Throughout the past months, we delivered a variety of professional learning events, including AI Literacy sessions for primary and secondary teachers, reaching over 500 adults, among which teachers, researchers, education leaders, policymakers, and industry professionals.

We were speakers听at the听 webinar and听. We co-organised the听Designing EdTech for Equity听workshop in collaboration with the University of Oxford and co-hosted Professor Matti Tedre’s public lecture on Finland’s AI literacy programme Generation AI with the

The Hub co-lead Professor Judy Robertson delivered the AI and Creativity in English online session for Education Scotland鈥檚 Literacy and English Curriculum Network. You can watch the recording here: .

We are looking forward to continuing our work to establish a professional learning community for Scottish teachers interested in developing research-informed practice in digital, data, and AI literacy.

Below, you can sign up for our upcoming events:

– 30 April 2026, 16:00-17:00 BST: Digital Pedagogy
– 14 May 2026, 16:00-17:00 BST: Introduction to Assistive Technology for Universal Support
– 20 May 2026, 16:00-17:00 BST: Introduction to Assistive Technology to Support Communication

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The Centre for Teaching Excellence (CfTE) supports research and innovation in teaching practice for all children and young people with the aim of making Scotland a world-leader in teaching practice. The CfTE brings together a range of thematic hubs that will together develop both teacher and young people鈥檚 competencies to support 21st-century learning.

The Digital Education and AI hub will establish and support a thriving professional learning community for Scottish teachers interested in developing research-informed practice in digital, data, and AI literacy.

Led by our academic lead and team colleague 听the hub will provide:

1. Accessible, research-informed professional learning for teachers on digital, data, and AI literacy topics.
2. Opportunities for teachers and academics to share, discuss and reflect on their knowledge and pedagogical practices.
3. High-quality, interactive educational materials for teachers to use in their classes, supporting them in providing engaging learning experiences for their students.

鈥淲e are delighted to establish this hub and be part of the CfTE to support all Scottish teachers by sharing practice and research about digital learning and AI in education in creative, ethical and responsible ways鈥�, said Serdar Abaci.

The hub aims to reach all teachers across Scotland through an innovative pedagogical approach combining professional learning workshops and seminars with educational resources. Topics covered will include AI and Creativity in English, Supporting Schools with AI policy, Using AI Responsibly in Schools, and Supporting Inclusive Education with Digital Tools.

Alongside the Digital Education and AI Hub, two new hubs have been launched: the hub, hosted by the University of Strathclyde, and the hub, hosted by the University of Aberdeen.

Last week, Education Secretary Jenny Gilruth met teacher secondees at the听CfTE. Watch the video to learn more about their innovative approaches to improving teaching practice and making a difference for Scotland鈥檚 learners:听

To find out more about the Digital Education and AI hub, you can email听Serdar.Abaci@ed.ac.uk.

The Hub will be hosted by the University of Edinburgh , and its team will be based in the .

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The 天美视频 team have been developing this resource to give primary students an introduction to concepts in statistics and data analysis, and encourage learners to think about and gain an interest in Robotics.

The Cards allow for cross-curricular links and can be used to learn about early mathematical concepts, create simple graphs (i.e., bar charts and scatterplots), and more.

Learn more and download the Cards at this link: Robot Cards – 天美视频.

We’re looking forward to your feedback!

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Chris highlighted the as a standout element from the course,听鈥榯hey are useful even when听we鈥檙e听sharing 鈥榖oring鈥� progress and achievement data鈥�.听It really gives a different viewpoint on what听you鈥檙e听looking at right from the outset.听

In addition to the slow reveal graphs, among the practical resources he has used are the听Dragonistics听cards (鈥�probably 100 times!鈥�)* and the 天美视频 Explorer Cards听and he regularly听brings classes to听escape room events as shared by the team. With the and ways of looking at graphs and data,听鈥榠t really was beneficial because it showed the most simplistic ways of learning听about visualisation of data鈥�.听With Primary 6, for example, he has already used the approach to help them use data on different countries from world fact books; it lets them听鈥榣ook at the information in an interesting way鈥�.听

