Flowy: Supporting UX Design Decisions Through AI-Driven Pattern Annotation in Multi-Screen User Flows
<|reference_start|>Flowy: Supporting UX Design Decisions Through AI-Driven Pattern Annotation in Multi-Screen User Flows: Many recent AI-powered UX design tools focus on generating individual static UI screens from natural language. However, they overlook the crucial aspect of interactions and user experiences across multiple screens. Through formative studies with UX professionals, we identified limitations of these tools in supporting realistic UX design workflows. In response, we designed and developed Flowy, an app that augments designers' information foraging process in ideation by supplementing specific user flow examples with distilled design pattern knowledge. Flowy utilizes large multimodal AI models and a high-quality user flow dataset to help designers identify and understand relevant abstract design patterns in the design space for multi-screen user flows. Our user study with professional UX designers demonstrates how Flowy supports realistic UX tasks. Our design considerations in Flowy, such as representations with appropriate levels of abstraction and assisted navigation through the solution space, are generalizable to other creative tasks and embody a human-centered, intelligence augmentation approach to using AI in UX design.<|reference_end|>
@article{lu2024flowy:,
    title={Flowy: Supporting UX Design Decisions Through AI-Driven Pattern
  Annotation in Multi-Screen User Flows},
    author={Yuwen Lu, Ziang Tong, Qinyi Zhao, Yewon Oh, Bryan Wang, Toby Jia-Jun
  Li},
    journal={arXiv preprint arXiv:2406.16177},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16177},
    primaryClass={cs.HC}
}