Teaching about AI and data science

Speakers: Sarah Schönbrodt (Salzburg University), Stephan Kindler and Martin Frank (Karlsruhe Institute of Technology)

Artificial intelligence (AI) and machine learning (ML) rely on mathematical modeling. Interestingly, many of the core mathematical techniques underpinning ML are quite straightforward and accessible with high-school level mathematics. This observation suggests that AI education should not be limited to computer science courses, but should also be meaningfully integrated into mathematics curricula.

In our talk, we explored various strategies for simplifying and teaching key mathematical ideas behind support vector machines and artificial neural networks. These methods are deeply rooted in mathematical concepts from linear algebra and calculus — mathematical areas that students often find abstract or unengaging. By addressing data-driven problems from AI within the context of mathematics education, we have a unique opportunity to make mathematical concepts more relevant and exciting for students, while also fostering a deeper understanding of AI, including its risks and potential. We outlined intended learning pathways and digital learning material for high-school students and critically examined which underlying mathematical concepts can be explored in the classroom (‘white-box’ approaches), and which may need to be treated as ‘black-box’ due to their inherent complexity.

Sarah Schönbrodt is an assistant professor of mathematics education at Salzburg University. She is the founder and CEO of the non-profit organisation KI macht Schule Austria. In her research, she focuses on learning about modern applications of mathematics in school teaching, with an emphasis on applications from the field of AI (www.schoenbrodt.info/en). She also manages the Computational and Mathematical Modeling Program (CAMMP) in Salzburg, which gives high-school students insight into the relevance of modern mathematics.

Stephan Kindler is a PhD student at the Karlsruhe Institute of Technology (KIT). After completing his studies in mathematics and chemistry as well as a teaching traineeship, he began working as a teacher and started his PhD under Professor Martin Frank. His research focuses on the didactic aspects of artificial neural networks and the development of teaching and learning materials on this topic. He is also involved in the Computational and Mathematical Modelling Programme (CAMMP) in Karlsruhe.

Martin Frank is a professor for Computational Science & Mathematical Methods at the Karlsruhe Institute of Technology (KIT) and director of the Scientific Computing Center. After a junior professorship at TU Kaiserslautern, he was Professor of Mathematics at RWTH Aachen University (2009-2017). There, he founded the Computational and Mathematical Modeling Program (CAMMP) education lab. Since 2017, he has been a professor at KIT, where he is an active researcher in both mathematical methods (including AI) and mathematics education.

Speaker: Netta Iivari (University of Oulu)

The seminar introduced transformative agency and its importance for children’s computing education. It offered examples of ways to foster children’s transformative agency within their computing education, specifically focusing on how to address AI and to invite children to critically analyse and design their future with AI in it.

Netta Iivari is a Professor in Information Systems and research unit leader of the INTERACT research unit at the University of Oulu. Her long-lasting research interest concerns understanding and strengthening children’s participation in shaping and making their digital futures. She has explored participatory design, critical design, empowerment, inclusion, ethics, values and criticality in collaboration with children, working in the context of their computing education. Recently, she has focused on emerging technologies such as artificial intelligence, engaging children in critical analysis and design of AI futures.

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Henriikka Vartiainen and Matti Tedre (University of Eastern Finland)

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 introduced ‘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 described 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.

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• The no-code app development studio: Using the GenAI Teachable Machine studio, children train ML models with their chosen data, define app actions, and deploy fully functional standalone mobile apps. This fosters a deep, hands-on understanding of supervised learning and ML workflows. The studio emphasises inclusivity and accessibility with its no-code approach. It offers instant app previews for iterative testing and an eXplainable AI (XAI) feature for easier debugging. It promotes collaborative learning, allowing multiple students to co-create and contribute training data simultaneously from their individual mobile devices. It creates a line-of-sight from their ML-driven apps to their own data sets.  Research on the tool has shown it is well suited to teaching what bias is and how it forms in ML systems. Try the app development studio at tm.gen-ai.fi

• Code for GenAI Teachable Machine at Nick Pope's GitHub: https://github.com/knicos/genai-tm

• Pedagogical designs: https://doi.org/10.15388/infedu.2024.15

• Technology description: https://doi.org/10.1109/TLT.2025.3529994

Speakers: David Weintrop, Rotem Israel-Fishelson and Peter F. Moon (University of Maryland)

This seminar introduced ‘API Can Code’, an interest-driven data science curriculum for high-school students. It focused on strategies for integrating data science learning within students’ lived experiences and fostering authentic engagement. Drawing on insights from classroom implementations, the speakers explored methods for designing contextually relevant and meaningful educational experiences in data science.

Dr David Weintrop is an Associate Professor and the Dean's Impact Professor in the Department of Teaching & Learning, Policy & Leadership in the College of Education with a joint appointment in the College of Information at the University of Maryland (UMD). His research focuses on the design, implementation, and evaluation of effective, engaging, and equitable computational learning experiences. His work lies at the intersection of design, computer science education, and the learning sciences. David holds a PhD in the Learning Sciences from Northwestern University and a B.S. in Computer Science from the University of Michigan.

