trust, and our delivery partners for their invaluable support.
The energy felt on the event day continues to drive our determination to do the work that lies ahead. As we look forward to the future, our hope and the hope of both the Coding Academy team and students are aligned: hope for a brighter, technologically empowered future, where education becomes a beacon of opportunity for all.
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.
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.
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.
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.
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.
‘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.”
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:
AI literacy: What it is and how we teach it
Rethinking computer science (and possibly some other subjects)
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.
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.
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.
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.
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.”
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.
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.
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.
Block-based programming applications like Scratch and ScratchJr provide millions of children with an introduction to programming; they are a fun and accessible way for beginners to explore programming concepts and start making with code. ScratchJr, in particular, is designed specifically for children between the ages of 5 and 7, enabling them to create their own interactive stories and games. So it’s no surprise that they are popular tools for primary-level (K–5) computing teachers and learners. But how can teachers assess coding projects built in ScratchJr, where the possibilities are many and children are invited to follow their imagination?
Aim Unahalekhala
In the latest seminar of our series on computing education for primary-aged children, attendees heard about two research studies that explore the use of ScratchJr in K–2 education. The speaker, Apittha (Aim) Unahalekhala, is a graduate researcher at the DevTech Research Group at Tufts University. The two studies looked at assessing young children’s ScratchJr coding projects and understanding how they create projects. Both of the studies were part of the Coding as Another Language project, which sees computer science as a new literacy for the 21st century, and is developing a literacy-based coding curriculum for K–2.
How to evaluate children’s ScratchJr projects
ScratchJr offers children 28 blocks to choose from when creating a coding project. Some of these are simple, such as blocks that determine the look of a character or setting, while others are more complex, such as messaging blocks and loops. Children can combine the blocks in many different ways to create projects of different levels of complexity.
Selecting blocks for a ScratchJr project
At the start of her presentation, Aim described a rubric that she and her colleagues at DevTech have developed to assess three key aspects of a ScratchJr coding project. These aspects are coding concepts, project design, and purposefulness.
Coding concepts in ScratchJr are sequencing, repeats, events, parallelism, coordination, and the number parameter
Project design includes elaboration (number of settings and characters, use of speech bubbles) and originality (character and background customisation, animated looks, sounds)
The rubric lets educators or researchers:
Assess learners’ ability to use their coding knowledge to create purposeful and creative ScratchJr projects
Identify the level of mastery of each of the three key aspects demonstrated within the project
Identify where learners might need more guidance and support
The elements covered by the ScratchJr project evaluation rubric. Click to enlarge.
As part of the study, Aim and her colleagues collected coding projects from two schools at the start, middle, and end of a curriculum unit. They used the rubric to evaluate the coding projects and found that project scores increased over the course of the unit.
They also found that, overall, the scores for the project design elements were higher than those for coding concepts: many learners enjoyed spending lots of time designing their characters and settings, but made less use of other features. However, the two scores were correlated, meaning that learners who devoted a lot of time to the design of their project also got higher scores on coding concepts.
The rubric is a useful tool for any teachers using ScratchJr with their students. If you want to try it in your classroom, the validated rubric is free to download from the DevTech research group’s website.
How do young children create a project?
The rubric assesses the output created by a learner using ScratchJr. But learning is a process, not just an end outcome, and the final project might not always be an accurate reflection of a child’s understanding.
By understanding more about how young children create coding projects, we can improve teaching and curriculum design for early childhood computing education.
In the second study Aim presented, she set out to explore this question. She conducted a qualitative observation of children as they created coding projects at different stages of a curriculum unit, and used Google Analytics data to conduct a quantitative analysis of the steps the children took.
A project creation process involving iteration
Her findings highlighted the importance of encouraging young learners to explore the full variety of blocks available, both by guiding them in how to find and use different blocks, and by giving them the time and tools they need to explore on their own.
She also found that different teaching strategies are needed at different stages of the curriculum unit to support learners. This helps them to develop their understanding of both basic and advanced blocks, and to explore, customise, and iterate their projects.
Early-unit strategy:
Encourage free play to self-discover different functions, especially basic blocks
Mid-unit strategy:
Set plans on how long children will need on customising vs coding
More guidance on the advanced blocks, then let children explore
End-of-unit strategy:
Provide multiple sessions to work
Promote iteration by encouraging children to keep improving code and adding details
Teaching strategies for different stages of the curriculum
Join our next seminar on primary computing education
At our next seminar, we welcome Aman Yadav (Michigan State University), who will present research on computational thinking in primary school. The session will take place online on Tuesday 7 November at 17:00 UK time. Don’t miss out and sign up now:
From 27 to 29 September 2023, we and the University of Cambridge are hosting the WiPSCE International Workshop on Primary and Secondary Computing Education Research for educators and researchers. This year, this annual conference will take place at Robinson College in Cambridge. We’re inviting all UK-based teachers of computing subjects to apply for one of five ‘all expenses paid’ places at this well-regarded annual event.
You could attend WiPSCE with all expenses paid
WiPSCE is where teachers and researchers discuss research that’s relevant to teaching and learning in primary and secondary computing education, to teacher training, and to related topics. You can find more information about the conference, including the preliminary programme, at wipsce.org.
As a teacher at the conference, you will:
Engage with high-quality international research in the field where you teach
Learn ways to use that research to develop your own classroom practice
Find out how to become an advocate in your professional community for research-informed approaches to the teaching of computing.
We are delighted that, thanks to generous funding from a funder, we can offer five free places to UK computing teachers, covering:
The registration fee
Two nights’ accommodation at Robinson College
Up to £500 supply costs paid to your school to cover your teaching
Up to £100 travel costs
The application deadline is Wednesday 19 July.
