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When Discourse Stalls: Moving Past Five Semantic Stopsigns about Generative AI in Design Research

arXiv 2503.08565 ↗First posted 11 Mar 2025cs.CY cs.AIcited by 0

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Abstract (arXiv)

This essay examines how Generative AI (GenAI) is rapidly transforming design practices and how discourse often falls into over-simplified narratives that impede meaningful research and practical progress. We identify and deconstruct five prevalent "semantic stopsigns" -- reductive framings about GenAI in design that halt deeper inquiry and limit productive engagement. Reflecting upon two expert workshops at ACM conferences and semi-structured interviews with design practitioners, we analyze how these stopsigns manifest in research and practice. Our analysis develops mid-level knowledge that bridges theoretical discourse and practical implementation, helping designers and researchers interrogate common assumptions about GenAI in their own contexts. By recasting these stopsigns into more nuanced frameworks, we provide the design research community with practical approaches for thinking about and working with these emerging technologies.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 90 references that are in the map. First posted on arXiv 11 Mar 2025.

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21 of 90 references.

References · 21

  1. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  2. 2024The Effects of Generative AI on Design Fixation and Divergent Thinkingcited by 37
  3. 2023CreativeConnect: Supporting Reference Recombination for Graphic Design Ideation with Generative AIcited by 34
  4. 2022Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governancecited by 28
  5. 2024UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedbackcited by 27
  6. 2024Understanding Nonlinear Collaboration between Human and AI Agents: A Co-design Framework for Creative Designcited by 22
  7. 2022Cultural Incongruencies in Artificial Intelligencecited by 21
  8. 2021Meaningful human control: actionable properties for AI system developmentcited by 18
  9. 2023DesignGPT: Multi-Agent Collaboration in Designcited by 17
  10. 2024Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Trainingcited by 15
  11. 2024Generative AI Models for Different Steps in Architectural Design: A Literature Reviewcited by 12
  12. 2022The Myth of Culturally Agnostic AI Modelscited by 11
9 more
  1. 2024No Longer Trending on Artstation: Prompt Analysis of Generative AI Artcited by 7
  2. 2024Patterns of Creativity: How User Input Shapes AI-Generated Visual Diversitycited by 4
  3. 2023Experiments on Generative AI-Powered Parametric Modeling and BIM for Architectural Designcited by 3
  4. 2024Improved Emotional Alignment of AI and Humans: Human Ratings of Emotions Expressed by Stable Diffusion v1, DALL-E 2, and DALL-E 3cited by 3
  5. 2021Gaud'i: Conversational Interactions with Deep Representations to Generate Image Collectionscited by 2
  6. 2024Responding to Generative AI Technologies with Research-through-Design: The Ryelands AI Lab as an Exploratory Studycited by 2
  7. 2024AI Royalties -- an IP Framework to Compensate Artists & IP Holders for AI-Generated Contentcited by 2
  8. 2024Recommendations for public action towards sustainable generative AI systemscited by 1
  9. 2025The Unbearable Lightness of Prompting: A Critical Reflection on the Environmental Impact of genAI use in Design Educationcited by 1

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