mlsanityData to 7 Oct 2026

Imagining a Future of Designing with AI: Dynamic Grounding, Constructive Negotiation, and Sustainable Motivation

arXiv 2402.07342 ↗First posted 12 Feb 2024cs.HC cs.AIcited by 7

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Abstract (arXiv)

We ideate a future design workflow that involves AI technology. Drawing from activity and communication theory, we attempt to isolate the new value large AI models can provide design compared to past technologies. We arrive at three affordances -- dynamic grounding, constructive negotiation, and sustainable motivation -- that summarize latent qualities of natural language-enabled foundation models that, if explicitly designed for, can support the process of design. Through design fiction, we then imagine a future interface as a diegetic prototype, the story of Squirrel Game, that demonstrates each of our three affordances in a realistic usage scenario. Our design process, terminology, and diagrams aim to contribute to future discussions about the relative affordances of AI technology with regard to collaborating with human designers.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 16 of its 70 references that are in the map. First posted on arXiv 12 Feb 2024.

Builds on

16 of 70 references.

References · 16

  1. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  2. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  3. 2023Towards Understanding Sycophancy in Language Modelscited by 988
  4. 2020AmbigQA: Answering Ambiguous Open-domain Questionscited by 385
  5. 2023CodePlan: Repository-level Coding using LLMs and Planningcited by 139
  6. 2023ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testingcited by 88
  7. 2023Rehearsal: Simulating Conflict to Teach Conflict Resolutioncited by 54
  8. 2023Privacy-Preserving Parameter-Efficient Fine-Tuning for Large Language Model Servicescited by 43
  9. 2019Answer-based Adversarial Training for Generating Clarification Questionscited by 42
  10. 2023ClarifyGPT: Empowering LLM-based Code Generation with Intention Clarificationcited by 37
  11. 2024DynaVis: Dynamically Synthesized UI Widgets for Visualization Editingcited by 34
  12. 2023CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agentscited by 28
4 more
  1. 2019User Intent Prediction in Information-seeking Conversationscited by 19
  2. 2023Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responsescited by 18
  3. 2020Resolving Intent Ambiguities by Retrieving Discriminative Clarifying Questionscited by 16
  4. 2019Identifying Unclear Questions in Community Question Answering Websitescited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 1.

7 papers.

  1. 2025Creating General User Models from Computer Usecited by 44
  2. 2025Navigating Rifts in Human-LLM Grounding: Study and Benchmarkcited by 27
  3. 2024ChainBuddy: An AI Agent System for Generating LLM Pipelinescited by 6
  4. 2025Intent Tagging: Exploring Micro-Prompting Interactions for Supporting Granular Human-GenAI Co-Creation Workflowscited by 6
  5. 2025Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scalecited by 3
  6. 2026MineNPC-Task: Task Suite for Memory-Aware Minecraft Agentscited by 1
  7. 2024SysCaps: Language Interfaces for Simulation Surrogates of Complex Systemscited by 0