The application details
To be eligible to apply:
You need to be a currently practising, UK-based teacher of Computing (England), Computing Science (Scotland), ICT or Digital Technologies (N. Ireland), or Computer Science (Wales)
Your headteacher needs to be able to provide written confirmation that they are happy for you to attend WiPSCE
You need to be available to attend the whole conference from Wednesday lunchtime to Friday afternoon
You need to be willing to share what you learn from the conference with your colleagues at school and with your broader teaching community, including through writing an article about your experience and its relevance to your teaching for this blog or Hello World magazine
The application form will ask your for:
Your name and contact details
Demographic and school information
Your teaching experience
A statement of up to 500 words on why you’re applying and how you think your teaching practice, your school and your colleagues will benefit from your attendance at WiPSCE (500 words is the maximum, feel free to be concise)
After the 19 July deadline, we’re aiming to inform you of the outcome of your application on Friday 21 July.
Your application will be reviewed by the 2023 WiPSCE Chairs:
Sue and Mareen will:
Use the information you share in your form, particularly in your statement
Select applicants from a mix of primary and secondary schools, with a mix of years of computing teaching experience, and from a mix of geographic areas
Join us in strengthening research-informed computing classroom practice
We’d be delighted to receive your application. Being able to facilitate teachers’ attendance at the conference is very much aligned with our approach to research. Both at the Foundation and the Raspberry Pi Computing Education Research Centre, we’re committed to conducting research that’s directly relevant to schools and teachers, and to working in close collaboration with teachers.
We hope you are interested in attending WiPSCE and becoming an advocate for research-informed computing education practice. If your application is unsuccessful, we hope you consider coming along anyway. We’re looking forward to meeting you there. In the meantime, you can keep up with WiPSCE news on Twitter.
Learning materials, teaching approaches, and the curriculum as a whole are three areas where culturally relevance is important.
In our latest research study, funded by Cognizant, we worked with 13 primary school teachers in England on adapting computing lessons to incorporate culturally relevant and responsive principles and practices. Here’s an insight into the workshop we ran with them, and what the teachers and we have taken away from it.
Adapting lesson materials based on culturally relevant pedagogy
In the group of 13 England-based primary school Computing teachers we worked with for this study:
One third were specialist primary Computing teachers, and the other two thirds were class teachers who taught a range of subjects
Some acted as Computing subject lead or coordinator at their school
Most had taught Computing for between three and five years
The majority worked in urban areas of England, at schools with culturally diverse catchment areas
In November 2022, we held a one-day workshop with the teachers to introduce culturally relevant pedagogy and explore how to adapt two six-week units of computing resources.
An example of a collaborative activity from the workshop
The first part of the workshop was a collaborative, discussion-based professional development session exploring what culturally relevant pedagogy is. This type of pedagogy uses equitable teaching practices to:
Draw on the breadth of learners’ experiences and cultural knowledge
Facilitate projects that have personal meaning for learners
Develop learners’ critical consciousness
The rest of the workshop day was spent putting this learning into practice while planning how to adapt two units of computing lessons to make them culturally relevant for the teachers’ particular settings. We used a design-based approach for this part of the workshop, meaning researchers and teachers worked collaboratively as equal stakeholders to decide on plans for how to alter the units.
We worked in four groups, each with three or four teachers and one or two researchers, focusing on one of two units of work from The Computing Curriculum for teaching digital skills: a unit on photo editing for Year 4 (ages 8–9), and a unit about vector graphics for Year 5 (ages 9–10).
In order to plan how the resources in these units of work could be made culturally relevant for the participating teachers’ contexts, the groups used a checklist of ten areas of opportunity. This checklist is a result of one of our previous research projects on culturally relevant pedagogy. Each group used the list to identify a variety of ways in which the units’ learning objectives, activities, learning materials, and slides could be adapted. Teachers noted down their ideas and then discussed them with their group to jointly agree a plan for adapting the unit.
By the end of the day, the groups had designed four really creative plans for:
A Year 4 unit on photo editing that included creating an animal to represent cultural identity
A Year 4 unit on photo editing that included creating a collage all about yourself
A Year 5 unit on vector graphics that guided learners to create their own metaverse and then add it to the class multiverse
A Year 5 unit on vector graphics that contextualised the digital skills by using them in online activities and in video games
Outcomes from the workshop
Before and after the workshop, we asked the teachers to fill in a survey about themselves, their experiences of creating computing resources, and their views about culturally relevant resources. We then compared the two sets of data to see whether anything had changed over the course of the workshop.
The workshop was a positive experience for the teachers.
After teachers had attended the workshop, they reported a statistically significant increase in their confidence levels to adapt resources to be culturally relevant for both themselves and others.
Teachers explained that the workshop had increased their understanding of culturally relevant pedagogy and of how it could impact on learners. For example, one teacher said:
“The workshop has developed my understanding of how culturally adapted resources can support pupil progress and engagement. It has also highlighted how contextual appropriateness of resources can help children to access resources.” – Participating teacher
Some teachers also highlighted how important it had been to talk to teachers from other schools during the workshop, and how they could put their new knowledge into practice in the classroom:
“The dedicated time and value added from peer discourse helped make this authentic and not just token activities to check a box.” – Participating teacher
“I can’t wait to take some of the work back and apply it to other areas and subjects I teach.” – Participating teacher
What you can expect to see next from this project
After our research team made the adaptations to the units set out in the four plans made during the workshop, the adapted units were delivered by the teachers to more than 500 Year 4 and 5 pupils. We visited some of the teachers’ schools to see the units being taught, and we have interviewed all the teachers about their experience of delivering the adapted materials. This observational and interview data, together with additional survey responses, will be analysed by us, and we’ll share the results over the coming months.
As part of the project, we observed teachers delivering the adapted units to their learners.
