Rethinking the UI of GenUI: A Tale of Two Designs

2026-06-11

AI Summary

Existing generative UI (GenUI) tools rely on unstructured prompts, depth-first exploration strategies, and high-fidelity outputs, which hinder effective support for early-stage UI design exploration. This work proposes a contrastive GenUI approach that employs structured inputs, breadth-first exploration, and low-fidelity generation. Through controlled experiments—the first to systematically evaluate the impact of input structure, exploration strategy, and output fidelity on early design—we assess this paradigm using large language model–generated prototypes and user studies. Involving 24 UX designers and product managers, our findings indicate that structured inputs enhance visibility across design dimensions yet raise usability barriers; breadth-first exploration expands the creative solution space but introduces challenges in multi-screen preview management; and expert users still favor high-fidelity outputs. The study elucidates key trade-offs and contextual applicability among different GenUI paradigms.

Abstract

GenUI is an emergent class of AI tools that use large models to generate UI mock-ups based on users' high-level descriptions, promising to democratize UX design exploration for a broader audience. Most GenUI designs to date tend to inherit the conventions of conversational large models, such as ChatGPT and Gemini, where a user describes their design needs primarily via an unstructured prompt, and the tool then takes a depth-first approach, delving into the design right away and producing a high-fidelity prototype. In this research, we rethink how well this unstructured, depth-first, and high-fidelity GenUI design can support early-stage, 0-to-1 design exploration. To probe this question, we propose a contrastive design with structured input, breadth-first exploration, and low-fidelity generation. We then conducted a comparison study with 24 UX designers and product managers who conducted mini design exploration exercises using an existing GenUI tool and our contrastive GenUI tool. Findings reveal participants' perceived benefits and trade-offs of the two GenUI designs: structured input surfaces key facets but requires more work, raising entry barriers to start exploration; breadth-first workflow reveals more possibilities, but previewing UX ideas spanning many screens remains hard; and though low fidelity has value, professionals favor high fidelity because it fits practice and GenAI heightens fidelity expectations. We conclude with design implications for GenUI and similar AI-powered creativity support tools.

Problem

• GenUI
• UI generation
• design exploration
• early-stage design
• AI-powered creativity

Innovation

• GenUI
• structured input
• breadth-first exploration
• low-fidelity generation
• AI-powered design tools