Tiffany Knearem
Tiffany is a User Experience Researcher with expertise in Human-Computer Interaction (HCI), currently conducting research in the area of front-end development (e.g., designer-developer collaboration, design handoff, AI-supported product development). She has wide-ranging interests in the areas of human-AI alignment, product design, creativity support tooling and participatory design.
She is active in HCI research communities, specifically ACM CHI, CSCW, C&C and UIST.
Tiffany holds a PhD in Information Sciences and Technologies from Pennsylvania State University, advised by Dr. John M. Carroll. Her PhD focused on community informatics; she developed a research program to understand and enable community innovation and peer-to-peer care through information and communication technologies.
For more information, please visit her Google Scholar.
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Solidarity not Charity! Empowering Local Communities for Disaster Relief during COVID-19 through Grassroots Support
Jeongwon Jo
Oluwafunke Alliyu
John M. Carroll
Computer Supported Cooperative Work (2024) (2024)
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The COVID-19 pandemic brought wide-ranging, unanticipated societal changes as communities rushed to slow the spread of the novel coronavirus. In response, mutual aid groups bloomed online across the United States to fill in the gaps in social services and help local communities cope with infrastructural breakdowns. Unlike many previous disasters, the long-haul nature of COVID-19 necessitates sustained disaster relief efforts. In this paper, we conducted an interview study with online mutual aid group administrators to understand how groups facilitated disaster relief, and how disaster relief initiatives developed and maintained over the course of the first year of COVID-19. Our findings suggest that the groups were crucial sources of community-based support for immediate needs, innovated long-term solutions for chronic community issues and grew into a vehicle for justice-centered work. Our insights shed light on the strength of mutual aid as a community capacity that can support communities to collectively be more prepared for future long-haul disasters than they were with COVID-19.
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Computational Methodologies for Understanding, Automating, and Evaluating User Interfaces
Yuwen Lu
Yue Jiang
Christof Lutteroth
Toby Jia-Jun Li
Jeffery Nichols
Wolfgang Stuerzlinger
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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.
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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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Relay: A collaborative UI model for design handoff
Kris Giesing
ACM Symposium on User Interface Software and Technology (2023)
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The design handoff process refers to the stage in the user interface (UI) design process where a designer gives their finished design to a developer for implementation. However, design decisions are lost when developers struggle to interpret and implement the designer’s original intent. To address this problem, we built a system called Relay that utilizes concrete UI models to capture design intent. To our knowledge, Relay is the first system described in the academic literature to take artifacts from an existing design tool and generate a UI model.
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