Artificial intelligence, machine learning and data science
Over 2021 and 2022, we hosted a series of seminars to hear from researchers working on topics related to how to teach school-age learners about AI, machine learning, and data science. Watch recordings, read our summary blogs, and download speakers' slides.
Democratizing AI education with and for families (1 March 2022)
Speaker: Stefania Druga (University of Washington)
Children are now growing up with AI and we are slowly transitioning from a digital generation to an AI generation. Since 2017 Stefania has conducted research to explore how children interact with and make sense of the growing collection of “smart” inter-connected playthings in the world around them. Her findings uncover how children, as they play with these new devices, develop new ways of thinking about intelligence, emotion, and social interaction. She and her co-researchers also proposed guidelines and curriculum for teachers and parents to best support youth to develop a critical understanding of algorithmic bias and demystify AI capabilities. In this seminar, Stefania presented findings from the most recent international studies they conducted and also presented their open-source education tools such as Cognimates and curriculum.
Stefania Druga is currently a third-year Ph.D. candidate at the University of Washington Information School. Her research focuses on AI Literacy and the design of new computing platforms for children and parents. She also enjoys designing and building future smart toys and games. She is a Weizenbaum Research Fellow and awardee of the Jacobs Foundation Grant. She was previously a LEGO Papert Fellow during her time as a master’s student at MIT researching with Professor Mitch Resnick and the Scratch team. For more information, please have a look at her projects, papers, or resume.
Teaching youth to use AI to tackle the Sustainable Development Goals (1 February 2022)
Speaker: Tara Chklovski (Technovation)
Technovation is a global technology education nonprofit, empowering girls and underserved communities to tackle local, community problems using mobile and AI technologies. In this talk, Tara shared lessons on how to inspire and support youth to develop innovative solutions to complex real-world problems, in particular identifying areas for climate action.
Tara Chklovski is CEO of Technovation, a nonprofit that has empowered 300,000 participants from underserved communities in 100+ countries to tackle local problems using cutting-edge technologies (mobile and AI). She has been featured in the award-winning documentary Codegirl, and named “the pioneer empowering the incredible tech girls of the future” by Forbes. She has led the Global Online Education Taskforce to address education needs during COVID, the 2019 education track at the UN’s AI for Good Global Summit, presented at the International Joint Conference on AI, and the Global Partnership on AI for Humanity convened by the French Government.
Teaching Artificial Intelligence in K-12 (11 January 2022)
Speakers: Dave Touretzky (Carnegie Mellon University, AI4K12 Initiative) and Fred Martin (University of Massachusetts Lowell, AI4K12 Initiative)
What should K-12 students know about artificial intelligence, and what should they be able to do with it? The AI4K12 Initiative (AI4K12.org) is a joint project of the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA), with funding from the US National Science Foundation. AI4K21.org is developing national guidelines for teaching AI in K-12. Their work began with the release of a list of “Five Big Ideas in AI”, described in a poster that is now available in 15 languages. The guidelines themselves are organised as a series of progression charts, one for each big idea, covering four grade bands: K-2, 3-5, 6-8, and 9-12. In this talk, Dave and Fred described some of the key insights into AI that they hope children will acquire, and how they see K-12 AI education evolving over the next few years.
David S. Touretzky is a Research Professor in the Computer Science Department and the Neuroscience Institute at Carnegie Mellon University. He is also the founder and chair of the AI4K12 Initiative (AI4K12.org). Dr. Touretzky’s 40-year research career spans work in knowledge representation, artificial neural networks, computational neuroscience, autonomous mobile robots, and computer science education. He is a Senior Member of Association for the Advancement of Artificial Intelligence, a Fellow of the American Association for the Advancement of Science, and was named a Distinguished Scientist by the Association for Computing Machinery.
