Research seminars

13 May: Henriikka Vartiainen and Matti Tedre (University of Eastern Finland)

AI as a design domain: Empowering students with no-code AI/ML app development in the classroom

In the landscape of AI education, it is important to provide students with the tools and understanding to actively participate in AI-driven environments. This talk introduces ‘GenAI Teachable Machine’, an educational platform designed for students aged 10–16 to explore core AI concepts through making practical, hands-on applications. The talk describes the educational theory, pedagogical strategies, and classroom resources developed to support teachers.

GenAI Teachable Machine is a no-code app development studio that empowers young learners who have little to no programming experience to create their own AI/ML-powered applications for mobile phones and computers. It simplifies complex machine learning (ML) processes into a user-friendly, accessible interface familiar from Google Teachable Machine. Students can independently design and build apps using their own creative ideas, designs, and data sets.

Henriikka Vartiainen is a lecturer and senior researcher at the University of Eastern Finland, School of Applied Educational Science and Teacher Education. Her research interests in AI education include data agency, co-design, design-oriented pedagogy, and the role of AI in art, craft, and design. Henriikka received her PhD in education from the University of Eastern Finland and the title of Docent in Education from University of Jyväskylä, Finland.

Matti Tedre is a professor of computer science at the School of Computing, University of Eastern Finland. His research interests include AI education, computing education research, and the history and philosophy of computer science. Tedre received a PhD from the University of Joensuu. He is an extraordinary professor at North-West University, and holds the title of docent at Stockholm University and University of Eastern Finland.

10 June: Jesús Moreno Leon (University of Seville)

How to measure AI literacy

In this seminar, Jesús Moreno-León will explore methods for measuring and evaluating artificial intelligence literacy. He will present an assessment instrument that has been successfully implemented in several research studies, involving thousands of primary and secondary education students across Spain, discussing both its strengths and limitations. 

Additionally, he will share recent progress in developing a new instrument influenced by the SEAME framework, which groups learning objectives and resources related to teaching AI and machine learning based on whether they focus on Social and Ethical aspects (SE), Applications (A), Models (M), or Engines (E — how AI works).

Jesús Moreno-León is a computer science researcher and professor at the University of Seville. His career spans diverse educational landscapes, including leadership roles at the Spanish Ministry of Education and pedagogic management of Fundación Telefónica's 42 Campus network across Spain. He is also co-director of Programamos, a non-profit organisation fostering computer science education from early ages that has won multiple awards.

8 July: Stephan Kindler and Sarah Schönbrodt (Karlsruhe Institute of Technology)

More information coming soon.

9 September: Matti Tedre (University of Eastern Finland)

More information coming soon.

14 October: Viktoriya Olari (Free University of Berlin)

Data-related concepts and practices for AI education in K–12 

Curricula around the world have started to include content related to AI into their agendas. This process is timely and important, but it is also challenging because the elaboration of the AI field for K–12 is still ongoing. Current efforts often underappreciate the critical role of data, despite it being a fundamental component of AI systems. If the goal is to enable students to understand how AI systems work and what their implications are, it is crucial to understand what data underpins these systems, and how it is collected and processed.

To advance knowledge about the role of data in AI education, Viktoriya Olari conducted a comprehensive theoretical analysis of the AI field. In this seminar, she will present the results of her work: a model of key data-related practices and a collection of key data-related concepts that are essential for understanding how AI systems are created and function. She will also share insights from a design-based research study on implementing data-related concepts and practices in AI education in schools.

Viktoriya Olari is a researcher and PhD student in the Computing Education Research Group at the Free University of Berlin, with research interests in AI and data education. She has worked on various projects educating teachers and students on AI and data literacy, such as TrainDL. She served as a member of the AI literacy expert group at the Council of Europe and is currently an advisory board member of the Data Civic Lab in Germany.

11 November: Karl-Emil Bilstrup (Copenhagen University)

Using the micro:bit to learn about machine learning practices

This seminar will present the research and design rationales behind ml-machine.org — the precursor to micro:bit’s CreateAI. This educational tool lets students build machine learning models with accelerometer data from the micro:bit. 

Karl-Emil Bilstrup will show prototypes of tangible interfaces and embodied activities that enable secondary-level students to explore data processes and machine learning practices. He will discuss how these tools can support critical reflections on the ethical design considerations and trade-offs involved in creating data-driven systems and how they can unbox aspects of modern chatbots. Further, he will describe his collaborations with teachers, schools, and other educational organisations, such as the Danish Broadcasting Corporation and the Micro:bit Educational Foundation, to integrate his tools into educational and organisational practices.

Karl-Emil Bilstrup is a postdoctoral computer science researcher and interaction designer at Copenhagen University. He is part of the Center for Computational Thinking and Design at Aarhus University, Denmark, where he did his PhD on designing educational tools for teaching machine learning in secondary education with a Scandinavian focus on sustaining students’ agency in an increasingly digitised and computationalised world. He has contributed to research in embodied interaction for learning and has been a visiting researcher in the Embodied Design Research Laboratory at UC Berkeley. He has collaborated with the Micro:bit Educational Foundation about designing CreateAI, which brings embodied activities with machine learning into classrooms.