AI Assistance for UX: A Literature Review Through Human-Centered AI

AI Assistance for UX: A Literature Review Through Human-Centered AI

2024 | Yuwen Lu, Yuewen Yang, Qinyi Zhao, Chengzhi Zhang, Toby Jia-Jun Li
This paper presents a systematic literature review of 359 papers to explore the current landscape of AI-assisted user experience (UX) design, focusing on the human-centered AI (HCAI) perspective. The review aims to identify trends, gaps, and unmet needs in AI support for UX practitioners. Guided by the Double Diamond design framework, the analysis highlights that UX practitioners' unique focuses on empathy building and experiences across UI screens are often overlooked. Simplistic AI automation can obstruct the empathy-building process, and focusing solely on individual UI screens without considering interactions and user flows reduces the practical value of AI systems for UX designers. The study calls for a deeper understanding of UX mindsets and more designer-centric datasets and evaluation metrics to bridge the gap between UX practices and AI research. The findings emphasize the need for a balanced approach that incorporates human-centered investigations and aligns AI capabilities with UX goals to enhance the overall value and impact of AI solutions in the UX industry.This paper presents a systematic literature review of 359 papers to explore the current landscape of AI-assisted user experience (UX) design, focusing on the human-centered AI (HCAI) perspective. The review aims to identify trends, gaps, and unmet needs in AI support for UX practitioners. Guided by the Double Diamond design framework, the analysis highlights that UX practitioners' unique focuses on empathy building and experiences across UI screens are often overlooked. Simplistic AI automation can obstruct the empathy-building process, and focusing solely on individual UI screens without considering interactions and user flows reduces the practical value of AI systems for UX designers. The study calls for a deeper understanding of UX mindsets and more designer-centric datasets and evaluation metrics to bridge the gap between UX practices and AI research. The findings emphasize the need for a balanced approach that incorporates human-centered investigations and aligns AI capabilities with UX goals to enhance the overall value and impact of AI solutions in the UX industry.
Reach us at info@study.space