Canvil: Designerly Adaptation for LLM-Powered User Experiences
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Abstract (arXiv)
Advancements in large language models (LLMs) are sparking a proliferation of LLM-powered user experiences (UX). In product teams, designers often craft UX to meet user needs, but it is unclear how they engage with LLMs as a novel design material. Through a formative study with 12 designers, we find that designers seek a translational process that enables design requirements to shape and be shaped by LLM behavior, motivating a need for designerly adaptation to facilitate this translation. We then built Canvil, a Figma widget that operationalizes designerly adaptation. We used Canvil as a probe to study designerly adaptation in a group-based design study (6 groups, N=17), finding that designers constructively iterated on both adaptation approaches and interface designs to enhance end-user interaction with LLMs. Furthermore, designers identified promising collaborative workflows for designerly adaptation. Our work opens new avenues for processes and tools that foreground designers' human-centered expertise when developing LLM-powered applications.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jan 2024.
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Citing papers per year: 2024: 1; 2025: 5; 2026: 3.
9 papers.
- 2024Not Just Novelty: A Longitudinal Study on Utility and Customization of an AI Workflowcited by 14
- 2025Collaborative Document Editing with Multiple Users and AI Agentscited by 5
- 2025Disaggregated Prefill and Decoding Inference System for Large Language Model Serving on Multi-Vendor GPUscited by 3
- 2025From Queries to Criteria: Understanding How Astronomers Evaluate LLMscited by 2
- 2025Exploring the Innovation Opportunities for Pre-trained Modelscited by 0
- 2025PolicyPad: Collaborative Prototyping of LLM Policiescited by 0
- 2026Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMscited by 0
- 2026Gricea: An Open Science Platform for Conversational AI Researchcited by 0
- 2026Does AI Save Time on Product Design? A Randomized Controlled Experiment of AI Prompt-to-Design Workflowscited by 0
- 2026Does AI Save Time on Product Design? A Randomized Controlled Experiment of AI Prompt-to-Design Workflows22 Sep 2026
- 2026Gricea: An Open Science Platform for Conversational AI Research18 Sep 2026
- 2026Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMs5 Feb 2026
- 2025PolicyPad: Collaborative Prototyping of LLM Policies24 Sep 2025
- 2025Disaggregated Prefill and Decoding Inference System for Large Language Model Serving on Multi-Vendor GPUs22 Sep 2025
- 2025Collaborative Document Editing with Multiple Users and AI Agents15 Sep 2025
- 2025From Queries to Criteria: Understanding How Astronomers Evaluate LLMs21 Jul 2025
- 2025Exploring the Innovation Opportunities for Pre-trained Models21 May 2025
- 2024Not Just Novelty: A Longitudinal Study on Utility and Customization of an AI Workflow15 Feb 2024