This september, ScaDS.AI Dresden/Leipzig had the exciting opportunity to present its work INFOS 2025 and ISSEP 2025 – two major conferences focused on machine learning education. Attending INFOS and ISSEP allowed ScaDS.AI Dresden/Leipzig to:

• showcase innovative teaching methods for AI and machine learning education,
• engage with educators and researchers about making our research instruments accessible to schools,
• highlight the ethical and societal dimensions of machine learning, and
• increase the visibility of educational initiatives at ScaDS.AI Dresden/Leipzig.

INFOS 2025: Best Paper Award

At INFOS (“Informatik und Schule”), the largest German-speaking conference on informatics education, Erik Marx presented a paper on a newly developed research instrument for assessing students’ conceptual understanding of central machine learning topics. Erik Marx, Dr. Thiemo Leonhardt, and Prof. Nadine Bergner won the Best Paper Award for this publication. This award reflects both the quality and relevance of their work.

They also co-authored a poster on machine learning education and ethics. The poster shared a classroom approach that encourages students to critically evaluate machine learning systems from societal and cultural perspectives. Early tests showed that students could make more informed, multi-perspective decisions about machine learning systems.

ISSEP 2025: Best Presentation Award

Two weeks before INFOS, the team also attended ISSEP, an international conference on informatics in schools. They presented Art Interactive – The Intelligent Museum Guide, a hands-on workshop that teaches fundamental machine learning concepts through an interactive museum scenario.

Students trained image classification models using 3D-printed objects and the tool Teachable Machine, gaining first-hand experience with machine learning workflows and evaluation metrics. This session earned Erik Marx and Prof. Nadine Bergner the Best Presentation Award, showcasing the power of activity-based learning in machine learning education.

Meaningful Outcomes and Future Directions

Recognition at both conferences has already sparked new opportunities for collaboration, from adapting our research instruments for use in classrooms to developing new, activity-based learning formats that reach even more students. Furthermore, these exchanges reinforced our strong commitment to helping the next generation build meaningful AI and machine learning skills that connect technical understanding with ethical reflection.