Although HCI research papers offer valuable design insights, designers often struggle to apply them in design workflows due to difficulties in finding relevant literature, understanding technical jargon, the lack of contextualization, and limited actionability. To address these challenges, we present ReFinE, a Figma plugin that supports real-time design iteration by surfacing contextualized insights from research papers. ReFinE identifies and synthesizes design implications from HCI literature relevant to the mockup’s design context, and tailors this research evidence to a specific design mockup by providing actionable visual guidance on how to update the mockup. To assess the system’s effectiveness, we conducted a technical evaluation and a user study. Results show that ReFinE effectively synthesizes and contextualizes design implications, reducing cognitive load and improving designers’ ability to integrate research evidence into UI mockups. This work contributes to bridging the gap between research and design practice by presenting a tool for embedding scholarly insights into the UI design process.

Conversational agents that participate in or mediate group interaction introduce challenges that extend beyond supporting individual users, raising new questions about how agents participate in and influence groups. To characterise this emerging design space, we present a systematic review of 53 peer-reviewed studies on group conversational agents (GCAs). We analyse how GCAs intervene in group-level processes, including participation regulation, conflict mediation, task alignment, and execution support. Using concepts from group research as an analytic lens, we organise prior GCA work around recurring group interactional challenges (orientation, conflict, alignment, and execution), and examine the roles agents are designed to play in addressing these challenges. We find that GCAs are predominantly designed as short-term, role-bounded interventions targeting isolated challenges in bounded interactional contexts. We further identify recurring structural tensions in GCA design, including tradeoffs between visibility and discretion, proactivity and group autonomy, and agent authority and group ownership. Together, these findings clarify how current GCAs are positioned within group interaction, surface the implicit assumptions embedded in their designs, and outline open questions for future research on conversational agents as group-level interventions.

The dissemination of scholarly research is critical, yet researchers often lack the time and skills to create engaging content for popular media such as short-form videos. To address this gap, we explore the use of generative AI to help researchers transform their academic papers into accessible video content. Informed by a formative study with science communicators and content creators (N = 8), we designed PaperTok, an end-to-end system that automates the initial creative labor by generating script options and corresponding audiovisual content from a source paper. Researchers can then refine based on their preferences with further prompting. A mixed-methods user study (N = 18) and crowdsourced evaluation (N = 100) demonstrate that PaperTok’s workflow can help researchers create engaging and informative short-form videos. We also identified the need for more fine-grained controls in the creation process. To this end, we offer implications for future generative tools that support science outreach.

The rise of Large Language Models (LLMs) has ushered in a wave of conversational search engines that allow people to engage in dialogues with LLM-infused chatbots to seek information. As people tend to infer personalities from digital social interactions, and given that personality cues have been shown to affect credibility, these perceptions of chatbot design may shape how users assess the credibility of information in conversational search. In this study, we conducted a controlled online study with 190 participants who assessed conversational search results with chatbots designed to exhibit different levels of personality traits. We found that in conversational search, personality can affect perceptions of credibility. Specifically, perceived conscientiousness and agreeableness of a chatbot can increase credibility, while perceived extraversion and neuroticism can decrease the credibility of the information. This research contributes to our understanding of how conversational interfaces and their personality and persona designs can impact credibility. We also provide design implications for conversational search interfaces based on our findings.

Despite the wealth of knowledge in research papers, practitioners struggle to apply research results to their work due to significant research-practice gaps.This study addresses the rigor-relevance paradox, where academic rigor can undermine the practical relevance of research for designers.Specifically, we explore the potential of large language models (LLMs) to customize translational research artifacts (i.e., design cards) and improve relevance to specific designers' needs.In our preliminary study (𝑁 = 15), designers defined relevance as alignment between the content of the translational artifact and their design context-including target users, modalities/domains, and design stages.Based on these findings, we implemented an LLM-powered pipeline that allows designers to customize research papers into design cards tailored to their contexts.Our evaluation (𝑁 = 20) demonstrated that designers perceived customized artifacts as more relevant, actionable, valid, generative, and inspiring than those without customization-even for less topically related papers-indicating LLM-powered customization can be used to support research translation.

Figure 1: An overview of PosterMate.Consisting of a canvas (left) and a side panel (right), PosterMate assists a user in 1 ○ creating a set of persona agents based on the marketing brief, each of which 2○ provides a set of design feedback on the (i) text, (ii) image, and (iii) theme for the poster draft, along with its preview presented on hover.3 ○ The user can also discuss with the persona agents on a design conflict to reach a conclusion.

OBJECTIVE: Artificial Intelligence (AI)-based approaches for extracting Social Drivers of Health (SDoH) from clinical notes offer healthcare systems an efficient way to identify patients' social needs, yet we know little about the acceptability of this approach to patients and clinicians. We investigated patient and clinician acceptability through interviews. MATERIALS AND METHODS: We interviewed primary care patients experiencing social needs (n = 19) and clinicians (n = 14) about their acceptability of "SDoH autosuggest," an AI-based approach for extracting SDoH from clinical notes. We presented storyboards depicting the approach and asked participants to rate their acceptability and discuss their rationale. RESULTS: Participants rated SDoH autosuggest moderately acceptable (mean = 3.9/5 patients; mean = 3.6/5 clinicians). Patients' ratings varied across domains, with substance use rated most and employment rated least acceptable. Both groups raised concern about information integrity, actionability, impact on clinical interactions and relationships, and privacy. In addition, patients raised concern about transparency, autonomy, and potential harm, whereas clinicians raised concern about usability. DISCUSSION: Despite reporting moderate acceptability of the envisioned approach, patients and clinicians expressed multiple concerns about AI systems that extract SDoH. Participants emphasized the need for high-quality data, non-intrusive presentation methods, and clear communication strategies regarding sensitive social needs. Findings underscore the importance of engaging patients and clinicians to mitigate unintended consequences when integrating AI approaches into care. CONCLUSION: Although AI approaches like SDoH autosuggest hold promise for efficiently identifying SDoH from clinical notes, they must also account for concerns of patients and clinicians to ensure these systems are acceptable and do not undermine trust.