In our next blog post about this work, we will delve into the fascinating realm of parental attitudes to culturally relevant computing, and we’ll explore how embracing diversity in the digital landscape is shaping the future for both children and their families.
We’ve also written about this professional development activity in more detail in a paper to be published at the UKICER conference in September, and we’ll share the paper once it’s available.
Finally, we are grateful to Cognizant for funding this academic research, and to our cohort of primary computing teachers for their enthusiasm, energy, and creativity, and their commitment to this project.
We love hearing from members of the community and how they use their passion for computing and digital making to inspire others. Our community stories series takes you on a tour of the globe to meet educators and young tech creators from the USA, Iraq, Romania, and more.
For our latest story, we are in the UK with Spencer, a Computer Science teacher at King Edward VI Sheldon Heath Academy (KESH), Birmingham. After 24 years as a science teacher, Spencer decided to turn his personal passion for digital making into a career and transitioned to teaching Computer Science.
From the moment he printed his name on the screen of an Acorn Electron computer at age ten, Spencer was hooked on digital making. He’s remained a member of the digital making community throughout his life, continuing to push himself with his creations and learn new skills whenever possible. Wanting to spread his knowledge and make sure the students at his school had access to computer science, he began running a weekly Code Club in his science lab:
“Code Club was a really nice vehicle for me to get students into programming and digital making, before computer science was an option at the school. So Code Club originally ran in my science lab around the Bunsen burners and all the science equipment, and we do some programming on a Friday afternoon making LEDs flash and a little bit of Minecraft. And from that, the students really got an exciting sense of what programming and digital making could be.”
– Spencer
While running his Code Club, Spencer really embedded himself in the Raspberry Pi community, attending Raspberry Jams, engaging with like-minded people on Twitter, and continuing to rely on our free training to upskill.
When leadership at KESH began to explore introducing Computer Science to the curriculum, Spencer knew he was the right person for the job, and just where to look to make sure he had the right support:
“So when I decided to change from being a science teacher to a computer science teacher, there were loads of course options you could find online, and a lot of them required some really specific prior knowledge and skills. The Foundation’s resources take you from a complete novice, complete beginner — my very first LED flashing on and off — to being able to teach computational thinking and algorithms. So it was a really clear progression from using the Foundation resources that helped take me from a Physics teacher, who could use electricity to light and LED on, to a programmer who could teach how to use this in our digital making for our students.”
– Spencer
Thanks to the support from KESH and Spencer’s compelling can-do attitude, he was soon heading up a brand-new Computer Science department. This was met with great enthusiasm from the learners at KESH, with a willing cohort eagerly signing up for the new subject.
“It’s really exciting to see how students have embraced Computer Science as a brand-new subject at school. The take-up for our first year at GCSE was fantastic with 25 students, and this year I’ve really got students asking about, ‘Is there an option for next year, and how can I get on to it?’ Students are almost blown away by the resources now.”
– Spencer
Supporting all students
Spencer has a mission to make sure all of KESH’s learners can learn about computing, and making his lessons accessible to all means he’s become a firm favourite amongst the students for his collaborative teaching approach.
“Mr Organ teaches you, and then he just puts you in. If you do need help, you can ask people around you, or him, but he lets you make your own mistakes and learn from there. He will then give you help so you don’t make those mistakes the next time.”
Spencer’s work is shaped by his awareness that many of the learners at KESH come from under-resourced areas of Birmingham and backgrounds that are underrepresented in computing. He knows that many of them have previously had limited opportunities to use digital tools. This is something he is driven to change.
“I want my young students here, regardless of their background, regardless of their area they’ve been brought up in, to have the same experiences as all other students in the country. And the work I do with Raspberry Pi, and the work I do with Code Club, is a way of opening those doors for our young people.”
– Spencer
Share Spencer’s story and inspire other educators
As a passionate member of the Raspberry Pi Foundation community, Spencer has been counted on as a friendly face for many years, sharing his enthusiasm on training courses, at Foundation events, and as a part of discussions on Twitter. With the goal to introduce Computer Science at A level shortly, and an ever-growing collection of digital makes housed in his makerspace, Spencer shows no signs of slowing down.
If you are interested in changing your teaching path to focus on Computer Science, take a look at the free resources we have available to support you on your journey.
Help us celebrate Spencer and his dedication to opening doors for his learners by sharing his story on Twitter, LinkedIn, and Facebook.
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.
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.
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.
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:
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.
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.
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. 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.
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.
Launched six years ago, Hello World magazine is the education magazine about computing and digital making. It’s made for educators by educators, and a community of teachers around the world reads and contributes to every issue. We’re now starting a monthly Hello World newsletter to bring you more great content for computing educators while you await each new magazine issue.
A monthly newsletter for Hello World readers
The Hello World community is an amazing group of people, and we love hearing your ideas about what could make Hello World even better at supporting your classroom practice. That’s why we host a fun and informative Hello World podcast to chat with educators around the globe about all things computing and digital making, and why we regularly share some of our favourite past magazine articles online to keep the conversation on important topics going.
Now we’re starting a monthly newsletter to offer you another way to get regular computing education ideas and insights you can use in your teaching. Every month, we’ll be curating a couple of interesting Hello World articles, plus news about the free education resources, research, community stories, and events from the Foundation. You can expect bite-size summaries of all items, plus links for you to explore more in your own time.
Sign up today
Keep up with all of the education news from the Raspberry Pi Foundation and Hello World by signing up for the Hello World newsletter today.
If you’re already signed up to the Raspberry Pi LEARN newsletter, then you don’t need to do anything: this newsletter replaces LEARN and you will be automatically subscribed.
We hope you’ll enjoy the first Hello World newsletter, which we will send out this Wednesday. As always, let us know what you think of it on Twitter or Facebook, or here in the comments.
