English

Canvil: Designerly Adaptation for LLM-Powered User Experiences

Human-Computer Interaction 2025-03-04 v3

Abstract

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.

Keywords

Cite

@article{arxiv.2401.09051,
  title  = {Canvil: Designerly Adaptation for LLM-Powered User Experiences},
  author = {K. J. Kevin Feng and Q. Vera Liao and Ziang Xiao and Jennifer Wortman Vaughan and Amy X. Zhang and David W. McDonald},
  journal= {arXiv preprint arXiv:2401.09051},
  year   = {2025}
}

Comments

CHI 2025 paper

R2 v1 2026-06-28T14:19:02.786Z