# Responding to Generative AI Technologies with Research-through-Design: The Ryelands AI Lab as an Exploratory Study

arXiv: https://arxiv.org/abs/2405.04677 · HTML: https://mlsanity.com/paper/2405.04677/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 101 references that are in the map.
- First posted on arXiv 7 May 2024.

## Builds on (most cited first, 11 of 11)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Multimodal datasets: misogyny, pornography, and malignant stereotypes](https://mlsanity.com/paper/2110.01963/) (2021)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [The Creation and Detection of Deepfakes: A Survey](https://mlsanity.com/paper/2004.11138/) (2020)
- ['It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions](https://mlsanity.com/paper/1801.10408/) (2018)
- [Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach](https://mlsanity.com/paper/2002.01092/) (2020)
- [Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry](https://mlsanity.com/paper/2101.04035/) (2021)
- [Dice in the Black Box: User Experiences with an Inscrutable Algorithm](https://mlsanity.com/paper/1812.03219/) (2018)
- [The Entoptic Field Camera as Metaphor-Driven Research-through-Design with AI Technologies](https://mlsanity.com/paper/2301.09545/) (2023)
- [Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics](https://mlsanity.com/paper/2001.00089/) (2019)
- [Explanation Strategies as an Empirical-Analytical Lens for Socio-Technical Contextualization of Machine Learning Interpretability](https://mlsanity.com/paper/2109.11849/) (2021)

## Built on by (most cited first, 2 of 2)

- [The Interaction Layer: An Exploration for Co-Designing User-LLM Interactions in Parental Wellbeing Support Systems](https://mlsanity.com/paper/2411.01228/) (2024)
- [When Discourse Stalls: Moving Past Five Semantic Stopsigns about Generative AI in Design Research](https://mlsanity.com/paper/2503.08565/) (2025)

## 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.