Frank Richard Bentley
Frank leads User Research for the Google Design Platform, including Material Design, Google Fonts/Icons, and Design System Management. For over 20 years, Frank has led teams in a wide variety of companies in creating new experiences that take advantage of a user's context and ecosystem of devices. He has also taught a class, currently called Understanding Users, for the past 16 years at Stanford at MIT.
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Design systems have become an industry standard for creating consistent, usable, and effective digital interfaces. However, detecting and correcting violations of design system guidelines, known as UI linting, is a major challenge. Manual UI linting is time-consuming and tedious, making it a prime candidate for automation. This paper presents a case study of adopting AI for UI linting. Through collaborative prototyping with UX designers, we analyzed the limitations of existing AI models and identified designers’ core needs and priorities in UI linting. With such knowledge, we designed a hybrid technical pipeline that combines the deterministic nature of heuristics with the flexibility of large language models. Our case study demonstrates that AI alone is not sufficient for practical adoption and highlights the importance of a deep understanding of AI capabilities and user-centered design approaches.
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Exploring the future of design tooling: The role of artificial intelligence in tools for user experience professionals
Mohammed Kamal Khwaja
ACM CHI 2023(2023)
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Recently, artificial intelligence (AI) has been introduced into a variety of consumer applications for creative work. Although AI-driven features in design tooling are nascent, there is growing interest in utilizing AI to support user experience (UX) workflows. In this case study, we surveyed industry UX professionals ("UXers") to understand how they perceive AI-driven assists in their tools, their concerns about accepting AI in design tools and which design-related workflows could be promising for future research. Our results suggest that UXers are overall positive about AI-driven features in design tools; looking to AI as a creative partner to iterate with and as an assistant with mundane tasks. We offer practical directions for the future of AI in UX tooling, but caution against developing tools that do not sufficiently address UXer's concerns around bias and trust.
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Prototypes, platforms and protocols: identifying common issues with remote unmoderated studies and their impact on research participants
Steven Schirra
Sasha Volkov
Shraddhaa Narasimha
Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, ACM(2023)
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Remote, unmoderated research platforms have increased the efficiency of traditional design research approaches, such as usability testing, while also allowing practitioners to collect more diverse user perspectives than afforded by lab-based methods. The self-service nature of these platforms has also increased the number of studies created by requesters without formal research training. Past research has explored the quality and validity of research findings on these platforms, but little is known about the everyday issues participants face while completing studies. We conducted an interview-based study with 22 experienced research participants to understand which issues are most commonly encountered and how participants mitigate issues as they arise. We found that a majority of the issues are distributed across research platforms, requestor protocols and prototypes, and participant responses range from filing support tickets to simply quitting studies. We discuss the consequences of these issues and provide recommendations for researchers and platform providers.
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