Collaborative care management is an evidence-based approach to integrated psychosocial care for patients with comorbid cancer and depression. Prior work highlights challenges in patient-provider collaboration in navigating parallel cancer care and psychosocial care journeys of these patients. We design and deploy SCOPE , a platform for technology-enhanced collaborative care combining a patient-facing mobile app with a provider-facing registry. We examine SCOPE through a total of 45 interviews with patients and providers conducted in SCOPE 's 15 months of design and development and 24 Months of SCOPE 's deployment for actual care in 6 cancer clinics. We find that: (1) SCOPE supported patient engagement in its underlying collaborative care and behavioral activation interventions, (2) patient-generated data in SCOPE improved patient-provider collaboration between and within in-person sessions, (3) SCOPE supported providers in delivering care and improved care team collaboration, (4) experience with SCOPE created evolving expectations for collaboration around data, and (5) SCOPE 's deployment in actual care surfaced important implementation barriers. We discuss the implications of our findings in terms of designing for engagement with behavioral health interventions, negotiating patient data sharing and provider responsiveness, supporting personalized self-tracking goals in evidence-based interventions, exploring the role of digital health navigators in technology-enhanced care, and the need for flexibility in aligning technology-supported interventions to patient needs.

Student feedback is critical for improving teaching, yet instructors often avoid reading evaluations due to emotional burden and information overload. We present a systematic exploration of how language models can distill and transform student evaluations into adaptive, actionable insights. Through a systematic design space exploration combining 4 feedback strategies (removing harmful content, paraphrasing criticism, sandwiching negatives, adding constructive suggestions) with 4 presentation formats (themes, cards, letters, chatbots), we created six AI-augmented prototypes of teaching evaluations. Interviews with 16 post-secondary instructors revealed that effective use of AI in feedback processing should: (1) support action formation through focused views and divergent thinking, (2) reduce emotional costs while enabling celebration and sharing, (3) facilitate longitudinal engagement and re-contextualization across terms, and (4) maintain transparency and preserve access to original context to build trust. Our work provides design guidelines for AI-augmented feedback systems and demonstrates how language models can adaptively process and present information based on feedback receivers' specific needs and contexts.

Trust is not just a cognitive issue but also an emotional one, yet the research in human-AI interactions has primarily focused on the cognitive route of trust development. Recent work has highlighted the importance of studying affective trust towards AI, especially in the context of emerging human-like LLM-powered conversational agents. However, there is a lack of validated and generalizable measures for the two-dimensional construct of trust in AI agents. To address this gap, we developed and validated a set of 27-item semantic differential scales for affective and cognitive trust through a scenario-based survey study. We then further validated and applied the scale through an experiment study. Our empirical findings showed how the emotional and cognitive aspects of trust interact with each other and collectively shape a person's overall trust in AI agents. Our study methodology and findings also provide insights into the capability of the state-of-art LLMs to foster trust through different routes.

Communicating design implications is common within the HCI community when publishing academic papers, yet these papers are rarely read and used by designers. One solution is to use design cards as a form of translational resource that communicates valuable insights from papers in a more digestible and accessible format to assist in design processes. However, creating design cards can be time-consuming, and authors may lack the resources/know-how to produce cards. Through an iterative design process, we built a system that helps create design cards from academic papers using an LLM and text-to-image model. Our evaluation with designers (N = 21) and authors of selected papers (N = 12) revealed that designers perceived the design implications from our design cards as more inspiring and generative, compared to reading original paper texts, and the authors viewed our system as an effective way of communicating their design implications. We also propose future enhancements for AI-generated design cards.

This paper explores the integration of causal pathway diagrams (CPD) into human-centered design (HCD), investigating how these diagrams can enhance the early stages of the design process. A dedicated CPD plugin for the online collaborative whiteboard platform Miro was developed to streamline diagram creation and offer real-time AI-driven guidance. Through a user study with designers (N = 20), we found that CPD’s branching and its emphasis on causal connections supported both divergent and convergent processes during design. CPD can also facilitate communication among stakeholders. Additionally, we found our plugin significantly reduces designers’ cognitive workload and increases their creativity during brainstorming, highlighting the implications of AI-assisted tools in supporting creative work and evidence-based designs.

Despite the central role that stories play in social movement-building, they are difficult to sustainably document for many reasons. To explore this challenge, this paper describes the design of a community-based conversational storytelling agent (CSA) to document digital stories of housing insecurity. Building on insights from an ongoing grassroots project, the Anti-Eviction Mapping Project, we share how a study initially focused on CSA-support opened an investigation of the role that artificial intelligence may play in housing justice movements. Drawing from 17 interviews with narrators of housing insecurity experiences and collectors of such stories, we find that collectors perceive opportunities to expand means of documentation with multimedia and multi-language support. Meanwhile, some narrators perceive potential for a CSA to offer therapeutic storytelling experiences and document otherwise unrecorded stories. Yet, CSA encounters also surface perils of machine bias, as well as reduced possibilities of human connections and relations.

Card-based design tools–design cards–increasingly present opportunities to support practitioners. However, the breadth and depth of the design card landscape remain underexplored. In this work, we surveyed 103 design practitioners to assess current usages and associated barriers. Additionally, we analyzed and classified 161 decks of design cards from 1952-2020. We held a workshop with four experienced practitioners to generate initial categories, and then coded the remaining decks. We found that the cards contain seven different types of design knowledge: Creative Inspiration; Human Insights; Material & Domain; Methods & Tooling; Problem Definition; Team Building; and Values in Practice. The content of these cards can support designers across design stages; however, most are intended to support the early stages of design (e.g., research and ideation) rather than later design stages (e.g., prototyping and implementation). We share additional patterns uncovered and provide recommendations to support the future development and adoption of these tools.

