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

arXiv: https://arxiv.org/abs/2402.07342 · HTML: https://mlsanity.com/paper/2402.07342/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 70 references that are in the map.
- First posted on arXiv 12 Feb 2024.

## Builds on (most cited first, 16 of 16)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [AmbigQA: Answering Ambiguous Open-domain Questions](https://mlsanity.com/paper/2004.10645/) (2020)
- [CodePlan: Repository-level Coding using LLMs and Planning](https://mlsanity.com/paper/2309.12499/) (2023)
- [ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing](https://mlsanity.com/paper/2309.09128/) (2023)
- [Rehearsal: Simulating Conflict to Teach Conflict Resolution](https://mlsanity.com/paper/2309.12309/) (2023)
- [Answer-based Adversarial Training for Generating Clarification Questions](https://mlsanity.com/paper/1904.02281/) (2019)
- [Privacy-Preserving Parameter-Efficient Fine-Tuning for Large Language Model Services](https://mlsanity.com/paper/2305.06212/) (2023)
- [ClarifyGPT: Empowering LLM-based Code Generation with Intention Clarification](https://mlsanity.com/paper/2310.10996/) (2023)
- [DynaVis: Dynamically Synthesized UI Widgets for Visualization Editing](https://mlsanity.com/paper/2401.10880/) (2024)
- [CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents](https://mlsanity.com/paper/2306.10376/) (2023)
- [User Intent Prediction in Information-seeking Conversations](https://mlsanity.com/paper/1901.03489/) (2019)
- [Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses](https://mlsanity.com/paper/2312.00763/) (2023)
- [Resolving Intent Ambiguities by Retrieving Discriminative Clarifying Questions](https://mlsanity.com/paper/2008.07559/) (2020)
- [Identifying Unclear Questions in Community Question Answering Websites](https://mlsanity.com/paper/1901.06168/) (2019)

## Built on by (most cited first, 7 of 7)

- [Creating General User Models from Computer Use](https://mlsanity.com/paper/2505.10831/) (2025)
- [Navigating Rifts in Human-LLM Grounding: Study and Benchmark](https://mlsanity.com/paper/2503.13975/) (2025)
- [ChainBuddy: An AI Agent System for Generating LLM Pipelines](https://mlsanity.com/paper/2409.13588/) (2024)
- [Intent Tagging: Exploring Micro-Prompting Interactions for Supporting Granular Human-GenAI Co-Creation Workflows](https://mlsanity.com/paper/2502.18737/) (2025)
- [Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale](https://mlsanity.com/paper/2504.09283/) (2025)
- [SysCaps: Language Interfaces for Simulation Surrogates of Complex Systems](https://mlsanity.com/paper/2405.19653/) (2024)
- [MineNPC-Task: Task Suite for Memory-Aware Minecraft Agents](https://mlsanity.com/paper/2601.05215/) (2026)

## 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.