PS Remember that if you work or volunteer as an educator in the UK, you can subscribe to receive free Hello World print copies to your home or workplace.
How do we best prepare young children for a world filled with digital technology? This is the question the writers in our newest issue of Hello World respond to with inspiration and ideas for computing education in primary school.
It is vital that young children gain good digital literacy skills and understanding of computing concepts, which they can then build on as they grow up. Digital technology is here to stay, and as Sethi De Clercq points out in his article, we need to prepare our youngest learners for circumstances and jobs that don’t yet exist.
Primary computing education: Inspiration and ideas
Issue 21 of Hello World covers a big range of topics in the theme of primary computing education, including:
Cross-curricular project ideas to keep young learners engaged
Perfecting typing skills in the primary school classroom
Using picture books to introduce programming concepts to children
Toolkits for new and experienced computing primary teachers, by Neil Rickus and Catherine Archer
Explorations of different approaches to improving diversity in computing and instilling a sense of belonging from the very start of a child’s educational journey, by Chris Lovell and Peter Marshman
The issue also has useful news and updates about our work: we share insights from our primary-specialist learning managers, tell you a bit about the research presented at our ongoing primary education seminar series, and include some relevant lesson plans from The Computing Curriculum.
As always, you’ll find many other articles to support and inspire you in your computing teaching in this new issue. Topics include programming with dyslexia, exploring filter bubbles with your learners to teach them about data science, and using metaphors, similes, and analogies to help your learners understand abstract concepts.
What do you think?
This issue of Hello World focusses on primary computing education because readers like you told us in the annual readers’ survey that they’d like more articles for primary teachers.
We love to hear your ideas about what we can do to continue making Hello World interesting and relevant for you. So please get in touch on Twitter with your thoughts and suggestions.
How do we teach our youngest learners digital and computing skills? Hello World‘s issue 21 will focus on this question and all things primary school computing education. We’re excited to share this new issue with you on Tuesday 30 May. Today we’re giving you a taste by sharing an article from it, written by our own Sway Grantham.
How are you preparing young children for a world filled with digital technology? Technology use of our youngest learners is a hotly debated topic. From governments to parents and from learning outcomes to screen-time rules, everyone has an opinion on the ‘right’ approach. Meanwhile, many young children encounter digital technology as a part of their world at home. For example in the UK, 87 percent of 3- to 4-year-olds and 93 percent of 5- to 7-year-olds went online at home in 2023. Schools should be no different.
As educators, we have a responsibility to prepare learners for life in a digital world. We want them to understand its uses, to be aware of its risks, and to have access to the wide range of experiences unavailable without it. And we especially need to consider the children who do not encounter technology at home. Education should be a great equaliser, so we need to ensure all our youngest learners have access to the skills they need to realise their full potential.
Exploring technology and the world
A major aspect of early-years or kindergarten education is about learners sharing their world with each other and discovering that everyone has different experiences and does things in their own way. Using digital technology is no different.
Allowing learners to share their experiences of using digital technology both accepts the central role of technology in our lives today and also introduces them to its broader uses in helping people to learn, talk to others, have fun, and do work. At home, many young learners may use technology to do just one of these things. Expanding their use of technology can encourage them to explore a wider range of skills and to see technology differently.
In their classroom environment, these explorations can first take place as part of the roleplay area of a classroom, where learners can use toys to show how they have seen people use technology. It may seem counterintuitive that play-based use of non-digital toys can contribute to reducing the digital divide, but if you don’t know what technology can do, how can you go about learning to use it? There is also a range of digital roleplay apps (such as the Toca Boca apps) that allow learners to recreate their experiences of real-world situations, such as visiting the hospital, a hair salon, or an office. Such apps are great tools for extending roleplay areas beyond the resources you already have.
Another aspect of a child’s learning that technology can facilitate is their understanding of the world beyond their local community. Technology allows learners to explore the wider world and follow their interests in ways that are otherwise largely inaccessible. For example:
Using virtual reality apps, such as Expeditions Pro, which lets learners explore Antarctica or even the bottom of the ocean
Using augmented reality apps, such as Octagon Studio’s 4D+ cards, which make sea creatures and other animals pop out of learners’ screens
Doing a joint project with a class of children in another country, where learners blog or share ‘email’ with each other
Each of these opportunities gives children a richer understanding of the world while they use technology in meaningful ways.
Technology as a learning tool
Beyond helping children to better understand our world, technology offers opportunities to be expressive and imaginative. For example, alongside your classroom art activities, how about using an app like Draw & Tell, which helps learners draw pictures and then record themselves explaining what they are drawing? Or what about using filters on photographs to create artistic portraits of themselves or their favourite toys? Digital technology should be part of the range of tools learners can access for creative play and expression, particularly where it offers opportunities that analogue tools don’t.
Using technology is also invaluable for learners who struggle with communication and language skills. When speaking is something you find challenging, it can often be intimidating to talk to others who speak much more confidently. But speaking to a tablet? A tablet only speaks as well as you do. Apps to record sounds and listen back to them are a helpful way for young children to learn about how clear their speech is and practise speech exercises. ChatterPix Kids is a great tool for this. It lets learners take a photo of an object, e.g. their favourite soft toy, and record themselves talking about it. When they play back the recording, the app makes it look like the toy is saying their words. This is a very engaging way for young learners to practise communicating.
Technology is part of young people’s world
No matter how we feel about the role of technology in the lives of young people, it is a part of their world. We need to ensure we are giving all learners opportunities to develop digital skills and understand the role of technology, including how people can use it for social good.
This is not just about preparing them for their computing education (although that’s definitely a bonus!) or about online safety (although this is vital — see my articles in Hello World issue 15 and issue 19 for more about the topic). It’s about their right to be active citizens in the digital world.