鈥楾he 天美视频 Literacy course听showed how data can be taught in a in a more interesting way and make it more accessible to a lot of learners. That really was what I got from it, but also, from my professional side as a leader, it gave me an awful lot to return to staff with and upskill them.鈥�听

With this in mind, Chris听took a pragmatic, pro-active approach to sharing with other teachers at his school.听

They started with little听ice-breakers听at the start of collegiate time, then began to set aside more dedicated collegiate sessions to sharing data literacy approaches, introducing colleagues to the resources, illustrating the practical, hands-on nature of them.听听

The key take away was that 鈥�it was the hands-on听nature,听it is more likely to be adopted if you can show a teacher that their collegiate time will directly听impact听their teaching鈥�.听The ripple effect happened as a 鈥�a sort of staged process, where a partner would see a resource that was being used like the听Dragonistics听cards, or some of these slow reveal graphs would be part of their planning. They would then share the resource. The keenness, shall we say, of the teachers was naturally picking up other people. So that small ripple effect I think would happen in any school.鈥欌�� We were lucky enough to have a team that focused on this as a professional inquiry, which then meant that we could run with it and the goal was to spread it across the school.鈥�听

So听what started as听one colleague听attending the 天美视频 Literacy professional learning course led to ripples听of data literacy teaching听across a whole school.听

鈥楩rom a personal point of view, I got a huge amount out of it 鈥� from a professional point of view, even more so.听听

* Inspired by the Dragonistics cards, we have created our own Robot Cards. You can download them at Robot Cards – 天美视频.

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As part of the , and in partnership with , and , our team are developing resources to support critical AI literacy skills to equip young people for living and working in the age of AI, and offer professional learning for teachers. You can read more here: AI in Secondary Schools – 天美视频.

In the context of this project, we have brought together a cohort of six AI Teachers in Residence from secondary schools in Scotland who will create learning resources on the use and understanding of AI across different subjects.

In order to support them, we will deliver professional learning workshops, the first of which was held on the 19^th November 2024 at the Edinburgh Futures Institute.

Learn more about our Resident teachers and the work they are conducting at their schools: AI Teachers in Residence – 天美视频.

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We’re thrilled that the session has been included and that Kate Farrell (天美视频)听will be joining Ronit Nehemia (Israel Ministry of Education, Conrad Wolfram (Wolfram), and Tue Halgreen (OECD) at the event.

SXSW is short for South by Southwest, a conference and festival that celebrates (amongst other areas) innovative technology and gaming. SXSW EDU is the education focused element听 of the annual conference and festival that takes place in Austin, Texas.

About the session

U.S. education leaders lament our relative progress to other countries, but do little to proactively collaborate with those who are charging ahead. But K-12 data science does things differently. Born from global collaboration, data science education advocates leveraged global connections to accelerate a new field from its inception. From the first-ever cross-country K-12 learning framework to international exchange of research and curricula models, join us to learn how going global made data science education a reality and what lessons we can learn from our partners across the globe.

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The article links to the Scottish Government Digital Strategy and includes quotes from teachers already engaging with the 天美视频 programme of work.

‘It is important for students to realise that they leave a significant digital footprint, and we have discussed ways to use their data effectively. This has been an effective way to integrate the topic into classroom discussions’ (Teacher)

Dr Serdar Abaci is based in the Institute of Education, Teaching and Leadership at Moray House School of Education and Sport. Serdar’s research interests include data and digital literacies, formative assessment and feedback, online learning, and evaluation of teaching and learning technologies.

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On Tuesday, primary learners signed up to be trainee Agents of DATA, working for the Defence Against Temporal Attacks to protect Scotland against the high-tech VIKING villains! They used datasets of underground bunkers, wind farms, data centres and undersea fibre-optic internet cables to work out where the evil VIKINGS were building their new secret lair!

