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.
Authored Publications
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Usability Hasn’t Peaked: Exploring How Expressive Design Overcomes the Usability Plateau
Alyssa Sheehan
Bianca Gallardo
Ying Wang
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26), April 13–17, 2026, Barcelona, Spain (2026)
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Critics have argued that mobile usability has largely been optimized, and that only incremental gains are possible. We set out to explore if the newest generation of design systems, which promote greater flexibility and a return to design basics, could produce substantially more usable designs while maintaining or increasing aesthetic judgments. Through a study with 48 diverse participants completing tasks in 10 different applications, we found that in designs created following Material 3 Expressive guidelines, users fixated on the correct screen element for a task 33% faster, completed tasks 20% faster, and rated experiences more positively compared to versions designed using the previous Material design system. These improvements in performance and aesthetic ratings challenge the premise of a usability plateau and show that mobile usability has not peaked. We illustrate specific opportunities to make mobile experiences more usable by returning to design fundamentals while highlighting risks of added flexibility.
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Human-Computer Interaction research and design pedagogy rely on idealized process models, such as the Double Diamond, to describe how user experiences are designed. These models assume an orderly, linear design process that, while easy to understand, fails to capture the iterative and collaborative reality of professional practice. A few qualitative studies have successfully captured this complexity -- still, they often suffer from retrospective narrative smoothing and lack systemic scale. To understand how design unfolds in real products, we analyzed historical snapshots of 102 Figma files from a multi-national technology company and investigated the true trajectories of the design process at scale. Our analysis reveals that while the established process models might be applicable, the operational details are highly non-linear. Rather than a straight line from ideation toward completion, design advances are repeatedly reset to the ideation stage as feedback is received. We argue that by treating design files as operational telemetry, the industry can move beyond abstract frameworks to build practices and collaborative tools that support the non-linear realities of modern product development.
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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
Tiffany Knearem
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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