So I ask again: how are you preparing young children for a digital world?
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The first experiences children have with learning about computing and digital technologies are formative. That’s why primary computing education should be of interest to all educators, no matter what the age of your learners is. This issue covers for example:
And there’s much more besides. So don’t miss out on this upcoming issue of Hello World — subscribe for free today to receive every PDF edition in your inbox on the day of publication.
Every day, most of us both consume and create data. For example, we interpret data from weather forecasts to predict our chances of a good weather for a special occasion, and we create data as our carbon footprint leaves a trail of energy consumption information behind us. Data is important in our lives, and countries around the world are expanding their school curricula to teach the knowledge and skills required to work with data, including at primary (K–5) level.
Kate FarrellProf. Judy Robertson
In our most recent research seminar, attendees heard about a research-based initiative called Data Education in Schools. The speakers, Kate Farrell and Professor Judy Robertson from the University of Edinburgh, Scotland, shared how this project aims to empower learners to develop data literacy skills and succeed in a data-driven world.
“Data literacy is the ability to ask questions, collect, analyse, interpret and communicate stories about data.”
– Kate Farrell & Prof. Judy Robertson
Being a data citizen
Scotland’s national curriculum does not explicitly mention data literacy, but the topic is embedded in many subjects such as Maths, English, Technologies, and Social Studies. Teachers in Scotland, particularly in primary schools, have the flexibility to deliver learning in an interdisciplinary way through project-based learning. Therefore, the team behind Data Education in Schools developed a set of cross-curricular data literacy projects. Educators and education policy makers in other countries who are looking to integrate computing topics with other subjects may also be interested in this approach.
Data citizens have skills they need to thrive in a world shaped by digital technology.
The Data Education in Schools projects are aimed not just at giving learners skills they may need for future jobs, but also at equipping them as data citizens in today’s world. A data citizen can think critically, interpret data, and share insights with others to effect change.
Kate and Judy shared an example of data citizenship from a project they had worked on with a primary school. The learners gathered data about how much plastic waste was being generated in their canteen. They created a data visualisation in the form of a giant graph of types of rubbish on the canteen floor and presented this to their local council.
Sorting food waste from lunch by type of material
As a result, the council made changes that reduced the amount of plastic used in the canteen. This shows how data citizens are able to communicate insights from data to influence decisions.
A cycle for data literacy projects
Across its projects, the Data Education in Schools initiative uses a problem-solving cycle called the PPDAC cycle. This cycle is a useful tool for creating educational resources and for teaching, as you can use it to structure resources, and to concentrate on areas to develop learner skills.
The PPDAC data problem-solving cycle
The five stages of the cycle are:
Problem: Identifying the problem or question to be answered
Plan: Deciding what data to collect or use to answer the question
Analysis: Preparing, modelling, and visualising the data, e.g. in a graph or pictogram
Conclusion: Reviewing what has been learned about the problem and communicating this with others
Smaller data literacy projects may focus on one or two stages within the cycle so learners can develop specific skills or build on previous learning. A large project usually includes all five stages, and sometimes involves moving backwards — for example, to refine the problem — as well as forwards.
Data literacy for primary school learners
At primary school, the aim of data literacy projects is to give learners an intuitive grasp of what data looks like and how to make sense of graphs and tables. Our speakers gave some great examples of playful approaches to data. This can be helpful because younger learners may benefit from working with tangible objects, e.g. LEGO bricks, which can be sorted by their characteristics. Kate and Judy told us about one learner who collected data about their clothes and drew the results in the form of clothes on a washing line — a great example of how tangible objects also inspire young people’s creativity.
As learners get older, they can begin to work with digital data, including data they collect themselves using physical computing devices such as BBC micro:bit microcontrollers or Raspberry Pi computers.
Coming soon: the recording of Kate’s and Judy’s seminar for you to watch. You can access their slides here.
Free resources for primary (and secondary) schools
For many attendees, one of the highlights of the seminar was seeing the range of high-quality teaching resources for learners aged 3–18 that are part of the Data Education in Schools project. These include:
Data 101 videos: A set of 11 videos to help primary and secondary teachers understand data literacy better.
Lesson resources: Lots of projects to develop learners’ data literacy skills. These are mapped to the Scottish primary and secondary curriculum, but can be adapted for use in other countries too.
More resources are due to be published later in 2023, including a set of prompt cards to guide learners through the PPDAC cycle, a handbook for teachers to support the teaching of data literacy, and a set of virtual data-themed escape rooms.
You may also be interested in the units of work on data literacy skills that are part of The Computing Curriculum, our complete set of classroom resources to teach computing to 5- to 16-year-olds.
Join our next seminar on primary computing education
At our next seminar we welcome Aim Unahalekhaka from Tufts University, USA,who will share research about a rubric to evaluate young learners’ ScratchJr projects. If you have a tablet with ScratchJr installed, make sure to have it available to try out some activities. The seminar will take place online on Tuesday 6 June at 17.00 UK time, sign up now to not miss out.
To find out more about connecting research to practice for primary computing education, you can see a list of our upcoming monthly seminars on primary (K–5) teaching and learning and watch the recordings of previous seminars in this series.
Broadening participation and finding new entry points for young people to engage with computing is part of how we pursue our mission here at the Raspberry Pi Foundation. It was also the focus of our March online seminar, led by our own Dr Bobby Whyte. In this third seminar of our series on computing education for primary-aged children, Bobby presented his work on ‘designing multimodal composition activities for integrated K-5 programming and storytelling’. In this research he explored the integration of computing and literacy education, and the implications and limitations for classroom practice.
Motivated by challenges Bobby experienced first-hand as a primary school teacher, his two studies on the topic contribute to the body of research aiming to make computing less narrow and difficult. In this work, Bobby integrated programming and storytelling as a way of making the computing curriculum more applicable, relevant, and contextualised.