Image: Tommy Lawson, Kate Farrell and Jasmeen Kanwal.

On Wednesday, S2 learners worked in groups to analyse a data set and create a visualisation to represent the information they had analysed.听听It certainly helped their motivation that the 鈥榙ataset鈥� was a bag of Haribo sweets and they were able to eat the sweets after they鈥檇 made their graphs!

On Thursday, teachers from across the local authority and visitors from further afield gathered together to listen to talks and try STEM activities at different stalls.听听The DES team spoke to teachers about teaching data literacy across the curriculum.听 We’re delighted that some of the teachers were so enthused they have signed up for the next professional learning sessions that the team are running. We look forward to welcoming them to our next course.

Find out more

4 day data literacy courses for primary and secondary teachers

Professional learning workshops (Edinburgh and South East Scotland schools)

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By Valentina Andries and Judy Robertson.

Smart speakers 鈥� such as Alexa, Siri and Google 天美视频 – have entered our lives and are now comfortably installed on kitchen tables across the land. A recent OfCom report notes that 83% of children have at least tried using a smart speaker, while our own research in Edinburgh found that 91% of the children in our sample had a smart speaker in their home. Because smart speakers typically use voice interactions and are designed to be conversational assistants, they emulate human-human interactions. But could talking to a smart speaker as if it is human be confusing for children? Might they then assume the technology is more intelligent and human-like than it actually is? What consequences might this have for their interactions with technology and their attention to privacy? Our current research aims to document a baseline of children鈥檚 knowledge about smart speakers and address potential misconceptions about these systems through educational materials for schools.听

We have employed a mixed-methods approach with primary-school children to investigate themes such as children鈥檚 general knowledge of Artificial Intelligence and how smart speakers work, user data management and security aspects, as well as children鈥檚 verbal interactions with their smart speakers. So far, we collected and analysed data from two state-funded primary schools in the Edinburgh area.听

It’s fascinating to learn what the children think about their smart speakers. The younger children were more inclined to assume that smart speakers are intelligent 鈥� more intelligent than themselves but not so intelligent as scientists! They gave a variety of explanations for what makes smart speakers intelligent, ranging from the view that their intelligence is the result of programming to the slightly alarming idea that a 鈥渞eally smart guy鈥� plugged himself into it and then it [Alexa] got really smart and then he plunged himself into multiple [Alexas] and now there’s an army鈥�. Not all children agreed that smart speakers were intelligent but rather that humans make them work – 鈥淚 think Alexa’s made by humans who are on a computer and then they listen to what you’re saying. And then when it says, 鈥楢lexa鈥�, it turns on and then they type something in.鈥澨�听

In terms of privacy, most of the children did not realise that others in their home, and people who work for the company which makes the smart speaker can review their commands and interactions. In fact, they were often horrified at the prospect. Some of them imagined unpleasant (but often technically inaccurate) scenarios in which sharing private information could go wrong -鈥淎lexa and Siri work together and then they could spread 鈥� then it could go viral.鈥� It is clear that the children value their privacy; we owe it to them to clearly and accurately explain the real privacy risks of using smart speakers and how they can mitigate these. As part of this, we need to educate children about the basic facts of how such technology works.听

听The ultimate goal of this project is to produce a range of educational materials as well as to collate and curate relevant resources that can be used by STEM teachers in Scotland to educate young children on how smart speaker technology works, the implications of its use, as well as foster AI literacy learning. We are adopting a participatory approach to developing such materials together with young learners and other relevant stakeholders such as specialists in education and teachers.听听

We are collating a list of resources and approaches that can be used and adapted by teachers in their classrooms. We have organised a workshop with teachers, during which we presented our findings, and we invited the teachers to a group discussion on different teaching approaches that they would adopt to address some of the children鈥檚 misconceptions or open-ended questions about AI and CAs. The insights that we gathered from that session are useful in further refining educational materials and approaches. We are also collaborating with Informatics students at the University of Edinburgh to share their knowledge of AI with young learners.听