Dr Rotem Israel-Fishelson is a postdoctoral researcher in the Department of Teaching & Learning, Policy & Leadership in the College of Education at the University of Maryland. Her research focuses on exploring ways to introduce learners to data science using engaging computational learning experiences. She is also interested in assessing computational thinking and creativity skills in game-based learning environments using learning analytics methods. Rotem holds a PhD in Science Education from Tel Aviv University, an M.Sc. in Media Technology from Linnaeus University, and a B.A. in Instructional Design from the Holon Institute of Technology.

Dr Peter F. Moon is a postdoctoral researcher at the University of Maryland. He graduated from UMD's PhD program in Mathematics Education in 2024. He has taught math, statistics, and computer science at Archbishop Curley High School in Baltimore while coaching swimming and he has taught courses in statistics for undergraduate middle grades pre-service teachers at UMD.

Seminar materials

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• Read our blog post about the seminar

Speakers: Lukas Höper and Carsten Schulte (Paderborn University)

In our increasingly AI-powered world, there’s no question that we should teach students about data-driven technologies to empower them to understand these everyday technologies and to help them make informed and self-determined decisions about their everyday interactions with such technologies. However, the question remains as to what exactly students should learn and how we can support them in connecting the concepts they learn in class to their everyday lives. 

In this seminar, Lukas Höper and Carsten Schulte will present the Data Awareness Framework. This explanatory model of data-driven technologies and designed interventions for middle school students is a way for students to explore the role of data in such technologies and apply it to applications they use daily. 

Lukas and Carsten will introduce the framework, show how it can be implemented in middle schools, and briefly summarise interesting findings from their research on fostering students’ data awareness.

Lukas Höper is a PhD student in Computing Education Research at Paderborn University, Germany. His main research interest is empirical research on teaching and learning processes in K–12 computing education. In his dissertation, he develops and evaluates the data awareness framework. Since 2020, he has been working on data awareness and other topics related to AI and data science education in schools in the ProDaBi project.

Dr Carsten Schulte is a Professor for Computing Education Research at Paderborn University, Germany. His work and research interests are the philosophy of computing education, artificial intelligence in education, and empirical research on teaching and learning processes (including eye movement research). Since 2017, he has been working together with Didactics of Mathematics (Paderborn University) on the ProDaBi project, in which data science and artificial intelligence are prepared as teaching topics. He is also a part of the collaborative research centre ‘Constructing Explainability’ on explainable AI.

Seminar materials

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• Read our blog post about the seminar

Speaker: Franz Jetzinger (Technical University of Munich)

It's true — AI is everywhere! Consequently, AI-related competencies are gradually being integrated into computer science (CS) curricula across the globe. However, this raises two challenges: first, teachers are not adequately prepared for this topic, which requires both AI and pedagogical knowledge. Second, evidence-based theories on teaching and learning about AI in K–12 education are still lacking. Franz Jetzinger addressed both issues in this talk. 

In the first part, Franz presented the implementation and evaluation of a scalable professional development (PD) programme that addresses relevant challenges such as limited resources, a large number of teachers to be trained, and the considerable heterogeneity of teachers’ backgrounds.

In the second part, he shared findings from an action research project investigating how teachers implement the topic in their classrooms and the difficulties teachers and students face, to provide a foundation for evidence-based advancements of teaching AI. 

Franz Jetzinger is a passionate high school teacher of computer science, physics, and music. He has authored several German CS textbooks, and conducted numerous workshops for teachers. Since 2021, he has been a member of the Computing Education Research Group at the Technical University of Munich and is actively working on improving computing education in the classroom. To this end, he designs professional development programmes to prepare CS teachers for effectively teaching about AI. His research focuses on evaluating these PD programmes and investigating the implementation of AI-related competencies in the CS classroom.

Seminar materials

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• Read our blog post about the seminar

Speaker: Shuchi Grover (Looking Glass Ventures)

AI has a dual role in K–12 education: it can be used as a teaching tool (teaching with AI) and it necessitates the promotion of AI literacy (teaching about AI).

Through these two themes, Shuchi Grover will look at how generative AI can be used in STEM and computer science (CS) classrooms to ethically support teaching and learning. She will also explore how foundational AI literacy and AI concepts can be integrated into CS curricula, to develop students' understanding of AI and machine learning as well as AI ethics.

Through concrete examples and recently developed frameworks, this seminar aims to equip educators with practical insights to understand AI's impact on society and its role in the future of education.

Dr Shuchi Grover is the Director of AI and Education Research at Looking Glass Ventures in Austin, Texas. She is a computer scientist and learning scientist with over 20 years’ experience in PK–12 computing education in formal and informal settings. She has led several National Science Foundation-funded projects involving research & design of curriculum, assessments, tools, and environments that help develop 21st-century competencies in computing, data science, AI, and cybersecurity, as well as the integration of STEM, computer science, and data science.

Seminar materials

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• Read our blog post about the seminar