Are We Still Under-Serving the Underserved?: An Analysis of 56 Blue-Collar Workers Using 2 Online Information ServicesWe examined the accessibility of online information services (OISs) for underserved communities through a user study involving 56 blue collar participants interacting with a website and an app for five tasks. The blue collar participants were generally unsuccessful on both platforms, with 12.7% (n=7) unable to successfully complete any tasks; a hundred percent required at least minor assistance. Participants were also inefficient, taking 28.62 more steps than optimal (143.1%) on the website and 10.41 more steps (47.3%) on the app. Time inefficiency was also noteworthy, with 535.76 more seconds than optimal (248.0%) on the website and 266.55 more seconds (142.4%) on the app. Though still poor, the app yielded better outcomes with higher success rates and usability ratings. Digital proficiency correlated with success on both platforms, which is good news as this is addressable by OIS providers. Qualitative analysis revealed that many in this underserved population were unaware that these valuable OISs were available to them. Findings underscore the need for OIS providers to prioritize targeted outreach to inform underserved communities that OISs are open and welcoming. Designing OISs with accessibility and simplicity targeted for mobile devices is crucial for bridging the digital literacy gap and empowering underserved communities to engage effectively with OISs.2025JAJinan Y. Azem et al.Universal & Inclusive DesignPrivacy Perception & Decision-MakingParticipatory DesignDIS
When Personas Talk to You: Evaluating the Evolution of User Personas from Static Profiles to Conversational User InterfacesThe development of persona systems provides a possibility for end users to interact with different persona modalities. In a 54-participant randomized controlled experiment, we compare two persona interaction modalities, document and dialogue personas, both generated using AI approaches from survey data. Overall, dialogue personas appear to be perceived more favorably than document personas. However, document personas exhibit a wider range of perceptions, suggesting that experiences with document personas are more polarizing among users. The document personas had higher transparency and were perceived as more complete, but the task completion was perceived as more difficult, although the task success rate was higher. The dialogue personas were perceived as more usable, with a higher System Usability Scale score, and more enjoyable. Our findings provide critical insights into the increasingly important area of persona interaction modalities and the broad paradigm of human-persona interaction.2025IKIlkka Kaate et al.Conversational ChatbotsAgent Personality & AnthropomorphismDIS
"You Always Get an Answer": Analyzing Users' Interaction with AI-Generated Personas Given Unanswerable Questions and Risk of HallucinationWe investigated the presence and acceptance of hallucinations (i.e., accidental misinformation) of an AI-generated persona system that leverages large language models for persona creation from survey data in a 54-user within-subjects experiment. After interacting with the personas, users were given a task to ask the personas a series of questions, including an unanswerable question, meaning the personas lacked the data to answer the question. The AI-generated persona system provided a plausible but incorrect answer half (52%) of the time, and more than half of the time (57%), the users accepted the incorrect answer, and the rest of the time, users answered the unanswerable question correctly (no answer). We found that when the AI-generated persona hallucinated, the user was significantly more likely to answer the unanswerable question incorrectly. Also, for genders separately, when the AI-generated persona hallucinated, it was significantly more likely for the female user and the male users to answer the unanswerable question incorrectly. We identified four themes in the AI-generated persona’s answers and found that users perceive AI-generated persona’s answers as long and unclear for the unanswerable question. Findings imply that personas leveraging LLMs require guardrails to ensure that personas clearly state the possibility of data restrictions and hallucinations when asked unanswerable questions.2025IKIlkka Kaate et al.Human-LLM CollaborationAI Ethics, Fairness & AccountabilityAlgorithmic Transparency & AuditabilityIUI
Who should set the Standards? Analysing Censored Arabic Content on Facebook during the Palestine-Israel ConflictNascent research on human-computer interaction concerns itself with fairness of content moderation systems. Designing globally applicable content moderation systems requires considering historical, cultural, and socio-technical factors. Inspired by this line of work, we investigate Arab users' perception of Facebook's moderation practices. We collect a set of 448 deleted Arabic posts, and we ask Arab annotators to evaluate these posts based on (a) Facebook Community Standards (FBCS) and (b) their personal opinion. Each post was judged by 10 annotators to account for subjectivity. Our analysis shows a clear gap between the Arabs' understanding of the FBCS and how Facebook implements these standards. The study highlights a need for discussion on the moderation guidelines on social media platforms about who decides the moderation guidelines, how these guidelines are interpreted, and how well they represent the views of marginalised user communities.2025WMWalid Magdy et al.University of Edinburgh, School of InformaticsContent Moderation & Platform GovernanceEmpowerment of Marginalized GroupsTechnology Ethics & Critical HCICHI
