Making (Non-)Sense — Teaching AI Intuition for the Design of Sensor-Based Interactions

The teaching format and workshop “Making (Non-)Sense” was held with design students from HfG Schwäbisch Gmünd and the Köln International School of Design (KISD) as part of the joint research project KITeGG. The workshop introduced students to sensor-based Artificial Intelligence (AI) through a playful yet hands-on approach, fostering intuitive understanding and critical engagement with machine learning technologies.

The workshop unfolded in two parts. The first used a role-playing activity to illustrate the components of a sensor-based AI system. Students acted as human sensors and classification algorithms in an everyday scenario. This embodied learning method helped to create a basic understanding of the mechanisms of AI algorithms, making abstract processes such as data collection and model training tangible and relatable.

The second part focused on technical exploration. Students worked with hardware and code-free software tools to collect data, train models, and deploy them on microcontrollers. This hands-on phase encouraged experimentation and ideation around AI-driven interactions.

As a final challenge, participants created a segment of an AI-powered Rube Goldberg machine, integrating sensor modules that classified specific input patterns to trigger actions. These segments were then connected into a collaborative chain reaction, showcased in a live group performance.

The aim of the workshop was not only to strengthen students‘ practical and conceptual understanding of AI technology, but also to support their ability to communicate about AI in interdisciplinary contexts and to collaborate with experts in the field.

The course materials are available as open source resources on GitLab. In June 2024, we also published a paper on the workshop format, explaining the process and content of the workshop in more detail. You can access the author’s version of the paper here.