Abstract
Widespread ML systems can be powerful tools for designers. Leveraging on their empathy, system-level thinking, and transformative influence, they may benefit both the ML field and society at large through the conceptualization and development of meaningful solutions integrating ML systems. Yet, they still have lacks in ML related knowledge, language, skills, and competencies. From these premises, the following argumentation depicts some preliminary experiments from a PhD research aiming to translate ML knowledge for design education, within an ethical frame.
With a research-through-design approach, it explores and develops a methodological contribution and designerly tools to enhance cross fertilization and interdisciplinary communication between design and ML. In particular, two workshops aimed at testing the effectiveness of the proposed educational projects (with the related methods and tools designed for the disciplinary translation) are portrayed, focusing on how the achievement of their intended learning outcomes is perceived through observation, teaching-learning activities, questionnaires, and the delivery and presentation of a concept.
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Sciannamè, M. (2022). Introducing ML in Design Education. Preliminary Experiments. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2022 Posters. HCII 2022. Communications in Computer and Information Science, vol 1580. Springer, Cham. https://doi.org/10.1007/978-3-031-06417-3_60
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