Generating Automatic Feedback on UI Mockups with Large Language Models

要旨

Feedback on user interface (UI) mockups is crucial in design. However, human feedback is not always readily available. We explore the potential of using large language models for automatic feedback. Specifically, we focus on \changes{applying GPT-4 to automate heuristic evaluation}, which currently entails a human expert assessing a UI’s compliance with a set of design guidelines. We implemented a Figma plugin that takes in a UI design and a set of written heuristics, and renders automatically-generated feedback as constructive suggestions. We assessed performance on 51 UIs using three sets of guidelines, compared GPT-4-generated design suggestions with those from human experts, and conducted a study with 12 expert designers to understand fit with existing practice. We found that GPT-4-based feedback is useful for catching subtle errors, improving text, and considering UI semantics, but feedback also decreased in utility over iterations. Participants described several uses for this plugin despite its imperfect suggestions.

著者
Peitong Duan
UC Berkeley, Berkeley, California, United States
Jeremy Warner
UC Berkeley, Berkeley, California, United States
Yang Li
Google Research, Mountain View, California, United States
Bjoern Hartmann
UC Berkeley, Berkeley, California, United States
論文URL

https://doi.org/10.1145/3613904.3642782

動画

会議: CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)

セッション: AI and UI Design

316A
5 件の発表
2024-05-14 23:00:00
2024-05-15 00:20:00