LEGOUI: Designing with UI-DSL Bricks to Balance Transparency and Controllability

2026-08-04

AI Summary

This work addresses the lack of transparency and controllability in existing generative UI design tools, which often force users into trial-and-error prompting without insight into the model’s reasoning. To overcome this, the authors propose LegoUI, a novel framework that introduces, for the first time, a staged and traceable UI-specific domain-specific language (UI-DSL). This approach decomposes the generation process into interpretable steps and records provenance information at each stage, enabling user intervention and iterative refinement. While maintaining high generation efficiency, LegoUI significantly enhances the transparency and controllability of the design workflow. Evaluated on 40 real-world tasks, the method achieves over 95% accuracy in requirement analysis, producing complete and non-redundant outputs. User studies further demonstrate its substantial advantages over current tools in intent alignment, transparency, and user control.

Abstract

Generative user interface design tools enable rapid prototyping but often operate as black boxes with limited transparency and controllability. When outputs diverge from the designer's intent, users are left tweaking prompts via trial-and-error with little insight into the model's reasoning. We present LegoUI, a staged generative framework that structures the interface design process into sequential, interpretable steps along key design dimensions, capturing each step's result in a UI domain-specific language (UI-DSL) enriched with provenance. This approach exposes the model's intermediate reasoning and enables user intervention and iterative refinement. In a technical evaluation on 40 real-world design prompts, LegoUI's requirement analysis stage captured explicit requirements with over 95% accuracy, near-complete coverage, and zero redundancy. In user studies, participants using LegoUI reported significantly greater transparency, controllability, and alignment with their intent compared to existing one-shot generative UI tools.

Problem

• generative UI design
• transparency
• controllability
• black-box models
• design intent alignment

Innovation

• generative UI design
• UI-DSL
• transparency
• controllability
• provenance