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Bridging the Gap Between UX Practices and AI-Enabled Design Tools



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Bridging the Gap Between UX Practices and AI-Enabled Design Tools
Yuwen Lu, Chengzhi Zhang, Iris Zhang, Toby Jia-Jun Li

CHI'22: ACM Conference on Human Factors in Computing Systems
Session: Late Breaking Work (LBW)

Abstract
User interface (UI) and user experience (UX) design have become an indispensable part of today’s tech industry. Recently, much progress has been made in machine-learning-enabled design support tools for UX designers. However, few of these tools have been adopted by practitioners. To learn the underlying reasons and understand user needs for bridging this gap, we conducted contextual interviews with 8 UX professionals to understand their practice and identify opportunities for future research. We found that the current ML-enabled systems to support UX work mainly work on graphical interface elements, while design activities that involve more `design thinking'' such as user interviews and user testings are more helpful for designers. Many current systems were also designed for overly-simplistic and generic use scenarios. We identified 4 areas in the UX workflow that can benefit from additional ML-enabled assistance: design inspiration search, design alternative exploration, design system customization, and design guideline violation check.

WEB:: https://chi2022.acm.org/

Pre-recorded presentations of CHI 2022
Category
Web design
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