Participatory AI Justice in HCI: A Scoping ReviewParticipatory design is increasingly used to address the negative social impacts of artificial intelligence (AI), aiming for more inclusive and equitable innovation. However, it can inadvertently reproduce injustice and reinforce power imbalances, even with good intentions. While the HCI community is critical of these issues, it remains challenging for AI researchers and policy-makers to act upon these critiques. This paper presents a scoping review of Participatory AI research in HCI discussed through the lens of design justice. The goal is to provide a richer understanding of how current PAI work engages with justice and what the stakes and barriers are to putting justice principles in action. We conclude with raising methodological questions on the roles of researchers and partnership with communities, and the essential but instrumental role of artefacts in supporting knowledge production and social change. The work contributes to a holistic understanding of the current takes and stakes of Participatory AI in critical human-computer interaction research.2026MLMaria Luce Lupetti et al.Politecnico di TorinoParticipatory DesignAI Ethics, Fairness & AccountabilityTechnology Ethics & Critical HCICHI
Going Beyond the I With CI: an Interview-based Design SpaceDeaf and hard-of-hearing (DHH) individuals using cochlear implants (CIs) often have regular jobs or enroll in mainstream education where they face complex social challenges. While first HCI interventions targeted this group’s communication skills, or compensated for limited sound perception, we instead focused on experiential aspects like fatigue and feeling different from others. We moved beyond individual-focused design by engaging interaction-partners to share responsibility for overcoming social barriers. This work identifies generative, intermediate-level design knowledge, addressing common interaction-level challenges. A design-oriented, thematic analysis of interviews with 14 CI users revealed four subsequent themes: invisible, shifting hearing demands; misunderstandings and social impact; strategies for managing interaction barriers; and emotional, relational costs. Mapping these themes to HCI concepts like seamfulness, social translucence, and proxemics highlights open-ended, concrete design opportunities that support socializing beyond functional access. Framing interaction success as shared responsibility broadens inclusive design discourse for DHH populations and wider disability design spaces.2026CLClaudia Alessandra Libbi et al.Leiden UniversityDeaf & Hard-of-Hearing Support (Captions, Sign Language, Vibration)Cognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Universal & Inclusive DesignCHI
Remembering with Reminiscope: Codesigning with Generative AI for Reminiscence Among Older AdultsGenerative AI has shown the potential to support older adults to reminisce about the past by producing personalized memory-related content despite the person's varied ability to elaborate or the lack of memory cues. We present two studies to investigate how generative AI can support older adults in individual and group reminiscence. In Study 1, we conducted individual co‑design sessions with 16 older adults, during which participants created textile collages inspired by personal memories and then used generative AI to transform these creations into memory‑related video content. In the second study, we incorporate the textile collages and AI-generated videos into an interactive artifact, Reminiscope, and introduce it in a series workshops with 15 participants (with 14 returning participants from Study 1) to support group reminiscence. Findings from these studies reveal how older adults’ perspectives towards collaborating with generative AI for creating memory-related content, and their experiences of engaging with an AI‑enhanced interactive artifact during shared reminiscence activities. Our work contributes to the emerging trend of leveraging generative AI to support reminiscence in older adults, and provide design implications for future reminiscence technologies.2026LZLisha Zhu et al.School of designGenerative AI (Text, Image, Music, Video)Inclusive DesignElderly Care & Dementia SupportCHI
Mapping Social Media Dependency: Functional and Psychological Platform Reliance as Mechanisms of Digital VulnerabilitySocial media dependency is a central mechanism through which digital vulnerability takes shape, making it critical to understand for research, design, and policy. This study distinguishes between functional dependency (needs-based reliance) and psychological dependency (compulsive engagement) and investigates how these dimensions intersect. We surveyed 873 adult users across Europe, measuring both dependency forms alongside demographics, well-being, motivations, platform choice, and exposure to manipulative design features. Latent profile analysis and multinomial logistic regression revealed five distinct dependency profiles: functional use, low-dependency pragmatic use, high-dependency social use, moderate-dependency hedonic use, and very high-dependency multi-motivated use. These findings show dependency is not uniform but layered and dynamic, shifting with users’ circumstances and socio-technical contexts. By situating dependency within both individual and design-related factors, the study advances theoretical debates on digital vulnerability and offers a profiles-based lens that helps inform the design of more autonomy-supportive social media platforms.2026JSJanneke M. Schokkenbroek et al.Delft University of TechnologySocial Platform Design & User BehaviorDark Patterns RecognitionPrivacy Perception & Decision-MakingCHI