Dr Fred Martin is professor of Computer Science and associate dean for Teaching, Learning, and Undergraduate Studies for the Kennedy College of Sciences at the University of Massachusetts Lowell. Martin’s research group, the Engaging Computing Group, develops and studies novel computational design environments for learners, empowering them to create meaningful, personally satisfying projects. Martin is presently co-leading an NSF-funded researcher-practitioner partnership, “CS Pathways RPP: A District Ownership-based Approach to Middle School Computer Science” with SUNY Albany and three urban school districts (two in Massachusetts, and one in New York State). Martin is a past chair of the Computer Science Teachers Association (CSTA), served on Massachusetts’ Digital Literacy and Computer Science Standards Panel, and was a founding member of the AI4K12 Initiative’s steering committee.
What is it about AI that makes it useful for teachers and learners? (7 December 2021)
Speaker: Rose Luckin (University College London)
There are many ways in which AI can be used to support the teaching and learning process. For example, adaptive tutors and tutoring platforms help deliver one-to-one tutoring in particular subjects, and across the curriculum, voice-activated interfaces allow people to interact without needing to use a keyboard, and recommender systems help teachers to find the most suitable resources for their students quickly and effectively. However, for teachers to know exactly how to use AI with a group of students and for students to know how best to use AI to meet their requirements, they all need to understand something about AI. For this reason Rose and her team have developed the concept of AI Readiness as a framework to support their conversations with teachers about AI and to underpin a training course aimed at providing teachers and students with a contextualised course about AI, specifically designed for people within education and training. The aim is that the course will help increase confidence within teachers and learners and enable them to make better decisions about the way they apply AI in their practice. In this talk, Rose discussed some examples of the work that she and her team have done with educational organisations using the AI readiness framework and explained the structure of the AI Readiness course they have developed. In the process, Rose explained what it is about AI that makes it useful in education and how to know if the AI you are looking at or interacting with is likely to be useful to you.
Rosemary (Rose) Luckin is Professor of Learner Centred Design at UCL Knowledge Lab. She was named one of the 20 most influential people in education in the Seldon List, 2017. Rose is Founder of EDUCATE Ventures Research Ltd., a London hub for start-ups, researchers and educators developing evidence-based educational technology. She is past president and current treasurer of the International Society for AI in Education and co-founder of the Institute for Ethical AI in Education. Rose’s 2018 book, Machine Learning and Human Intelligence: The Future of Education for the 21st Century describes how AI supports teaching and learning. Prior to joining Knowledge Lab in 2006, Rose was Pro-Vice Chancellor for Teaching and Learning at the University of Sussex.
ML education for K-12: emerging trajectories (2 November 2021)
Speakers: Matti Tedre and Henriikka Vartiainen (University of Eastern Finland)
Over the past decades, practical applications of machine learning (ML) techniques have shown the potential of data-driven approaches in computing. ML education has been primarily piloted in computing curricula in higher education, but increasingly in K-12 computing education, too. However, despite the central position of machine learning in the field of modern computing, the computing education research body of literature contains remarkably few studies of how people learn to train, test, improve, and deploy machine learning systems. This is especially true of the K-12 curriculum space. This talk mapped the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education. It situated that research in the broader context of computing education, and described what changes ML necessitates in the classroom. The talk outlined the paradigm shift that will be required in order to successfully integrate machine learning into the broader K-12 computing curricula. A crucial step is abandoning many tenets of rule-based “classical” programming.
Dr Matti Tedre is a professor of computer science, especially computing education and the philosophy of computer science, at the University of Eastern Finland. His 2019 book “Computational Thinking” (The MIT Press, with P.J. Denning) presented a rich picture of computing’s disciplinary ways of thinking and practicing, and his 2014 book “Science of Computing” (Taylor & Francis / CRC Press) portrayed the conceptual and technical history of computing as a discipline.
Dr Henriikka Vartiainen is a senior researcher and university lecturer at the University of Eastern Finland, School of Applied Educational Science and Teacher Education. She has also worked as responsible researcher in several multidisciplinary projects focusing on, for example, technology education, co-design in school context, design-oriented pedagogy, and 21st skills. Currently, her research focuses especially on learning Machine Learning through co-design as well as on the ways to support children’s data agency. Her work on design-oriented pedagogy has received The Doctoral Dissertation Award 2014 by The Finnish Educational Research Association (FERA) as well as Young Researcher Award of the UEF in 2015.
