open access publication

Conference Paper, 2024

ml-machine.org: Infrastructuring a Research Product to Disseminate AI Literacy in Education

Proceedings of the CHI Conference on Human Factors in Computing Systems, ISBN 9798400703300, Pages 1-16, 10.1145/3613904.3642539

Contributors

Bilstrup, Karl-Emil Kjaer 0000-0002-9285-7372 [1] Kaspersen, Magnus Høholt [1] Bouvin, Niels Olof 0000-0001-8421-4951 [1] Petersen, Marianne Graves [1]

Affiliations

  1. [1] Aarhus University
  2. [NORA names: AU Aarhus University; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

ml-machine.org is a web- and micro:bit-based educational tool for building machine learning models designed to enable more widespread teaching of AI literacy in secondary education. It has been designed as a research product in collaboration with partners from the educational sector, including the Danish Broadcasting Corporation and the Micro:bit Educational Foundation. ml-machine.org currently has more than 5000 unique users and is used in schools and teacher training. It is publicly available and promoted on the broadcasting corporation’s platforms. We describe the two-year process of developing and disseminating ml-machine.org. Based on interviews with partners and educators, we report on how ml-machine.org supports inquiry into the adoption and appropriation of such educational tools. We also provide insights on working with formal education infrastructures in order to scale and integrate a research product into teacher practices. Based on these experiences, we propose infrastructure as a novel quality of research products.

Keywords

AI literacy, Broadcasting Corporation, Danish, Danish Broadcasting Corporation, Education Foundation, Web, adoption, appropriateness, broadcast, collaboration, corporate platforms, corporations, education, education sector, educational infrastructure, educational tool, experiments, foundations, infrastructure, inquiry, interviews, learning models, literacy, machine, machine learning models, micro-, model, partners, platform, practice, process, production, quality, quality of research productivity, research, research productivity, school, secondary education, sector, teacher practice, teacher training, teachers, tools, training, two-year process, unique users, users, widespread teaching

Funders

  • The Velux Foundations

Data Provider: Digital Science