Research seminars

27 January: Salomey Afua Addo (University of Cambridge)

Teaching about neural networks in junior high schools in Ghana

(This is seminar is the final seminar in our 2025-2026 series on teaching about AI and data science)

In this talk, Salomey Afua Addo will take you through her three-year PhD study exploring how to teach about AI in junior high schools in Ghana (learners aged 13–16). First, she will lay the foundation by describing the Ghanaian education system through the lens of her case study. Then, she will share how she created a role play and a board game to learn about the feedforward, evaluation, and backpropagation stages of training a single-layer neural network, using simple synthetic data based on a farming scenario.

​In addition, Salomey will explore how storytelling can be a useful and engaging tool for teaching about AI ethics in a relatable way for young learners. She will also explore the perspectives and real classroom experiences of teachers as they use unplugged tools in their AI lessons, as well as the elements of teachers’ pedagogical content knowledge (PCK) required for teaching neural networks. 

Salomey Afua Addo is a third-year PhD student in the Department of Computer Science and Technology at the University of Cambridge. Her research focuses on exploring effective approaches for teaching AI concepts in junior high schools in Ghana. She employs a range of methodologies, including case study research, design-based research, thematic analysis, and general quantitative techniques. Her work engages with key themes in curriculum studies, teacher self-efficacy, and teachers’ pedagogical content knowledge, contributing to the growing field of research on AI education in the African context.

10 February: Thema Monroe-White (George Mason University)

Advancing AI for society via race-conscious algorithmic approaches

The increasing dependence on algorithmic systems across societal domains has underscored the need to address structural inequities encoded in scientific, and artificial intelligence (AI) systems. Thema Monroe-White will present her research on intersectional race and gender biases in large language models (LLMs) and scientific discourse with implications for educators and education research. She will discuss why and how emancipatory data practices, empowering algorithmic design principles, and responsible innovation can help to mitigate systemic biases in scientific- and AI-driven decision-making. By utilising critical quantitative approaches, her research not only advances scientific discovery; it prioritises the needs and well-being of marginalised people. Her interdisciplinary approach highlights the value of centering lived experiences and personal identities in computational and quantitative methodologies to ensure the benefits of science and technology are equitably distributed.

Thema Monroe-White is an Associate Professor of Artificial Intelligence, and Innovation Policy at the Schar School of Policy and Government and the Department of Computer Science (joint) at George Mason University. Her interests include bias mitigation in artificial intelligence (AI), critical quantitative and computational methods, and racial equity in social and economic systems. She is particularly concerned with understanding the pathways to empowerment for minoritised groups via AI education and emancipatory data science practices. She serves as a senior advisor for multiple nonprofit, community, and philanthropic agencies on equitable pathways in data science education, race and gender equity in the AI workforce, and fostering diversity in STEM pathways. 

17 March: Rebecca Fiebrink (University of the Arts London)

Teaching AI to creators

In this seminar, Rebecca Fiebrink will explore the questions of how and why we might teach AI for creative practitioners, including children, students, and professionals. She will describe ways to understand the broad creative value of AI beyond the headline-grabbing systems that produce fully-formed media from text prompts. This includes the use of AI in making new types of creative works, and in making creative experiences more accessible. She will demonstrate some of the tools she and her collaborators have built to scaffold AI learning and support AI use in contexts including interactive art, musical instrument design, and design ideation.

Rebecca Fiebrink is a Professor of Creative Computing at University of the Arts London. She has taught creative machine learning to university students, professional artists, members of the public, and children for over a decade. In 2016, she launched the world's first online course (MOOC) about creative machine learning. She and her collaborators have also developed numerous open-source software tools for creative machine learning (e.g., Wekinator for artists and musicians, InteractML for game and VR developers), and these are used by hundreds of thousands of creators and students worldwide.

14 April: Kathryn Jessen Eller (Data Science, AI & You (DSAIY) in Healthcare)

Teaching youth to critically evaluate AI in healthcare while learning and applying data science and machine learning skills

In this seminar, Kathryn Jessen Eller will share how the Data Science, AI and You (DSAIY) in Healthcare programme combines a semester-long high school foundations course with current event datathons to help students understand how AI works and how it impacts society. She will describe how the course introduces essential ideas, graphing, statistics, correlation, and linear regression via free online programs such as CODAP as a foundation for interpreting Python-generated pair plots, scatterplots, boxplots, and histograms. 

These visual tools and additional short engaging activities from sources like code.org help students learn to identify meaningful features, compare models, and understand supervised versus unsupervised learning. Students also learn to design, train, test, and refine a model. Kathryn will also discuss how students explore bias at every stage of the machine learning process, from data collection to model interpretation. Student collaboration with teachers, data scientists, and clinicians during DSAIY’s intergenerational healthcare datathons reinforces foundational concepts learned during the course.