Many people gather online and form teams with strangers to collaborate on tasks. However, while intrateam trust and cohesion are critical for team performance, such characteristics take time to establish and are harder to build up through computer-mediated communication. Building on prior research that has shown that enhancing familiarity between members can help, we hypothesized that the use of a chatbot to support the familiarization of ad hoc teammates can help their collaboration. As such, we designed IntroBot, a chatbot that builds on an online discussion facilitator framework and leverages the social media data of users to assist their familiarization process. Through a between-subjects study (N=60), we found that participants who used IntroBot reported higher levels of trust, cohesion, and interaction quality, as well as generated more ideas in a collaborative brainstorming task. We discuss insights gained from our study, and present opportunities for the future of chatbot-assisted collaboration.

Identifying patients' social needs is a first critical step to address social determinants of health (SDoH)-the conditions in which people live, learn, work, and play that affect health. Addressing SDoH can improve health outcomes, population health, and health equity. Emerging SDoH reporting requirements call for health systems to implement efficient ways to identify and act on patients' social needs. Automatic extraction of SDoH from clinical notes within the electronic health record through natural language processing offers a promising approach. However, such automated SDoH systems could have unintended consequences for patients, related to stigma, privacy, confidentiality, and mistrust. Using Floridi et al's "AI4People" framework, we describe ethical considerations for system design and implementation that call attention to patient autonomy, beneficence, nonmaleficence, justice, and explicability. Based on our engagement of clinical and community champions in health equity work at University of Washington Medicine, we offer recommendations for integrating patient voices and needs into automated SDoH systems.

Well-intentioned users sometimes enable the spread of misinformation due to limited context about where the information originated and/or why it is spreading. Building upon recommendations based on prior research about tackling misinformation, we explore the potential to support media literacy through platform design. We develop and design an intervention consisting of a tweet trajectory-to illustrate how information reached a user-and contextual cues-to make credibility judgments about accounts that amplify, manufacture, produce, or situate in the vicinity of problematic content (AMPS). Using a research through design approach, we demonstrate how the proposed intervention can help discern credible actors, challenge blind faith amongst online friends, evaluate the cost of associating with online actors, and expose hidden agendas. Such facilitation of credibility assessment can encourage more responsible sharing of content. Through our findings, we argue for using trajectory-based designs to support informed information sharing, advocate for feature updates that nudge users with reflective cues, and promote platform-driven media literacy.

Historically, crafts have been associated with women’s small-scale creative production in the home, equated with hobbies or amateur production, and devalued in comparison to both art and industrial production. During its early years, Etsy was seen as a champion of “handmade”, bringing visibility to crafts and providing economic value. This paper presents results of a qualitative study with 18 small online sellers of Etsy platform. Our study shows that Etsy’s sociotechnical design results in a high burden of invisible labor for sellers, including categories of labor not replicated by other online platforms. These new categories include negotiation and articulation work around defining and defending “handmade” products, understanding one’s intellectual property and how (and whether) to defend that IP elsewhere on the platform, understanding working of platform algorithms and adapting to changing platform regulations. Our findings provide new ways to frame the challenges faced by producers/sellers on emerging marketplace platforms.

ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) is considered one of the premium forums for research on accessibility. Recently, Mack et al. shed light on the demographics, goals, research methodologies, and evolution of accessibility research over time. We extend their work by exploring the frequencies and trends of disability categories and computer science research domains in publications at ASSETS (N=1,678). Our results show that disability categories and research domains varied significantly across the publication years. We found that in the past 10 years, publications targeting Mental-Health-Related disabilities and the research domain of AR/VR show an increasing trend. In opposition, Gaming, Input Methods/Interaction Techniques, and User Interfaces domains portray a decreasing trend. Additionally, our results show that the majority of the publications utilize the AI/ML/CV/NLP domain (19%) and focus on people with visual disabilities (42%). We share our preliminary exploration results and identify avenues for future work.

Goal setting is critical to achieving desired changes in life. Many technologies support defining and tracking progress toward goals, but these are just some parts of the process of setting and achieving goals. People want to set goals that are more complex than the ones supported through technology. Additionally, people use goal-setting technologies longitudinally, yet the understanding of how people's goals evolve is still limited. We study the collaborative practices of mental health therapists and clients for longitudinally setting and working toward goals through semi-structured interviews with 11 clients and 7 therapists who practiced goal setting in their therapy sessions. Based on the results, we create the Longitudinal Goal Setting Model in mental health, a three-stage model. The model describes how clients and therapists select among multiple complex problems, simplify complex problems to specific goals, and adjust goals to help people address complex issues. Our findings show collaboration between clients and therapists can support transformative reflection practices that are difficult to achieve without the therapist, such as seeing problems through new perspectives, questioning and changing practices, or addressing avoided issues.

Social media, blogs, podcasts, and other computer-mediated communication technology have become an integral way for the public to access and engage with research. However, despite the evolving challenges researchers face navigating these platforms, and the high stakes of online science communication, relatively little HCI research has focused on understanding and supporting online science communication through these participatory platforms. Through a review of the literature and a set of interviews with HCI researchers (n = 24), we identify challenges currently facing researchers who try to engage with the public about their work, and establish a research agenda for HCI to study, design, and evaluate technology to support science communication. Specifically, we advocate for the design of tools to support audience analytics, automated summary and outreach workflows, and providing quantitative and qualitative feedback about online outreach efforts, as well as additional research to elucidate the impacts of self-directed science communication efforts and the evolving roles of scientists on the participatory web. With shifting online platforms placing researchers in the role of advocates and participants in science communication, understanding and supporting these interactions is now more important than ever.