Verbal Descriptors for Electrotactile StimulationElectrotactile stimulation can evoke a wide range of sensations, including taps, squeezes, and strokes. Although verbal descriptors are available for vibrotactile and ultrasound stimuli, a comprehensive list has not been developed for electrotactile experiences. To address this, we used a text normalization approach to generate descriptors for wearable electrotactile research and design. In Experiment 1 (N=14), Dutch participants provided 504 open-ended descriptions in response to 36 electrotactile stimuli on the forearm. These were processed into 71 unique English descriptors with considerable inter-rater reliability. Experiment 2 (N=24) evaluated a reduced list of 42 descriptors under additional stimulation conditions, showing robust and consistent descriptor usage, also across varying stimulus intensities. This list partially overlaps with previous non-electrotactile descriptor lists but also includes terms that seem to be unique to electrotactile sensations. Altogether, our findings contribute to the development of common verbal descriptors for electrotactile stimulation, supporting future wearable haptic research and design.2026MSMelissa Esmeralda van Schaik et al.University of BoråsElectrical Muscle Stimulation (EMS)Haptic WearablesCHI
Resisting The Matrix: Perceptions of Manipulative Designs Among Vulnerable UsersManipulative designs - so-called "dark patterns'' - as ubiquitous design practices that can cause harm online are increasingly discussed among scholars and policymakers. While academia shows an increased interest in designing counter-interventions, the impact of manipulative designs on users in situations of vulnerability remains understudied. This work presents an interview study to understand how users in situations of vulnerability perceive and resist manipulative designs (N=19). The findings explain how socio-technical aspects mediate interaction with manipulative desgins, their associated harms, and resistance strategies. Lastly, it discusses the relationship between socio-digital inequalities and online vulnerability, providing directions for further research to tackle vulnerability by design.2025LCLorena Sánchez ChamorroToward More Ethical and Transparent Systems and EnvironmentsCSCW
Changing Health Goals with Personal InformaticsPeople’s health goals change as their contexts, needs, and values evolve. Personal informatics (PI) literature recognizes the importance of supporting goal change; however, little is known about how these tools can best support the changes people go through with their goals. To understand how PI are currently involved in goal change, we conducted a survey (N=80) and interview (N=10) study with people who had recently changed their health goals. Overall, we found that PI gave limited support to people during goal changes. Changes were driven by four actors (i.e., who and what influences the goal change): internal motivations, contexts, PI, and social surroundings. We further highlight five factors related to goal change (i.e., the ways in which the actors affect goal change): challenge, self-efficacy, changing priorities, learning, and enjoyment. We discuss how PI could better support people in goal change by considering different implementations and interactions of actors and factors.2025TETina Ekhtiar et al.Fitness Tracking & Physical Activity MonitoringDiet Tracking & Nutrition ManagementDIS
Let’s Talk Menopause: Promoting Intergenerational Dialogue about Menopause through DesignMenopause is an important life transition characterised by physiological, emotional, and social changes. It is surrounded by stigma and taboo. Thus, conversations about menopause are often rare, even among family members, and people often don’t know what to expect from menopause. We leverage design and collaborative making to promote (intergenerational) communication about menopause, between mothers and daughters, and between members of a broader audience. We describe a collaborative Research through Design process where we collaborated with six mother-daughter dyads to create material representations capturing and describing their diverse menopause experiences. Iterating on these representations, we designed and exhibited 5 interactive artifacts at Dutch Design Week 2024. We contribute with empirical findings on plural experiences around menopause, present the five artifacts built upon these experiences, and discuss the importance of pluralizing narratives around menopause through design.2025DODaisy O'Neill et al.Empowerment of Marginalized GroupsDesign FictionDIS
Enhancing Visitor Engagement in Interactive Art Exhibitions with Visual-Enhanced Conversational AgentsConversational agents in art exhibitions can enhance user engagement and understanding of artworks by providing contextual information, especially through voice interactions. However, creating a deeper personal connection with art - which often requires direct aesthetic and visual experiences - remains a challenge. This paper examines how integrating visual perception into conversational agents can enhance alignment with visitors' artistic interpretations, thereby fostering deeper engagement with interactive art exhibitions. We introduce a voice-based conversational agent enhanced with visual capabilities via a multimodal large language model (MLLM), allowing the agent to perceive and discuss artworks in real-time with visitors. The system utilizes a simplified Retrieval-Augmented Generation (RAG) architecture, which collects voice inputs, retrieves relevant information from a domain knowledge graph, and uses the LLM to generate conversational responses, which are then converted into voice outputs. A user study with 36 participants, divided into two groups, was conducted to compare the enhanced system with a baseline system that lacked visual input. Results show that the visually enhanced system significantly improved visitor engagement and satisfaction. Content analysis of the conversational transcripts further revealed a wider range of conversational topics, deeper visitor perceptions, and the agent's ability to provide more nuanced, visually-related discussions.2025HHHoang Phuoc Ho et al.Agent Personality & AnthropomorphismSocial & Collaborative VRMuseum & Cultural Heritage DigitizationIUI
