Clara Kliman-Silver
Clara is a UX Researcher on the Material Design team with a focus on designer-developer workflows and artificial intelligence. Previously, she designed and researched for AI and healthcare companies, and she holds an Sc.B. in cognitive science from Brown University.
Research Areas
Authored Publications
Google Publications
Other Publications
Sort By
Computational Methodologies for Understanding, Automating, and Evaluating User Interfaces
Yuwen Lu
Yue Jiang
Christof Lutteroth
Toby Jia-Jun Li
Jeffery Nichols
Wolfgang Stuerzlinger
Preview abstract
Building on the success of the first two workshops on user interfaces (UIs) at CHI 2022 and CHI 2023, this workshop aims to advance the research field by further exploring current research trends, such as applying large language models and visual language models. Previous work has explored computational approaches to understanding and adapting UIs using constraint-based optimization models and machine learning-based data-driven approaches. In addition to further delving into these established UI research areas, we aim to trigger the exploration into the application of the latest advancements in general-purpose large language and vision-language models within the UI domain. We will encourage participants to explore novel methods for understanding, automating, and evaluating UIs. The proposed workshop seeks to bring together academic researchers and industry practitioners interested in computational approaches for UIs to discuss the needs and opportunities for future user interface algorithms, models, and applications.
View details
Preview abstract
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.
View details
Exploring the future of design tooling: The role of artificial intelligence in tools for user experience professionals
Mohammed Kamal Khwaja
ACM CHI 2023(2023)
Preview abstract
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.
View details
Adapting User Experience Research Methods for AI-Driven Experiences
Kira Awadalla
Gregorio Convertino
Elizabeth Churchill
ACM CHI 2020(2020)
Preview abstract
This short paper describes how to adapt user experience research methods for artificial intelligence (AI)-driven applications. Presently, there is a dearth of guidance for conducting UX research on AI-driven experiences. We describe what makes this class of experiences unique, propose a preliminary foundational framework to categorize AI-driven experiences, and within the framework we show an example of methodological adaptations via a case study.
View details
No Results Found