Conversational interfaces have risen in popularity as businesses and users adopt a range of conversational agents, including chatbots and voice assistants. Although guidelines have been proposed, there is not yet an established set of usability heuristics to guide and evaluate conversational agent design. In this paper, we propose a set of heuristics for conversational agents adapted from Nielsen’s heuristics and based on expert feedback. We then validate the heuristics through two rounds of evaluations conducted by participants on two conversational agents, one chatbot and one voice-based personal assistant. We find that, when using our heuristics to evaluate both interfaces, evaluators were able to identify more usability issues than when using Nielsen’s heuristics. We propose that our heuristics successfully identify issues related to dialogue content, interaction design, help and guidance, human-like characteristics, and data privacy.

Live streaming is a rapidly growing industry, with millions of content creators using platforms like Twitch to share games, art, and other activities. However, with this rise in popularity, most streamers often fail to attract viewers and grow their platforms. Analytic tools—which have shown success in other business and learning contexts—may be one potential solution, but their use in streaming settings remains unexplored. In this study, we focused on game streaming and interviewed 18 game streamers on Twitch and Mixer about their information needs and current use of tools, supplemented by explorations into their Discord communities. We find that streamers have a range of content, marketing, and community information needs, many of which are not being met by available tools. We conclude with design implications for developing more streamer-centered analytics for video game streamers.

Sustained participation is critical to the success of digital citizen-science initiatives, yet much of the current literature focuses on mapping people’s motives to engage without considering the extent to which participation is sustained over time. We conducted a year-long experimental study (n=85) “in-thewild” to explore the effects of human-value orientations on the use of digital citizen-science tools. Participants took part in both the co-design and use of digital citizen-science tools in Lappeenranta, Finland from 2018–2019. Our statistical analysis finds evidence of relations between value orientations, sustained participation, and the number and quality of digital interactions. Specifically, we find that value orientations are linked with different usage patterns. For instance, people with a stronger openness-to-change (OTC) values tended to use the mobile application to check others’ submissions, even when they had nothing to submit, whereas people with stronger security values mostly used the application when they had something relevant to submit. Further understanding the influence of human values in digital citizen science is a promising area for future research that could contribute to a) guide the design of incentive mechanisms, b) understand user experiences in online communities, and c) inform the design and evaluation of digital citizen-science technologies.

Online sources such as social media have become increasingly important for the proliferation of science news, and past research has shown that reading user-generated comments after an article (i.e. in typical online news and blog formats) can impact the way people perceive the article. However, recent studies have shown that people are likely to read the comments before the article itself, due to the affordances of platforms like Reddit which display them up-front. This leads to questions about how comments can affect people's expectations about an article, and how those expectations impact their interest in reading it at all. This paper presents two experimental studies to better understand how the presentation of comments on Reddit affects people's engagement with science news, testing potential mediators such as the expected difficulty and quality of the article. Study 1 provides experimental evidence that difficult comments can reduce people's interest in reading an associated article. Study 2 is a pre-registered follow-up that uncovers a similarity heuristic; the various qualities of a comment (difficulty, information quality, entertainment value) signal that the article will be of similar quality, ultimately affecting participants' interest in reading it. We conclude by discussing design implications for online science news communities.

As an online community for discussing research findings, r/science has the potential to contribute to science outreach and communication with a broad audience. Yet previous work suggests that most of the active contributors on r/science are science-educated people rather than a lay general public. One potential reason is that r/science contributors might use a different, more specialized language than used in other subreddits. To investigate this possibility, we analyzed the language used in more than 68 million posts and comments from 12 subreddits from 2018. We show that r/science uses a specialized language that is distinct from other subreddits. Transient (newer) authors of posts and comments on r/science use less specialized language than more frequent authors, and those that leave the community use less specialized language than those that stay, even when comparing their first comments. These findings suggest that the specialized language used in r/science has a gatekeeping effect, preventing participation by people whose language does not align with that used in r/science. By characterizing r/science's specialized language, we contribute guidelines and tools for increasing the number of contributors in r/science.

Depression is common but under-treated in patients with cancer, despite being a major modifiable contributor to morbidity and early mortality. Integrating psychosocial care into cancer services through the team-based Collaborative Care Management (CoCM) model has been proven to be effective in improving patient outcomes in cancer centers. However, there is currently a gap in understanding the challenges that patients and their care team encounter in managing co-morbid cancer and depression in integrated psycho-oncology care settings. Our formative study examines the challenges and needs of CoCM in cancer settings with perspectives from patients, care managers, oncologists, psychiatrists, and administrators, with a focus on technology opportunities to support CoCM. We find that: (1) patients with co-morbid cancer and depression struggle to navigate between their cancer and psychosocial care journeys, and (2) conceptualizing co-morbidities as separate and independent care journeys is insufficient for characterizing this complex care context. We then propose the parallel journeys framework as a conceptual design framework for characterizing challenges that patients and their care team encounter when cancer and psychosocial care journeys interact. We use the challenges discovered through the lens of this framework to highlight and prioritize technology design opportunities for supporting whole-person care for patients with co-morbid cancer and depression.

Using scientific discoveries to inform design practice is an important, but difficult, objective in HCI. In this paper, we provide an overview of Translational Science in HCI by triangulating literature related to the research-practice gap with interview data from many parties engaged (or not) in translating HCI knowledge. We propose a model for Translational Science in HCI based on the concept of a continuum to describe how knowledge progresses (or stalls) through multiple steps and translations until it can influence design practice. The model offers a conceptual framework that can be used by researchers and practitioners to visualize and describe the progression of HCI knowledge through a sequence of translations. Additionally, the model may facilitate a precise identification of translational barriers, which allows devising more effective strategies to increase the use of scientific findings in design practice.