Kathryn Jessen Eller is Principal Investigator of the National Science Foundation-funded Data Science, AI and You (DSAIY) in Healthcare program. Her work focuses on designing and studying data science and AI learning experiences that help high school students build statistical reasoning, interpret real-world datasets, understand core machine learning concepts, and recognise ethical and societal impacts of AI in medicine. DSAIY ensures that students are aware of their responsibility in the ethical use of AI. She collaborates with Brown University, MIT Critical Data, and Rhode Island secondary schools to broaden participation in AI and support youth in developing human-centered, critical AI literacy. 

14 July: Dan Verständig (Goethe University Frankfurt)

On critique as practice and critical computational literacy

Digital tools have increasingly been subjected to critique that exposes their biases, power dynamics, and societal implications. In consequence, educational research and interdisciplinary scholarship are not only challenged to engage with what it means to be “critical,” but also to reflect on critique itself. As computational technologies and algorithmic systems become woven into educational and civic infrastructures, the task is no longer merely to analyse their effects, but to develop frameworks that also reimagine technology’s role in education and society. 

Against this background, this talk draws on historical perspectives on the relationship between romanticism and technology and argues that, together, art and science offer productive ways to overcome misconceptions about technology and to cultivate critique as practice. Romantic framings such as innovation as destiny, artificial intelligence as “mind,” or progress as inherently beneficial do not simply simplify complex systems; they shift responsibility and render political choices as technical inevitabilities. The talk proposes Critical Computational Literacy (CCL) as an educational response that treats computational systems as shaped by cultural, historical, and normative assumptions, rather than as neutral tools with the aim of supporting imagination, agency, participation and accountable educational practices in datafied worlds.

Dan Verständig is Professor of Educational Theory and Practices of Critical Computational Literacy at the Center for Critical Computational Studies (C3S), Goethe University Frankfurt. His research addresses learning in the context of digitality, with a focus on inequality, civic participation, and creative-critical approaches to coding and data literacy. He is a founding member of the Critical Big Data and Algorithmic Literacy Network (CBDALN) and works with experimental formats such as interactive installations, data-driven art, and playful methods to make digital infrastructures visible and reflect on their societal implications. He also advises NGOs on digital literacy and information security.

8 September: Jie Chao (The Concord Consortium)

More information coming soon.

6 October (7–8:30 pm BST): Eleni Petraki and Damith Herath (University of Canberra)

Advancing robotics education: directions for school and tertiary contexts

Robotics has long fascinated researchers and learners alike, and its significance has grown exponentially with the rise of artificial intelligence and automation in everyday life. This webinar explores the evolution of robotics education within STEAM disciplines, highlighting historical developments and current innovations that address the field’s multidisciplinary nature. While historically fields such as Artificial Intelligence, computer science and robotics were distinct disciplines, research is pointing to pedagogical approaches for integrated STEAM pedagogies. Drawing on the research within the first truly interdisciplinary textbooks (Herath and St-Onge, 2022), we illustrate the redesign of an engineering and robotics curriculum from an Australian tertiary course aimed at equipping future engineers with the diverse skills demanded by a growing workforce. The session concludes with evidence-based recommendations for best practices in robotics pedagogy, emphasising theory-driven methods and interdisciplinary learning approaches.

Eleni Petraki is an Associate Professor in Curriculum Design and Human–AI Education in the Faculty of Education at the University of Canberra. She has over 26 years of experience in language teaching and teacher education, spanning curriculum design, educational research, and applied linguistics. Eleni was a Chief Investigator and recipient of an ARC Discovery National Competitive Grant (2020–2025) in the field of machine education and artificial intelligence. Her research focuses on curriculum design, education for human–AI collaboration, artificial intelligence in education, and interaction analysis and communication.

Damith Herath is a Professor of Robotics and Art at the University of Canberra and leads the Collaborative Robotics Lab, a multidisciplinary research team with strong industry partnerships. An award-winning entrepreneur and a roboticist, Professor Herath has over two decades of experience leading complex robotic integration projects. In 2011, he founded Australia's first collaborative robotics startup, which was recognised as one of Australia's most innovative young tech companies in 2014. Professor Herath has also chaired several international workshops on Robots and Art and is the lead editor of the seminal book "Robots and Art: Exploring an Unlikely Symbiosis," a significant contribution to the field featuring leading roboticists and artists.

10 November: Doreen Boyd (University of Nottingham)

More information coming soon.