AlignUI: A Method for Designing LLM-Generated UIs Aligned with User Preferences

TL;DR

AlignUI is a novel method that uses a crowdsourced user preference dataset to guide LLMs in generating user interfaces that better match individual user tasks and preferences, streamlining UI design.

Contribution

The paper introduces AlignUI, a new approach that aligns LLM-generated UIs with user preferences using a dedicated dataset, improving personalization and efficiency in UI design.

Findings

Generated UIs closely match user preferences across multiple dimensions.

AlignUI outperforms baseline methods in user satisfaction and preference alignment.

The method is effective across various tasks and user groups.

Abstract

Designing user interfaces that align with user preferences is a time-consuming process, which requires iterative cycles of prototyping, user testing, and refinement. Recent advancements in LLM-based UI generation have enabled efficient UI generation to assist the UI design process. We introduce AlignUI, a method that aligns LLM-generated UIs with user tasks and preferences by using a user preference dataset to guide the LLM's reasoning process. The dataset was crowdsourced from 50 general users (the target users of generated UIs) and contained 720 UI control preferences on eight image-editing tasks. We evaluated AlignUI by generating UIs for six unseen tasks and conducting a user study with 72 additional general users. The results showed that the generated UIs closely align with multiple dimensions of user preferences. We conclude by discussing the applicability of our method to support user-aligned UI design for multiple task domains and user groups, as well as personalized user needs.