Online discussion websites, such as Reddit's r/science forum, have the potential to foster science communication between researchers and the general public. However, little is known about who participates, what is discussed, and whether such websites are successful in achieving meaningful science discussions. To find out, we conducted a mixed-methods study analyzing 11,859 r/science posts and conducting interviews with 18 community members. Our results show that r/science facilitates rich information exchange and that the comments section provides a unique science communication document that guides engagement with scientific research. However, this community-sourced science communication comes largely from a knowledgeable public. We conclude with design suggestions for a number of critical problems that we uncovered: addressing the problem of topic newsworthiness and balancing broader participation and rigor.

The process of settling in a new country can be extremely challenging, entailing various information needs to cope with rapid changes and adjustments to a new environment. Through interviews with 16 Korean immigrants in the United States, we explored their information behaviors in the settlement process. In line with prior work (Shoham & Strauss, 2008), we found that Korean immigrants needed various types of information: housing, work, banking, transportation, law, school, health, and language. Out of these information types, the Korean immigrants prioritized information for education and struggled to seek health and legal information. We further uncovered that various information needs are closely intertwined and found an additional type of information need: to build a new social network after migration. They often used Information and Communication Technologies (ICTs) as information sources while adapting the ICT infrastructures of the U.S. into their information practices. ICTs enabled them to build and maintain “local” and “global” identity; however, they may struggle to assess user‐generated content in the new context. We noted that their strong use of ICTs for intraethnic interaction might slow down their integration into the host society. We discuss implications for future work to support immigrants' settlement in the host country.

While numerous design methods used in industry help designers rapidly brainstorm design ideas, few help them to use theory in the design process. Behavior change theories can support such design activities as understanding, ideating, sketching, and prototyping. We present the Behavior Change Design Sprint (BCDS), a design process for applying behavior change theories to the design process and for prototyping behavior change technologies. BCDS facilitates the application of theories into the design process through a series of exercises that help designers identify intervention placement and project behavioral outcomes, conduct more focused ideation, and advocate for their design rationale. We present our process to create the sprint and findings from a series of sprint deployments.

Conversational agents stand to play an important role in supporting behavior change and well-being in many domains. With users able to interact with conversational agents through both text and voice, understanding how designing for these channels supports behavior change is important. To begin answering this question, we designed a conversational agent for the workplace that supports workers' activity journaling and self-learning through reflection. Our agent, named Robota, combines chat-based communication as a Slack Bot and voice interaction through a personal device using a custom Amazon Alexa Skill. Through a 3-week controlled deployment, we examine how voice-based and chat-based interaction affect workers' reflection and support self-learning. We demonstrate that, while many current technical limitations exist, adding dedicated mobile voice interaction separate from the already busy chat modality may further enable users to step back and reflect on their work. We conclude with discussion of the implications of our findings to design of workplace self-tracking systems specifically and to behavior-change systems in general.

Exercise plans help people implement behavior change. Crowd workers can help create exercise plans for clients, but their work may result in lower quality plans than produced by experts. We built CrowdFit, a tool that provides feedback about compliance with exercise guidelines and leverages strengths of crowdsourcing to create plans made by non-experts. We evaluated CrowdFit in a comparative study with 46 clients using exercise plans for two weeks. Clients received plans from crowd planners using CrowdFit, crowd planners without CrowdFit, or from expert planners. Compared to crowd planners not using CrowdFit, crowd planners using CrowdFit created plans that are more actionable and more aligned with exercise guidelines. Compared to experts, crowd planners created more actionable plans, and plans that are not significantly different with respect to tailoring, strength and aerobic principles. They struggled, however, to satisfy exercise requirements of amount of exercise. We discuss opportunities for designing technology supporting physical activity planning by non-experts.

Despite much recent progress in conversational systems, the vision of a truly "intelligent" agent is still far from being realized. Part of the reason is that current applications focus on offering conversational alternatives to GUI supported tasks. In my research I am focusing on application of dialogue-based interaction in area of self-learning in health behavior change, domain in which conversation can offer unique value. Yet, to be effective in this domain a conversational system needs to be "intelligent". In my research I define aspects of intelligence crucial for supporting long-term self-learning in behavior change through conversation and address the technical as well as design challenges of enabling such intelligent applications.

Prior research has examined the use of Social Question and Answer (Q&A) websites for answer and help seeking. However, the potential for these websites to support domain learning has not yet been realized. Helping users write effective answers can be beneficial for subject area learning for both answerers and the recipients of answers. In this study, we examine the utility of crowdsourced, criteria-based feedback for answerers on a student-centered Q&A website, Brainly.com. In an experiment with 55 users, we compared perceptions of the current rating system against two feedback designs with explicit criteria (Appropriate, Understandable, and Generalizable). Contrary to our hypotheses, answerers disagreed with and rejected the criteria-based feedback. Although the criteria aligned with answerers' goals, and crowdsourced ratings were found to be objectively accurate, the norms and expectations for answers on Brainly conflicted with our design. We conclude with implications for the design of feedback in social Q&A.

Donation-based support for open, peer production projects such as Wikipedia is an important mechanism for preserving their integrity and independence. For this reason understanding donation behavior and incentives is crucial in this context. In this work, using a dataset of aggregated donation information from Wikimedia's 2015 fund-raising campaign, representing nearly 1 million pages from English and French language versions of Wikipedia, we explore the relationship between the properties of contents of a page and the number of donations on this page. Our results suggest the existence of a reciprocity mechanism, meaning that articles that provide more utility value attract a higher rate of donation. We discuss these and other findings focusing on the impact they may have on the design of banner-based fundraising campaigns. Our findings shed more light on the mechanisms that lead people to donate to Wikipedia and the relation between properties of contents and donations.