Critically for computing educators and researchers in the area, Bobby explored how theories related to ‘programming as writing’ translate into practice, and what the implications of designing and delivering integrated lessons in classrooms are. While the two studies described here took place in the context of UK schooling, we can learn universal lessons from this work.
What is multimodal composition?
In the seminar Bobby made a distinction between applying computing to literacy (or vice versa) and true integration of programming and storytelling. To achieve true integration in the two studies he conducted, Bobby used the idea of ‘multimodal composition’ (MMC). A multimodal composition is defined as “a composition that employs a variety of modes, including sound, writing, image, and gesture/movement [… with] a communicative function”.
Storytelling comes together with programming in a multimodal composition as learners create a program to tell a story where they:
Decide on content and representation (the characters, the setting, the backdrop)
Structure text they’ve written
Use technical aspects (i.e. motion blocks, tension) to achieve effects for narrative purposes
Defining multimodal composition (MMC) for a visual programming context
Multimodality for programming and storytelling in the classroom
To investigate the use of MMC in the classroom, Bobby started by designing a curriculum unit of lessons. He mapped the unit’s MMC activities to specific storytelling and programming learning objectives. The MMC activities were designed using design-based research, an approach in which something is designed and tested iteratively in real-world contexts. In practice that means Bobby collaborated with teachers and students to analyse, evaluate, and adapt the unit’s activities.
Mapping of the MMC activities to storytelling and programming learning objectives
The first of two studies to explore the design and implementation of MMC activities was conducted with 10 K-5 students (age 9 to 11) and showed promising results. All students approached the composition task multimodally, using multiple representations for specific purposes. In other words, they conveyed different parts of their stories using either text, sound, or images.
Bobby found that broadcast messages and loops were the least used blocks among the group. As a consequence, he modified the curriculum unit to include additional scaffolding and instructional support on how and why the students might embed these elements.
Bobby modified the classroom unit based on findings from his first study
In the second study, the MMC activities were evaluated in a classroom of 28 K-5 students led by one teacher over two weeks. Findings indicated that students appreciated the longer multi-session project. The teacher reported being satisfied with the project work the learners completed and the skills they practised. The teacher also further integrated and adapted the unit into their classroom practice after the research project had been completed.
How might you use these research findings?
Factors that impacted the integration of storytelling and programming included the teacher’s confidence to teach programming as well as the teacher’s ability to differentiate between students and what kind of support they needed depending on their previous programming experience.
In addition, there are considerations regarding the curriculum. The school where the second study took place considered the activities in the unit to be literacy-light, as the English literacy curriculum is ‘text-heavy’ and the addition of multimodal elements ‘wastes’ opportunities to produce stories that are more text-based.
Bobby’s research indicates that MMC provides useful opportunities for learners to simultaneously pursue storytelling and programming goals, and the curriculum unit designed in the research proved adaptable for the teacher to integrate into their classroom practice. However, Bobby cautioned that there’s a need to carefully consider both the benefits and trade-offs when designing cross-curricular integration projects in order to ensure a fair representation of both subjects.
Can you see an opportunity for integrating programming and storytelling in your classroom? Let us know your thoughts or questions in the comments below.
Join our next seminar on primary computing education
At our next seminar, we welcome Kate Farrell and Professor Judy Robertson (University of Edinburgh). This session will introduce you to how data literacy can be taught in primary and early-years education across different curricular areas. It will take place online on Tuesday 9 May at 17.00 UK time, don’t miss out and sign up now.
Yo find out more about connecting research to practice for primary computing education, you can find other our upcoming monthly seminars on primary (K–5) teaching and learning and watch the recordings of previous seminars in this series.
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.
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 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
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.
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.
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.
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.
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.
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.
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.
In the 1950s, Alan Turing explored the central question of artificial intelligence (AI). He thought that the original question, “Can machines think?”, would not provide useful answers because the terms “machine” and “think” are hard to define. Instead, he proposed changing the question to something more provable: “Can a computer imitate intelligent behaviour well enough to convince someone they are talking to a human?” This is commonly referred to as the Turing test.
It’s been hard to miss the newest generation of AI chatbots that companies have released over the last year. News articles and stories about them seem to be everywhere at the moment. So you may have heard of machine learning (ML) chatbots such as ChatGPT and LaMDA. These chatbots are advanced enough to have caused renewed discussions about the Turing Test and whether the chatbots are sentient.
Chatbots are not sentient
Without any knowledge of how people create such chatbots, it’s easy to imagine how someone might develop an incorrect mental model around these chatbots being living entities. With some awareness of Sci-Fi stories, you might even start to imagine what they could look like or associate a gender with them.
The reality is that these new chatbots are applications based on a large language model (LLM) — a type of machine learning model that has been trained with huge quantities of text, written by people and taken from places such as books and the internet, e.g. social media posts. An LLM predicts the probable order of combinations of words, a bit like the autocomplete function on a smartphone. Based on these probabilities, it can produce text outputs. LLM chatbots run on servers with huge amounts of computing power that people have built in data centres around the world.
Our AI education resources for young people
AI applications are often described as “black boxes” or “closed boxes”: they may be relatively easy to use, but it’s not as easy to understand how they work. We believe that it’s fundamentally important to help everyone, especially young people, to understand the potential of AI technologies and to open these closed boxes to understand how they actually work.
As always, we want to demystify digital technology for young people, to empower them to be thoughtful creators of technology and to make informed choices about how they engage with technology — rather than just being passive consumers.
That’s the goal we have in mind as we’re working on lesson resources to help teachers and other educators introduce KS3 students (ages 11 to 14) to AI and ML. We will release these Experience AI lessons very soon.
