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.

翻译：GenUI是一类新兴的人工智能工具，它利用大模型根据用户的高层描述生成用户界面原型，旨在为更广泛的用户群体普及用户体验设计探索的民主化。迄今为止，大多数GenUI设计倾向于继承对话式大模型（如ChatGPT和Gemini）的惯例——用户主要通过非结构化提示描述其设计需求，随后工具采用深度优先策略，立即深入设计并生成高保真原型。在本研究中，我们重新审视这种非结构化、深度优先、高保真的GenUI设计能否有效支持早期从零到一的设计探索。为探究此问题，我们提出了一种对比性设计，采用结构化输入、广度优先探索和低保真生成。随后，我们邀请24名用户体验设计师和产品经理开展了一项比较研究，他们分别使用现有GenUI工具和我们的对比性GenUI工具进行迷你设计探索练习。研究结果揭示了参与者对两种GenUI设计的感知优势与权衡：结构化输入能凸显关键要素但需更多工作，从而提高了启动探索的门槛；广度优先工作流可揭示更多可能性，但预览跨多屏幕的用户体验想法仍具挑战；尽管低保真具有价值，但专业人士更青睐高保真，因其更贴合实践且生成式人工智能提升了保真度期望。最后，我们总结了针对GenUI及类似人工智能驱动创意支持工具的设计启示。