In this paper we present ReactionBot, a system that attaches emoji based on users' facial expressions to text messages on Slack. Through a study of 16 dyads, we found that ReactionBot was able to help communicate participants' affect, reducing the need for participants to self-react with emoji during conversations. However, contrary to our hypothesis, ReactionBot reduced social presence (behavioral interdependence) between dyads. Post study interviews suggest that the emotion feedback through ReactionBot indeed provided valuable nonverbal cues: offered more genuine feedback, and participants were more aware of their own emotions. However, this can come at the cost of increasing anxiety from concerns about negative emotion leakage. Further, the more active role of the system in facilitating the conversation can also result in unwanted distractions and may have attributed to the reduced sense of behavioral interdependence. We discuss implications for utilizing this type of cues in text-based communication.

Academic research can offer insights for HCI practitioners, yet past work shows that research findings are rarely used in industry. We interviewed 22 design practitioners to identify why they do not use academic research and why and how they use other resources at work. We contribute recommendations for the design of translational resources to bridge the gap between theory and practice in HCI. We recommend ways to create theory-driven examples tailored to specific activities: understanding, brainstorming, building, and advocacy. Additionally, practitioners prefer actionable guidance and see prescriptive recommendations and downloadable design patterns as most useful. Design-oriented filters, support for mapping design challenges to research keywords, and visual galleries of examples from theory have the potential to facilitate designers' search processes. Finally, translational resources and discussion features can be integrated into tools for designers and academics to support cross-community collaboration.

Social systems and applications often rely on message triggers to promote, remind and even persuade people to perform certain actions. However, repeated exposure to these triggers can lead to boredom, annoyance and decreased engagement. While existing research suggests that diversification of trigger contents may mitigate these issues, no systematic way of introducing it has been proposed. This paper proposes two message diversification strategies based on the use of cognitive spaces: 1) target-diverse -- using concepts cognitively close to the targeted action; and 2) self-diverse -- using concepts cognitively close to the message's recipient. Through a controlled experiment we found that the self-diverse strategy reduces annoyance and boredom from repeated exposure and that both strategies increase perceived informativeness and helpfulness of the triggers. In a subsequent 2-week long field deployment focused on assessing the effects of the self-diverse strategy, we found that this strategy results in higher activity completion through supporting awareness, providing more information, and making the triggers more personally relevant. These diverse triggers are perceived as motivators rather than simple reminders. We conclude with insights on how to design and generate diverse messages.

Targeted responder model is a recent approach in providing initial treatment to cardiac arrest patients. In this model, a group of trained responders are dispatched via mobile devices to nearby cardiac arrests. While prior work shows that targeted responder programs are successful in reducing average response time, less than a quarter of the responders who receive the notification of a nearby cardiac arrest travel to the scene of event. This study is an attempt to better understand barriers to respond in targeted responder programs. We conducted a weeklong diary study and focus groups with 12 participants. We identified four categories of barriers that emerge and we discussed the design implications of our findings within the broader context of location-based crowdsourcing.

Understanding whether and how motivation affects participation in online experiments is critical because who contributes and how they contribute can affect the validity of findings. Analyzing data from 7,674 participants across three different studies on the volunteer-based online experiment platform LabintheWild, we identified five motivation types for participating: boredom, comparison, fun, science, and self-learning. We found that these motivation types affect study selection, attention, and dropouts. Participants who were highly motivated by boredom paid less attention and were more likely to dropout than those who were motivated by the possibility of contributing to science. We additionally show that motivation can impact study results and suggest how researchers can take participants' motivation into account when designing and analyzing data from volunteer-based online experiments.

Designers often make use of social comparisons to motivate people to perform better. In this paper, we present the concept of closeness to comparison to improve the efficacy of social comparison feedback. Specifically, we test two design strategies related to closeness: (1) comparing users to a target described as a similarly experienced player and (2) adjusting the visual representation of performance so player scores appear closer to the comparison target. We evaluate the effects of these strategies for social comparison on player performance in an online game. In a controlled experiment with 425 participants, both feedback techniques improved game performance, but only for experienced players. We conclude with design implications for helping designers create social comparisons that motivate higher game performance.

Many people struggle with efforts to make healthy behavior changes, such as healthy eating. Several existing approaches promote healthy eating, but present high barriers and yield limited engagement. As a lightweight alternative approach to promoting mindful eating, we introduce and examine crumbs: daily food challenges completed by consuming one food that meets the challenge. We examine crumbs through developing and deploying the iPhone application Food4Thought. In a 3 week field study with 61 participants, crumbs supported engagement and mindfulness while offering opportunities to learn about food. Our 2x2 study compared nutrition versus non-nutrition crumbs coupled with social versus non-social features. Nutrition crumbs often felt more purposeful to participants, but non-nutrition crumbs increased mindfulness more than nutrition crumbs. Social features helped sustain engagement and were important for engagement with non-nutrition crumbs. Social features also enabled learning about the variety of foods other people use to meet a challenge.

Despite the prevalence of theories and interventions related to behavior change, our knowledge on how intention for a target, or planned behavior, changes over time remains limited. This hinders our ability to consider the temporal aspect in our designs to support behavior change. To understand the effect of temporal distances on planned behaviors, we conducted two studies, building on the Theory of Planned Behavior and Constual Level Theory. We found that attitude about the target is more salient the further away the event, as people focus on the why of a behavior. On the other hand, perceived behavior control can influence intention in both near and far future. When the target is in the near future, people generally focus on the feasibility, or the how of the behavior. In the far future, people may also consider factors related to behavior control, if they are motivated to do so (i.e., hold a strong attitude towards the action). Findings help advance the Theory of Planned Behavior and offer strategies for designers and event organizers to motivate planned behaviors that are in the near and far future.