Why we avoid describing AI as human-like
Our researchers at the Raspberry Pi Computing Education Research Centre have started investigating the topic of AI and ML, including thinking deeply about how AI and ML applications are described to educators and learners.
To support learners to form accurate mental models of AI and ML, we believe it is important to avoid using words that can lead to learners developing misconceptions around machines being human-like in their abilities. That’s why ‘anthropomorphism’ is a term that comes up regularly in our conversations about the Experience AI lessons we are developing.
To anthropomorphise: “to show or treat an animal, god, or object as if it is human in appearance, character, or behaviour”
Anthropomorphising AI in teaching materials might lead to learners believing that there is sentience or intention within AI applications. That misconception would distract learners from the fact that it is people who design AI applications and decide how they are used. It also risks reducing learners’ desire to take an active role in understanding AI applications, and in the design of future applications.
Examples of how anthropomorphism is misleading
Avoiding anthropomorphism helps young people to open the closed box of AI applications. Take the example of a smart speaker. It’s easy to describe a smart speaker’s functionality in anthropomorphic terms such as “it listens” or “it understands”. However, we think it’s more accurate and empowering to explain smart speakers as systems developed by people to process sound and carry out specific tasks. Rather than telling young people that a smart speaker “listens” and “understands”, it’s more accurate to say that the speaker receives input, processes the data, and produces an output. This language helps to distinguish how the device actually works from the illusion of a persona the speaker’s voice might conjure for learners.
Another example is the use of AI in computer vision. ML models can, for example, be trained to identify when there is a dog or a cat in an image. An accurate ML model, on the surface, displays human-like behaviour. However, the model operates very differently to how a human might identify animals in images. Where humans would point to features such as whiskers and ear shapes, ML models process pixels in images to make predictions based on probabilities.
Better ways to describe AI
The Experience AI lesson resources we are developing introduce students to AI applications and teach them about the ML models that are used to power them. We have put a lot of work into thinking about the language we use in the lessons and the impact it might have on the emerging mental models of the young people (and their teachers) who will be engaging with our resources.
It’s not easy to avoid anthropomorphism while talking about AI, especially considering the industry standard language in the area: artificial intelligence, machine learning, computer vision, to name but a few examples. At the Foundation, we are still training ourselves not to anthropomorphise AI, and we take a little bit of pleasure in picking each other up on the odd slip-up.
Here are some suggestions to help you describe AI better:
Avoid using
Instead use
Avoid using phrases such as “AI learns” or “AI/ML does”
Use phrases such as “AI applications are designed to…” or “AI developers build applications that…”
Avoid words that describe the behaviour of people (e.g. see, look, recognise, create, make)
Use system type words (e.g. detect, input, pattern match, generate, produce)
Avoid using AI/ML as a countable noun, e.g. “new artificial intelligences emerged in 2022”
Refer to ‘AI/ML’ as a scientific discipline, similarly to how you use the term “biology”
The purpose of our AI education resources
If we are correct in our approach, then whether or not the young people who engage in Experience AI grow up to become AI developers, we will have helped them to become discerning users of AI technologies and to be more likely to see such products for what they are: data-driven applications and not sentient machines.
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.
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.
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.
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.
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).
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.
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.
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.
We are excited to launch Ada Computer Science, the new online learning platform for teachers, students, and anyone interested in learning about computer science.
With the rapid advances being made in AI systems and chatbots built on large language models, such as ChatGPT, it’s more important than ever that all young people understand the fundamentals of computer science.
Our aim is to enable young people all over the world to learn about computer science through providing access to free, high-quality and engaging resources that can be used by both students and teachers.
A partnership between the Raspberry Pi Foundation and the University of Cambridge, Ada Computer Science offers comprehensive resources covering everything from algorithms and data structures to computational thinking and cybersecurity. It also has nearly 1000 rigorously researched and automatically marked interactive questions to test your understanding. Ada Computer Science is improving all the time, with new content developed in response to user feedback and the latest research. Whatever your interest in computer science, Ada is the place for you.
If you’re teaching or studying a computer science qualification at school, you can use Ada Computer Science for classwork, homework, and revision. Computer science teachers can select questions to set as assignments for their students and have the assignments marked directly. The assignment results help you and your students understand how well they have grasped the key concepts and highlights areas where they would benefit from further tuition. Students can learn with the help of written materials, concept illustrations, and videos, and they can test their knowledge and prepare for exams.
A comprehensive resource for computing education
Ada Computer Science builds on work we’ve done to support the English school system as part of the National Centre for Computing Education, funded by the Department for Education.
The topics on the website map to exam board specifications for England’s Computer Science GCSE and A level, and will map to other curricula in the future.
In addition, we want to make it easy for educators and learners across the globe to use Ada Computer Science. That’s why each topic is aligned to our own comprehensive taxonomy of computing content for education, which is independent of the English curriculum, and organises the content into 11 strands, including programming, computing systems, data and information, artificial intelligence, creating media, and societal impacts of digital technology.
If you are interested in how we can specifically adapt Ada Computer Science for your region, exam specification, or specialist area, please contact us.
Why use Ada Computer Science at school?
Ada Computer Science enables teachers to:
Plan lessons around high-quality content
Set self-marking homework questions
Pinpoint areas to work on with students
Manage students’ progress in a personal markbook
Students get:
Free computer science resources, written by specialist teachers
A huge bank of interactive questions, designed to support learning
A powerful revision tool for exams
Access wherever and whenever you want
In addition:
The topics include real code examples in Python, Java, VB, and C#
The live code editor features interactive coding tasks in Python
This year’s International Women’s Day (IWD) focuses on innovation and technology for gender equality. This cause aligns closely with our mission as a charity: to enable young people to realise their full potential through the power of computing and digital technologies. An important part of our mission is to shift the gender balance in computing education.