Professor Judy Robertson

More about the project ‘Designing conversational assistants to reduce gender bias’

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I studied AI at university, and I try to keep up with the field through occasional reading. I鈥檝e just finished The Alignment Problem which has hugely extended my thinking about AI and its societal implications, through a fascinating presentation of recent breakthroughs in machine learning. The book gives a good non-technical grounding in supervised and unsupervised machine learning (ML) and reinforcement learning early in the book and uses this as a foundation for forays into very new research into AI safety. It covers the problems with bias in datasets and the perils of using predictive algorithms for policing in some detail, along with the difficulties which arise when ML algorithms don鈥檛 generate human-understandable explanations of their reasoning.

Reading the book gave me pause to consider whether the concept of computational thinking 鈥� which rose to ascendency in 2006 – needs to be updated. Christian observes that 鈥淲e are now鈥� well beyond the point where our machines can do only that which we program into them in the explicit language of math and code鈥� (p261). This is interesting because it has long been a goal of computational thinking education to explain to learners that 鈥渃omputers can only do what programmers tell them to鈥�. We may need to update the intermediate-level explanations of computational thinking to include explanations of the capabilities of machine learning. Computers indeed execute code written by humans, but with machine learning, it is not always possible to read the code and know what the output of the code will be, even for the person who wrote the program. Does this mean that probabilistic reasoning needs to become part of computational thinking? This topic should be explored further in the ongoing discussions of AI literacy for schools.

In the later sections of the book, the author explores work on training complex autonomous systems to learn appropriate behaviour (and potentially underlying human values) by observing human behaviour or working in cooperation with users. In such cases, the AI would have no goals of its own, but would try to infer what the user wishes to achieve, and figure out how to help them achieve it. There is interesting material here from cognitive science and education including studies of how even very young children can figure out how to help adults who appear to be struggling with simple everyday tasks like opening cupboards. If you鈥檙e haunted by the horrifying possibility of future AIs behaving like a deranged 鈥淐lippy鈥� (the Microsoft Office helper agent of old) , you鈥檙e not alone. The author considers the problem of a toddler-like AI trying to help but actually making things worse. He points out that AIs will likely have to serve 鈥渢wo masters鈥� 鈥� the user and the company which created it, and there are definitely times when the interests of these two masters do not coincide. There is already growing concern about software companies ruthlessly optimising the design of their products to attract and keep our attention at the expense of our health and wellbeing. Any AI with the purpose of 鈥渁ssisting鈥� a user by building a model of their preferences may not consider the longer-term interests of the user, or allow for the idea that the user might wish to change in the future. For example, people who are overcoming an addiction will not benefit from their AI assistant showing them adverts or helping them to buy products they now want to avoid, even if this results in reduced advertising revenue for the company which owns the AI. 听听Christian writes: 听鈥淚 think there is an important policy matter here, as least as much a theoretical one. We should take seriously the idea that users have a right to both see and alter any preference model that a site or app or advertiser has about them. It is worth considering regulation to this effect: to say, in essence, I have a right to my own models. I have the right to say, 鈥�That’s not who I am鈥�. Or aspirationally, 鈥楾his is who I want to be. This is the person in whose interest you must work鈥�.” p275. This is a powerful contribution to discussions of data privacy: access to (interpretable) models about oneself as well as the raw data which feeds them could be a game changer.

The author makes the point that AI researchers are not blind to the ethical implications of their work: 听鈥�An ecosystem of research and policy efforts to influence both the near and long term is underway across the globe… Research on bias, fairness, transparency and the myriad dimensions of safety now forms a substantial portion of all the work presented at major AI and machine-learning conferences. Indeed at the moment they are the most dynamic and fastest-growing areas arguably not just in computing but in all of science.鈥� p313. The book is an excellent place to start for researchers in the humanities or social sciences who wish to add their critical skills to these efforts.

This book is a must-read for students and academics who want to get to grips with the current state of AI research in non-technical terms.

By Judy Robertson 3/9/22

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