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.

There are growing discussions within the research community about how to adapt study design given the widespread availability of Generative Artificial Intelligence (GenAI), including Large Language Models (LLMs). While much prior research has focused on LLM use from a researcher perspective (e.g. detecting and screening for LLM use) we present a complementary study from the perspective of participants who use LLMs during their research participation. In this exploratory interview study with 17 participants, we found a range of LLM use cases, from sourcing studies, to generating or modifying responses, to asking clarification questions about studies. We also explored participants' ethical considerations, finding that participants considered researchers' needs for authentic data when setting ethical boundaries. Participants also discussed how attempts to thwart their LLM use have negatively impacted their everyday participant experience. We propose a set of recommendations that researchers can incorporate into their studies to proactively address participant LLM use.

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.

Recent advances in general-purpose AI underscore the urgent need to align AI systems with human goals and values. Yet, the lack of a clear, shared understanding of what constitutes "alignment" limits meaningful progress and cross-disciplinary collaboration. In this position paper, we argue that the research community should explicitly define and critically reflect on "alignment" to account for the bidirectional and dynamic relationship between humans and AI. Through a systematic review of over 400 papers spanning HCI, NLP, ML, and more, we examine how alignment is currently defined and operationalized. Building on this analysis, we introduce the Bidirectional Human-AI Alignment framework, which not only incorporates traditional efforts to align AI with human values but also introduces the critical, underexplored dimension of aligning humans with AI – supporting cognitive, behavioral, and societal adaptation to rapidly advancing AI technologies. Our findings reveal significant gaps in current literature, especially in long-term interaction design, human value modeling, and mutual understanding. We conclude with three central challenges and actionable recommendations to guide future research toward more nuanced, reciprocal, and human-AI alignment approaches.

Shape is a fundamental visual characteristic in the design of common UI components like buttons, switches, and dialogs. It has commonly been used to enhance the visual aesthetic of a UI, or to express a distinct perspective in style or brand. However, it remains understudied how the shape of UI components convey semantic meaning and impact user perception of the information displayed in those UI components. As a first step to address this gap, we chose to study the dialog UI component. We first explored the shape of a dialog and created 6 different designs (e.g., dialogs with rounded corners, circle, and wiggly-circle) for an online survey study with 200 participants. We examined whether different dialog designs alter user perceptions and expectations of different messages displayed within them. This work serves as a practical study to explore the opportunity for shapes to be used intentionally in UI design.

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 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 UX professionals 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 UX professionals’ concerns around bias and trust.

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 requestors 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 these studies. We conducted an interview-based study with 22 experienced research participants to understand what issues are most commonly encountered and how participants mitigate issues as they arise. We found that a majority of the issues surface 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 platforms.

Scholars, journalists, and other commentators argue that many parts of the world, including the US, are suffering a social-epistemological crisis, sometimes called "post-truth," and that this crisis is related to the fragmentation of newsmedia. The conventional media effects research explanation for this relationship between news and "post-truth" is framed in terms of messages and information-especially misinformation-as the mechanism by which communication effects change. Analysing the results of a large (n = 164) interview study on mobile and other digital news audiencehood, this article presents an alternative, complementary explanation focused on ritual functions of communication. The primary method of affinity analysis of interview data identified a number of recurring themes: people's preferences, methods, and patterns of news consumption exhibit wild diversity beyond easy summary, but they share the experience of news as nearly ubiquitous, and often as excessive and therefore in need of management. Strategies for managing news broadly fell into categories of news avoidance and active research. Perhaps the most consistent observation across participants' accounts, however, is a conspicuous absence of other people, with news managed and confronted alone. Those findings are interpreted through James Carey's ritual theory of communication, which argues that meaning emerges not only through transmission of messages and information, but also through people's shared experiences of participating in the ritual processes of communication. This work makes a parallel argument that media fragmentation has been implicated in social-epistemological breakdown not only through the mechanism of messages and (mis)information, but also through the transformation and, in certain cases, loss of shared news rituals. The combination of large-scale interview research with media ritual analysis led to these insights about the cultural relevance and collective implications of people's experiences of ubiquitous news and the avoidance thereof.

This chapter explores glanceability as a crucial requirement for several types of mobile visualizations, thereby integrating knowledge from the Vision Sciences, Visualization, Human-Computer Interaction, and Ubiquitous Computing. In mobile contexts, quick information needs are frequently occurring and differ from those in traditional visualizations that are designed for analyzing complex datasets. The chapter therefore discusses specific characteristics of glanceable mobile visualizations, explores different evaluation methodologies, and concludes with open challenges in the design of future glanceable visualizations.

Over three billion people use personal email accounts for a wide variety of communications, largely from businesses. These messages often require additional information that users need to look for outside of the email itself, such as store hours, bill details, or related news articles. We studied these "email-prompted information needs" in a pilot interview-based study, a two-week diary study, and a large-scale survey with 790 total participants, finding that Notification, Deal, and Newsletter messages were the most likely to spark a need for external information. We conclude with several designs evaluated in a concept evaluation study with 276 participants and implications for the design of personal email services to better meet users' external information needs.

This chapter discusses the special challenges of evaluating mobile visualizations. Many research goals can be addressed by an evaluation study including validating rapid perception of differences in data or examining the long-term use and impact of visualizations. Different methods, time-scales of research, and participant recruitment strategies are needed depending on the questions that one wants to answer. This chapter explores the literature, discussing a variety of goals and evaluation approaches, highlighting best practices and making recommendations for future approaches to evaluating mobile visualizations.