Specific, achievable plans can increase people's commitment to behavior change and increase their likelihood of success. However, many people struggle to create such plans, and available plans often do not fit their individual constraints. We conducted a study with 22 participants exploring the creation of personalized plans by strangers and friends to support three kinds of behavior change: diet, physical activity, and financial. In semi-structured interviews and analyses of the generated plans, we found that friends and strangers can help create behavior change plans that are actionable and help improve behavior. Participants perceived plans more positively when they were personalized to their goals, routines and preferences, or when they could foresee executing the plans with friends - often the friend who created the plan. Participants felt more comfortable sharing information with strangers and they received more diverse recommendations from strangers than friends.

Designing effective incentives is a challenge across many social computing contexts, from attracting crowdworkers to sustaining online contributions. However, one aspect of incentivizing that has been understudied is its impact on participation bias, as different incentives may attract different subsets of the population to participate. In this paper, we present two empirical studies in the crowdworking context that show that the incentive offered influence who participates in the task. Using the Basic Human Values, we found that a lottery reward attracted participants who held stronger openness-to-change values while a charity reward attracted those with stronger self-transcendence orientation. Further, we found that participation self-selection resulted in differences in the task outcomes. Through attracting more self-directed individuals, the lottery reward resulted in more ideas generated in a brainstorming task. Design implications include utilizing rewards to target desired participants and using diverse incentives to improve participation diversity.

Recruiting qualified reviewers, though challenging, is crucial for ensuring a fair and robust scholarly peer review process. We conducted a survey of 307 reviewers of submissions to the International Conference on Human Factors in Computing Systems (CHI 2011) to gain a better understanding of their motivations for reviewing. We found that encouraging high‐quality research, giving back to the research community, and finding out about new research were the top general motivations for reviewing. We further found that relevance of the submission to a reviewer's research and relevance to the reviewer's expertise were the strongest motivations for accepting a request to review, closely followed by a number of social factors. Gender and reviewing experience significantly affected some reviewing motivations, such as the desire for learning and preparing for higher reviewing roles. We discuss implications of our findings for the design of future peer review processes and systems to support them.

E-petitioning has become one of the most important and popular forms of online activism. Although e-petition success is driven by user behavior, users have received relatively little study by HCI and social computing researchers. Drawing from theoretical and empirical work in analogous social computing systems, we identify two potentially competing theories about the trajectories of users in e-petition platforms: (1) "power" users in social computing systems are born, not made; and (2) users mature into "power" users. In a quantitative analysis of data from Change.org, one of the largest online e-petition platforms, we test and find support for both theories. A follow-up qualitative analysis shows that not only do users learn from their experience, systems also "learn" from users to make better recommendations. In this sense, we find that although power users are "born," they are also "made" through both processes of personal growth and improved support from the system.

This paper presents an experiment on the relationship between personal values and reading interests of online articles. Results suggest that individuals' values can predict their topical interests. For example, holding stronger universalism values predict interests towards environmental articles, whereas holding stronger achievement values predict interest towards work-related articles. Findings demonstrate the possibility of targeting based on individuals' personal values, but also highlight certain challenges and limitations when applying this approach for online content.

The theory of values posits that each person has a set of values, or desirable and trans-situational goals, that motivate their actions. The Basic Human Values, a motivational construct that captures people's values, have been shown to influence a wide range of human behaviors. In this work, we analyze people's values and their word use on Reddit, an online social news sharing community. Through conducting surveys and analyzing text contributions of 799 Reddit users, we identify and interpret categories of words that are indicative of user's value orientations. Using the same data, we further report a preliminary exploration on word-based prediction of Basic Human Values.

Searching for health information online has become increasingly common, yet few studies have examined potential negative emotional effects of online health information search. We present results from an experiment manipulating the presentation of search results for common symptoms, which shows that the frequency and placement of serious illness mentions within results can influence perceptions of symptom severity and susceptibility of having the serious illness, respectively. The increase in severity and susceptibility can then lead to higher levels of negative emotional outcomes experienced--including feeling overwhelmed and frightened. Interestingly, health literacy can help reduce perceived symptom severity, and high online health experience actually increases the likelihood that individuals use a frequency-based heuristic. Technological implications and directions for future research are discussed.

In this paper we explore how the decision of partaking in low-cost, low-risk online activism - slacktivism - \'14may affect subsequent civic action. Based on moral balancing and consistency effects, we designed an online experiment to test if signing or not signing an online petition increased or decreased subsequent contribution to a charity. We found that participants who signed the online petition were significantly more likely to donate money to a related charity, demonstrating a consistency effect. We also found that participants who did not sign the petition donated significantly more money to an unrelated charity, demonstrating a moral balancing effect. The results suggest that exposure to an online activism influences individual decision on subsequent civic actions.

Volunteer socializers are members of a community who voluntarily help newcomers become familiar with the popular practices and attitudes of the community. In this paper, we explore the social and psychological predictors of volunteer socializers on reddit, an online social news-sharing community. Through a survey of over 1000 reddit users, we found that social identity, prosocial-orientation and generalized reciprocity are all predictors of socializers in the community. Interestingly, a user's tenure with the online community has a quadratic effect on volunteer socialization behaviors -- new and long-time members are both more likely to help newcomers than those in between. We conclude with design implications for motivating users to help newcomers.