What is User Engagement?: A Systematic Review of 241 Research Articles in Human-Computer Interaction and BeyondUser engagement (UE) is widely discussed in HCI articles, but its definition, reliability, and application remain elusive. This research conducts a systematic literature review of 241 articles from 1993 to 2023 to analyze how UE is defined and measured within the domain of HCI. Our findings reveal significant definitional inconsistencies that hinder UE’s practical application in HCI research and system design. Based on our findings, we recommend using UE as a categorical label rather than a unified construct until more systematic frameworks are established. We also highlight the need for divergent views of UE across HCI research communities as a valuable avenue to pursue. This divergent view approach can help HCI researchers focus on specific, measurable aspects of UE that align with specific community practices and norms. Our findings also suggest that until such a framework emerges, researchers should be aware of its limitations when using UE as a research construct.2025BJBernard J. Jansen et al.Hamad Bin Khalifa University, Qatar Computing Research Institute; Penn State, College of Information Science and TechnologyUser Research Methods (Interviews, Surveys, Observation)CHI
"Why do we do this?": Moral Stress and the Affective Experience of Ethics in PracticeA plethora of toolkits, checklists, and workshops have been developed to bridge the well-documented gap between AI ethics principles and practice. Yet little is known about effects of such interventions on practitioners. We conducted an ethnographic investigation in a major European city organization that developed and works to integrate an ethics toolkit into city operations. We find that the integration of ethics tools by technical teams destabilises their boundaries, roles, and mandates around responsibilities and decisions. This lead to emotional discomfort and feelings of vulnerability, which neither toolkit designers nor the organization had accounted for. We leverage the concept of moral stress to argue that this affective experience is a core challenge to the successful integration of ethics tools in technical practice. Even in this best case scenario, organisational structures were not able to deal with moral stress that resulted from attempts to implement responsible technology development practices.2025SRSonja Rattay et al.Copenhagen University, Department of Computer Science; Interdisciplinary Transformation University AustriaAI Ethics, Fairness & AccountabilityTechnology Ethics & Critical HCICHI
“There’s Something About Noura”: Exploring Think-Aloud Reasonings for Users' Persona Choice in a Design TaskStakeholders like designers use personas to learn about users. After persona development, stakeholders are usually presented with a persona set. However, there is little research on how stakeholders select a persona from a persona set. A think-aloud analysis with 37 stakeholders who were asked to select a persona for a content design task reveals that persona selection is influenced by comparative, non-comparative, and subjective elements. Persona choice is often made with task compatibility in mind: interests, professions, and education were important contextual factors in our focal task. Storifying is commonly applied by stakeholders, reflecting personas’ narrative nature. The persona’s picture is often evoked, in addition to nationality and name, though demographics do not play a decisive role. Stakeholders refer to a host of persona attributes when explicating their persona choice. Overall, reasonings for persona choice are multifaceted and individualistic, as we might expect given the information-richness of personas.2024SŞSercan Şengün et al.Participatory DesignUser Research Methods (Interviews, Surveys, Observation)DIS
Deus Ex Machina and Personas from Large Language Models: Investigating the Composition of AI-Generated Persona DescriptionsLarge language models (LLMs) can generate personas based on prompts that describe the target user group. To understand what kind of personas LLMs generate, we investigate the diversity and bias in 450 LLM-generated personas with the help of internal evaluators (n=4) and subject-matter experts (SMEs) (n=5). The research findings reveal biases in LLM-generated personas, particularly in age, occupation, and pain points, as well as a strong bias towards personas from the United States. Human evaluations demonstrate that LLM persona descriptions were informative, believable, positive, relatable, and not stereotyped. The SMEs rated the personas slightly more stereotypical, less positive, and less relatable than the internal evaluators. The findings suggest that LLMs can generate consistent personas perceived as believable, relatable, and informative while containing relatively low amounts of stereotyping.2024JSJoni Salminen et al.University of VaasaHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityCHI
OpenMic: Utilizing Proxemic Metaphors for Conversational Floor Transitions in Multiparty Video MeetingsTurn-taking is one of the biggest interactivity challenges in multiparty remote meetings. One contributing factor is that current videoconferencing tools lack support for proxemic cues; i.e., spatial cues that humans use to enact their social relations and intentions. While more recent tools provide support for proxemic metaphors, they often focus on approach and leave-taking rather than turn-taking. In this paper, we present OpenMic, a videoconferencing system that utilizes proxemic metaphors for conversational floor management by providing 1) a Virtual Floor that serves as a fixed-feature space for users to be aware of others' intention to talk, and 2) Malleable Mirrors, which are video and screen feeds that can be continuously moved and resized for conversational floor transitions. Our exploratory user study found that these system features can aid the conversational flow in multiparty video meetings. With this work, we show potential for embedding proxemic metaphors to support conversational floor management in videoconferencing systems.2023EHErzhen Hu et al.University of VirginiaRemote Work Tools & ExperienceKnowledge Management & Team AwarenessCHI