Responding to Generative AI Technologies with Research-through-Design: The Ryelands AI Lab as an Exploratory Study
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Abstract (arXiv)
Generative AI technologies demand new practical and critical competencies, which call on design to respond to and foster these. We present an exploratory study guided by Research-through-Design, in which we partnered with a primary school to develop a constructionist curriculum centered on students interacting with a generative AI technology. We provide a detailed account of the design of and outputs from the curriculum and learning materials, finding centrally that the reflexive and prolonged `hands-on' approach led to a co-development of students' practical and critical competencies. From the study, we contribute guidance for designing constructionist approaches to generative AI technology education; further arguing to do so with `critical responsivity.' We then discuss how HCI researchers may leverage constructionist strategies in designing interactions with generative AI technologies; and suggest that Research-through-Design can play an important role as a `rapid response methodology' capable of reacting to fast-evolving, disruptive technologies such as generative AI.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 11 of its 101 references that are in the map. First posted on arXiv 7 May 2024.
Builds on
11 of 101 references.
References · 11
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2021Multimodal datasets: misogyny, pornography, and malignant stereotypescited by 326
- 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 251
- 2020The Creation and Detection of Deepfakes: A Surveycited by 246
- 2018'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisionscited by 91
- 2020Human-centered Explainable AI: Towards a Reflective Sociotechnical Approachcited by 90
- 2021Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquirycited by 14
- 2018Dice in the Black Box: User Experiences with an Inscrutable Algorithmcited by 7
- 2023The Entoptic Field Camera as Metaphor-Driven Research-through-Design with AI Technologiescited by 5
- 2019Measuring Non-Expert Comprehension of Machine Learning Fairness Metricscited by 4
- 2021Explanation Strategies as an Empirical-Analytical Lens for Socio-Technical Contextualization of Machine Learning Interpretabilitycited by 4
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.