Small point lights (e.g., LEDs) are used as indicators in a wide variety of devices today, from digital watches and toasters, to washing machines and desktop computers. Although exceedingly simple in their output - varying light intensity over time - their design space can be rich. Unfortunately, a survey of contemporary uses revealed that the vocabulary of lighting expression in popular use today is small, fairly unimaginative, and generally ambiguous in meaning. In this paper, we work through a structured design process that points the way towards a much richer set of expressive forms and more effective communication for this very simple medium. In this process, we make use of five different data gathering and evaluation components to leverage the knowledge, opinions and expertise of people outside our team. Our work starts by considering what information is typically conveyed in this medium. We go on to consider potential expressive forms -- how information might be conveyed. We iteratively refine and expand these sets, concluding with ideas gathered from a panel of designers. Our final step was to make use of thousands of human judgments, gathered in a crowd-sourced fashion (265 participants), to measure the suitability of different expressive forms for conveying different information content. This results in a set of recommended light behaviors that mobile devices, such as smartphones, could readily employ.

Icons in graphical user interfaces convey information in a mostly universal fashion that allows users to immediately interact with new applications, systems and devices. In this paper, we define Kineticons - an iconographic scheme based on motion. By motion, we mean geometric manipulations applied to a graphical element over time (e.g., scale, rotation, deformation). In contrast to static graphical icons and icons with animated graphics, kineticons do not alter the visual content or "pixel-space" of an element. Although kineticons are not new - indeed, they are seen in several popular systems - we formalize their scope and utility. One powerful quality is their ability to be applied to GUI elements of varying size and shape from a something as small as a close button, to something as large as dialog box or even the entire desktop. This allows a suite of system-wide kinetic behaviors to be reused for a variety of uses. Part of our contribution is an initial kineticon vocabulary, which we evaluated in a 200 participant study. We conclude with discussion of our results and design recommendations.

This study explores whether certain contribution incentives for online user-generated content can undermine or enhance contributor's credibility. In an online experiment, we found that contributors who are rewarded with donations made in their names are perceived to be more credible than contributors who are financially compensated through revenue-sharing or contribute voluntarily. In addition, disclosing the chosen charity for donation can also impact credibility. Content viewer's self-identification with charity and the congruency between charity and content topic are both factors that may enhance credibility. Our findings lead to practical implications on when and how to use contribution incentives to enhance credibility.

Electronic commerce has enabled a number of online pay-for-answer services. However, despite commercial interest, we still lack a comprehensive understanding of how financial incentives support question asking and answering. Using 800 questions randomly selected from a pay-for-answer site, along with site usage statistics, we examined what factors impact askers' decisions to pay. We also explored how financial rewards affect answers, and if question pricing can help organize Q&A exchanges for archival purposes. We found that askers' decisions are two-part--whether or not to pay and how much to pay. Askers are more likely to pay when requesting facts and will pay more when questions are more difficult. On the answer side, our results support prior findings that paying more may elicit a higher number of answers and answers that are longer, but may not elicit higher quality answers (as rated by the askers). Finally, we present evidence that questions with higher rewards have higher archival value, which suggests that pricing can be used to support archival use.

Community-based question and answer (Q&A) systems facilitate information exchange and enable the creation of reusable knowledge repositories. While these systems are growing in usage and are changing how people find and share information, current designs are inefficient, wasting the time and attention of their users. Furthermore, existing systems do not support signaling and screening of joking and non-serious questions. Coupling Q&A services with instant and text messaging for faster questions and answers may exacerbate these issues, causing Q&A services to incur high interruption costs on their users.

In this work, we introduce the use of tags to support the near synchronous use of instant messaging. As a proof-of-concept, we developed a plug-in in Lotus Sametime, an enterprise IM client. Our plug-in supports tasks that do not need immediate attention and tasks that have deadlines. A trial deployment and survey shows that users can see the potential usefulness of such a tagging system in their IM communication. Furthermore, users rated our design intuitive and easy to use. Longer study is needed to explore communication norms that results from its use.

There is a growing interest in applying market mechanisms to tackle everyday communication problems such as communication interruptions and communication overload. Prior analytic proofs have shown that a signaling and screening mechanism can make senders and recipients of messages better off. However, these proofs make certain assumptions that do not hold in real world environments. For example, these prior works assume that there are no transaction costs in a communication market and that monetary incentives are the only motivators in communication between strangers.

Experience sampling method (or ESM) is a common data collection method to understand user behavior and to evaluate ubiquitous computing technologies. However, ESM studies often demand too much time and commitment from participants, which leads to attrition and low compliance among participants. We introduce a new approach called experience sampling with feedback or ES+feedback that improves compliance by giving feedback to participants through various visualizations. Providing feedback to users makes the information personally relevant and increases the value of the study to participants, which increases their compliance. Our exploratory study shows that ES+feedback increases the compliance rate by 23%.

We present a system for the peripheral display of digital handwritten notes, motivated by the joint observation that people seldom refer back to their notes and that these notes often contain useful information. We describe the user-led design of the system, incorporating interviews, paper prototypes, and interactive prototypes. A preliminary field trial of the system indicates that users derive value from the system both for low-distraction reminding and for serendipitous idea generation. These promising initial results suggest significant scope for future work.

We present a technique for evaluating the usability and effectiveness of ambient displays. Ambient displays are abstract and aesthetic peripheral displays portraying non-critical information on the periphery of a user's attention. Although many innovative displays have been published, little existing work has focused on their evaluation, in part because evaluation of ambient displays is difficult and costly. We adapted a low-cost evaluation technique, heuristic evaluation, for use with ambient displays. With the help of ambient display designers, we defined a modified set of heuristics. We compared the performance of Nielsen's heuristics and our heuristics on two ambient displays. Evaluators using our heuristics found more, severe problems than evaluators using Nielsen's heuristics. Additionally, when using our heuristics, 3-5 evaluators were able to identify 40--60% of known usability issues. This implies that heuristic evaluation is an effective technique for identifying usability issues with ambient displays.