Gender inequality in the digital and computing sector
As the UN Women’s announcement for IWD 2023 says: “Growing inequalities are becoming increasingly evident in the context of digital skills and access to technologies, with women being left behind as the result of this digital gender divide. The need for inclusive and transformative technology and digital education is therefore crucial for a sustainable future.”
According to the UN, women currently hold only 2 in every 10 science, engineering, and information and communication technology jobs globally. Women are a minority of university-level students in science, technology, engineering, and mathematics (STEM) courses, at only 35%, and in information and communication technology courses, at just 3%. This is especially concerning since the WEF predicts that by 2050, 75% of jobs will relate to STEM.
We see this situation reflected in England: computer science is the secondary school subject with the largest gender gap at A level, with girls accounting for only 15% of students. That’s why over the past three years, we have run a research programme to trial ways to encourage more young women to study Computer Science. The programme, Gender Balance in Computing, has produced useful insights for designing equitable computing education around the world.
Who belongs in computing?
The UN says that “across countries, girls are systematically steered away from science and math careers. Teachers and parents, intentionally or otherwise, perpetuate biases around areas of education and work best ‘suited’ for women and men.” There is strong evidence to suggest that the representation of women and girls in computing can be improved by introducing them to computing role models such as female computing students or women in tech careers.
Presenting role models was central to the Belonging trial in our Gender Balance in Computing programme. One arm of this trial used resources developed by WISE called My Skills My Life to explore the effect of introducing role models into computing lessons for primary school learners. The trial provided opportunities for learners to speak to women who work in technology. It also offered a quiz to help learners identify their strengths and characteristics and to match them with role models who were similar to them, which research shows is more effective for increasing learners’ confidence.
“Learning about computing makes me feel good because it helps me think more about what I want to be.” — Primary school learner in the Belonging trial
“When [the resources were] showing all of the females in the jobs, nobody went ‘Oh, I didn’t know that a female could do that’, but I think they were amazed by the role of jobs and the fact it was all females doing it.“ — Primary school teacher in the Belonging trial
Learning together to give everyone a voice
When teachers and students enter a computing classroom, they bring with them diverse social identities that affect the dynamics of the classroom. Although these dynamics are often unspoken, they can become apparent in which students answer questions or succeed visibly in activities. Without intervention, a dominant group of confident speakers can emerge, and students who are not in this dominant group may lose confidence in their abilities. When teachers set collaborative learning activities that use defined roles or structured discussions, this gives a wider range of students the opportunity to speak up and participate.
Pair programming is one such activity that has been used in research studies to improve learner attitudes and confidence towards computing. In pair programming, one learner is the ‘driver’. They control the keyboard and mouse to write the code. The other learner is the ‘navigator’. They read out the instructions and monitor the code for errors. Learners swap roles regularly, so that both can participate equitably. The Pair Programming trial we conducted as part of Gender Balance in Computing explored the use of this teaching approach with students aged 8 to 11. Feedback from the teachers showed that learners found working in structured pairs engaging.
“Even those who are maybe a little bit more reluctant… those who put their hands up today and said they still prefer to work independently, they are still all engaging quite clearly in that with their pair and doing it really, really well. However much they say they prefer working independently, I think they clearly showed how much they enjoy it, engage with it. And you know they’re achieving with it — so we should be doing this.” – Primary school teacher in the Pair Programming trial
Another collaborative teaching approach is peer instruction. In lessons that use peer instruction, students work in small groups to discuss the answer to carefully constructed multiple choice questions. A whole-class discussion then follows. In the Peer Instruction trial with learners aged 12 to 13 in our Gender Balance in Computing programme, we found that this approach was welcomed by the learners, and that it changed which learners offered answers and ideas.
“I prefer talking in a group because then you get the other side of other people’s thoughts.” – Secondary school learner (female) in the Peer Instruction trial
“[…] you can have a bit of time to think for yourself then you can bounce ideas off other people.” – Secondary school learner (male) in the Peer Instruction trial
“I was very pleased that a lot of the girls were doing a lot of the talking.” – Secondary school teacher in the Peer Instruction trial
We need to do more, and sooner
Our Gender Balance in Computing research programme showed that no single intervention we trialled significantly increased girls’ engagement in computing or their intention to study it further. Combining several of the approaches we tested may be more impactful. If you’re part of an educational setting where you’d like to adopt multiple approaches at the same time, you can freely access the materials associated with the research programme (see our blog posts about the trials for links).
The research programme also showed that age matters: across Gender Balance in Computing, we observed a big difference in intent to study Computing between primary school and secondary school learners (data from ages 8–11 and 12–13). Fewer secondary school learners reported intent to study the subject further, and while this difference was apparent for both girls and boys, it was more marked for girls.
This finding from England is mirrored by a study the UN Women’s Gender Snapshot 2022 refers to: “A 2020 study of Filipina girls demonstrated that loss of interest in STEM subjects started as early as age 10, when girls began perceiving STEM careers as male-dominated and believing that girls are naturally less adept in STEM subjects. The relative lack of female STEM role models reinforced such perceptions.” That’s why it’s necessary that all primary school learners — no matter what their gender is — have a successful start in the computing classroom, that they encounter role models they can relate to, and that they are supported to engage in computing and creating with technology by their parents, teachers, and communities.
The Foundation’s vision is that every young person develops the knowledge, skills, and confidence to use digital technologies effectively, and to be able to critically evaluate these technologies and confidently engage with technological change. While making changes inside the computing classroom will be beneficial for gender equality, this is just one aspect of building an equitable digital future. We all need to contribute to creating a world where innovation and technology support gender equity.
This IWD, we invite you to share your thoughts on what equitable computing education means to you, and what you think is needed to achieve it, whether that’s in your school or club, in your local community, or in your country.
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