Exploring the data-driven world: Teaching AI and ML from a data-centric perspective (5 October 2021)
Speakers: Carsten Schulte, Yannik Fleischer and Lukas Höper (Paderborn University)
The talk raised the question of whether and how AI and ML should be taught differently from other themes in the CS curriculum at school. The tentative answer is that these topics require a paradigm shift for some teachers and that this shift has to do with the changed role of algorithms, of data, and of the societal context. The talk presented three small teaching examples from middle schools to illuminate the possible differences in teaching. The first example drew upon the Matchbox computer and successors like the sweet learning computer to teach the machine learning process, the second was about enactive teaching of Decision Trees, and the third about analysing location data. (Note: please have a fruit, ideally an apple, at hand during the presentation for some interactive elements!)
Dr Carsten Schulte is a professor of computing education research at Paderborn University, Germany. His work and research interests are the philosophy of computing education and empirical research into teaching-learning processes (including eye movement research). Since 2017, he has been working together with Didactics of Mathematics (Paderborn University) in the ProDaBi project, in which Data Science and Artificial Intelligence are prepared as teaching topics. He is also PI in the collaborative research centre ‘Constructing Explainability’ on explainable AI.
Yannik Fleischer is a PhD student in mathematics education research at Paderborn University, Germany. His main research interest is to develop a concept to teach machine learning methods in school with a focus on decision trees, and to evaluate this by developing and examining teaching materials in practice. Since 2019, he has been supervising year-long project courses on data science in upper secondary and developing, implementing, and evaluating teaching modules for different levels in secondary school, mainly about machine learning with decision trees.
Lukas Höper is a PhD student in computing education research at Paderborn University, Germany. His main research interest is to develop the concept of data awareness for computing education and evaluate this by developing and examining teaching materials in practice. Since 2020, he has been working on data awareness in the ProDaBi project, among other topics on AI and Data Science in schools.
AI ethics and engagement with children and young people (7 September 2021)
Speaker: Mhairi Aitken (The Alan Turing Institute)
While recent years have brought significant and growing interest in ethical considerations relating to AI and machine learning, CS education or STEM outreach programmes typically focus on technical dimensions. This seminar set out the importance of embedding ethics at the heart of education and youth engagement relating to AI. Embedding ethics throughout all CS education is vital to ensure that future technologies and AI-powered services are designed to maximise the value and societal benefits of AI while avoiding potential negative impacts. This seminar provided a brief overview and background to current debates around AI ethics, setting out key ethical principles and how they apply to AI, before discussing the ways in which these relate to children and young people. The talk drew on current research being undertaken in the Public Policy Programme at The Alan Turing Institute to illustrate opportunities and approaches to engage children and young people with this important topic. Moreover, it discussed the importance of engaging with children and young people to inform ethical practice.
Dr Mhairi Aitken is an Ethics Fellow in the Public Policy Programme at The Alan Turing Institute. She is a Sociologist whose research examines social and ethical dimensions of digital innovation particularly relating to uses of data and AI. Mhairi has a particular interest in the role of public engagement in informing ethical data practices. Her past research has focussed in particular on the role of machine learning in finance; governance of data-intensive health research; ethical considerations around secondary uses of health data and; planning and development processes relating to renewable energy projects. Prior to joining the Turing Institute, Mhairi was a Senior Research Associate at Newcastle University. Between 2009 and 2018 Mhairi was a Research Fellow at the University of Edinburgh where she undertook a programme of research and public engagement to explore social and ethical dimensions of data-intensive health research. She held roles as a Public Engagement Research Fellow in both the Farr Institute of Health Informatics Research and the Scottish Health Informatics Programme (SHIP).
Seminar resources
We have collected resources and links from the seminar series, including contributions from speakers, participants, and the Raspberry Pi Foundation.
View the resources