A common practice in HCI research is to conduct a survey to understand the generalizability of findings from smaller-scale qualitative research. These surveys are typically deployed to convenience samples, on low-cost platforms such as Amazon's Mechanical Turk or Survey Monkey, or to more expensive market research panels offered by a variety of premium firms. Costs can vary widely, from hundreds of dollars to tens of thousands of dollars depending on the platform used. We set out to understand the accuracy of ten different survey platforms/panels compared to ground truth data for a total of 6,007 respondents on 80 different aspects of demographic and behavioral questions. We found several panels that performed significantly better than others on certain topics, while different panels provided longer and more relevant open-ended responses. Based on this data, we highlight the benefits and pitfalls of using a variety of survey distribution options in terms of the quality, efficiency, and representative nature of the respondents and the types of responses that can be obtained.

This case study follows the research process of rethinking the design and functionality of a personal email client, Yahoo Mail. Over three years, we changed the focus of the product from composing emails towards automatically organizing specific categories of business to consumer email (such as deals, receipts, and travel) and creating experiences unique to each category. To achieve this, we employed iterative user research with over 1,500 in-person interviews in six countries and surveys to many thousands of people around the world. This research process culminated in the launch of Yahoo Mail 6.0 for iOS and Android devices in the fall of 2019.

Despite claims of Mobile TV's mainstream arrival in 2010, it took until 2017 for watching professionally-produced television content on mobile phones to truly become a mass-market phenomenon in America, with half of all TV content expected to be watched on mobile phones by 2020. But what professionally produced content are people watching on their phones and when are they watching it? Are there any clusters of behavior that emerge in the broader population when it comes to watching TV on the phone? We set out to answer these questions through two surveys deployed to representative samples of online Americans. We discuss our findings on the mass-market arrival of mobile TV viewing and differences from how the HCI community has previously envisioned mobile video. We conclude with implications for the design of future mobile TV systems.

Laptop and desktop computers are frequently used to watch online videos from a wide variety of services. From short YouTube clips, to television programming, to full-length films, users are increasingly moving much of their video viewing away from television sets towards computers. But what are they watching, and when? We set out to understand current video use on computers through analyzing full browsing histories from a diverse set of online Americans, finding some temporal differences in genres watched, yet few differences in the length of videos watched by hour. We also explore topics of videos, how users arrive at online videos through referral links, and conclude with several implications for the design of online video services that focus on the types of content people are actually watching online.

The news landscape has been changing dramatically over the past few years. Whereas news once came from a small set of highly edited sources, now people can find news from thousands of news sites online, through a variety of channels such as web search, social media, email newsletters, or direct browsing. We set out to understand how Americans read news online using web browser logs collected from 174 diverse participants. We found that 20% of all news sessions started with a web search, that 16% started from social media, that 61% of news sessions only involved a single news domain, and that 47% of our participants read news from both sides of the political spectrum. We conclude with key implications for online news, social media, and search sites to encourage more balanced news browsing.

Voice has become a widespread and commercially viable interaction mechanism with the introduction of voice assistants (VAs), such as Amazon’s Alexa, Apple’s Siri, Google Assistant, and Microsoft’s Cortana. Despite their prevalence, we do not have a detailed understanding of how these technologies are used in domestic spaces. To understand how people use VAs, we conducted interviews with 19 users, and analyzed the log files of 82 Amazon Alexa devices, totaling 193,665 commands, and 88 Google Home Devices, totaling 65,499 commands. In our analysis, we identified music, search, and IoT usage as the command categories most used by VA users. We explored how VAs are used in the home, investigated the role of VAs as scaffolding for Internet of Things device control, and characterized emergent issues of privacy for VA users. We conclude with implications for the design of VAs and for future research studies of VAs.

There are currently a wide variety of ways to share news with others: from sharing in a personal message, to sharing on a social network, to publicly posting. Through a survey with over one thousand people and an artifact analysis of 262 shared articles, we examine differences in motivations and frequency of sharing news on public, social and private platforms. We find that public sharing is more focused on spreading an ideology, while private sharing in messaging is dominated by stories inspired by the recipient's interests or context. The survey revealed three main groups of news sharing practices: those who shared to all channels (public, social, private), those who didn't share at all, and those who shared to private and social. The groups differed in their attitudes toward online discussion; those that shared the most were neutral and those that didn't share had negative attitudes about discussion online. We discuss sharing practices and implications for social systems that support sharing news.

Over the past two years the Ubicomp vision of ambient voice assistants, in the form of smart speakers such as the Amazon Echo and Google Home, has been integrated into tens of millions of homes. However, the use of these systems over time in the home has not been studied in depth. We set out to understand exactly what users are doing with these devices over time through analyzing voice history logs of 65,499 interactions with existing Google Home devices from 88 diverse homes over an average of 110 days. We found that specific types of commands were made more often at particular times of day and that commands in some domains increased in length over time as participants tried out new ways to interact with their devices, yet exploration of new topics was low. Four distinct user groups also emerged based on using the device more or less during the day vs. in the evening or using particular categories. We conclude by comparing smart speaker use to a similar study of smartphone use and offer implications for the design of new smart speaker assistants and skills, highlighting specific areas where both manufacturers and skill providers can focus in this domain.