Artificial Intimacy: Exploring Normativity and Personalization through Fine-tuning LLM ChatbotsFine-tuning Large Language Models (LLMs) is one response to the critique of LLMs being biased, erasing diversity, and raising ethical concerns. The Artificial Intimacy project employs artistic methods, taking personalization of chatbots to an extreme by fine-tuning LLMs on individual social media data. We find that regular GPT-3 chatbots attempt to circumvent value-laden content through flagging prompts and producing generic non-answers with variable success. While the transactional nature of such output allowed participants to make sense of responses with less personification, fine-tuned models presented value-laden, normative, and familiar personalities, resulting in strong personification as a way of making sense of the interactions. This mimicry of emotional connection resulted in a sense of artificial intimacy creating expectations for reciprocity and consideration that the models cannot express by design. As the commercialization of interactions with chatbots continues, we discuss the ethics of such emotional manipulation and its implications for personalization of LLMs.2025MJMirabelle Jones et al.University of Copenhagen, Dept. Computer ScienceAgent Personality & AnthropomorphismHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityCHI
Facilitators and Barriers of Wearable Stress Management Technology: A Narrative Review of User PerspectivesResearch and technological advancements have driven the development of wearable technology for stress management. Previous reviews primarily focused on its performance and effectiveness in health contexts. In contrast, this review takes a human-centric approach and reviews studies on users’ attitudes and experiences. We conducted a narrative review to identify (1) the facilitators and barriers of wearable stress management technology (WSMT) and (2) design considerations for human-centered WSMT. We identified 28 articles reporting user perspectives on stress management technology, primarily based on evaluation studies in which user perspectives were gathered through qualitative methods. We found five facilitators and barriers of WSMT (i.e., usefulness, functionality/interactivity, seamlessness, user privacy, and technology’s image). Additionally, we synthesized 18 design considerations, highlighted two main design challenges, and proposed a value-sensitive approach for future research. This review adds to the HCI literature by demonstrating the complexity of designing human-centered WSMT and the need for actionable recommendations.2025MBMerel K. N. van den Berg et al.University of Twente, Interaction Design GroupSleep & Stress MonitoringCHI
Selective Trust: Understanding Human-AI Partnerships in Personal Health Decision-Making ProcessAs artificial intelligence (AI) becomes more embedded in personal health technology, its potential to transform health decision-making through personalised recommendations is becoming significant. However, there is limited understanding of how individuals perceive AI-assisted decision-making in the context of personal health. This study investigates the impact of AI-assisted decision-making on trust in physical activity-related health decisions. By employing MoveAI, a GPT-4.0-based physical activity decision-making tool, we conducted a mixed-methods study and conducted an online survey (N=184) and semi-structured interviews (N=24) to explore this dynamic. Our findings emphasise the role of nuanced personal health recommendations and individual decision-making styles in shaping trust in AI-assisted personal health decision-making. This paper contributes to the HCI literature by elucidating the relationship between decision-making styles and trust in the AI-assisted personal health decision-making process and showing the challenges of aligning AI recommendations with individual decision-making preferences.2025SASterre van Arum et al.University of TwenteAI-Assisted Decision-Making & AutomationFitness Tracking & Physical Activity MonitoringCHI
GustosonicSense: Towards understanding the design of playful gustosonic eating experiencesThe pleasure that often comes with eating can be further enhanced with intelligent technology, as the field of human-food interaction suggests. However, knowledge on how to design such pleasure-supporting eating systems is limited. To begin filling this knowledge gap, we designed “GustosonicSense”, a novel gustosonic eating system that utilizes wireless earbuds for sensing different eating and drinking actions with a machine learning algorithm and trigger playful sounds as a way to facilitate pleasurable eating experiences. We present the findings from our design and a study that revealed how we can support the "stimulation", "hedonism", and "reflexivity" for playful human-food interactions. Ultimately, with our work, we aim to support interaction designers in facilitating playful experiences with food.2024YWYan Wang et al.Monash University, Monash UniversityFood Culture & Food InteractionCHI
ReactGenie: A Development Framework for Complex Multimodal Interactions Using Large Language ModelsBy combining voice and touch interactions, multimodal interfaces can surpass the efficiency of either modality alone. Traditional multimodal frameworks require laborious developer work to support rich multimodal commands where the user’s multimodal command involves possibly exponential combinations of actions/function invocations. This paper presents ReactGenie, a programming framework that better separates multimodal input from the computational model to enable developers to create efficient and capable multimodal interfaces with ease. ReactGenie translates multimodal user commands into NLPL (Natural Language Programming Language), a programming language we created, using a neural semantic parser based on large-language models. The ReactGenie runtime interprets the parsed NLPL and composes primitives in the computational model to implement complex user commands. As a result, ReactGenie allows easy implementation and unprecedented richness in commands for end-users of multimodal apps. Our evaluation showed that 12 developers can learn and build a non-trivial ReactGenie application in under 2.5 hours on average. In addition, compared with a traditional GUI, end-users can complete tasks faster and with less task load using ReactGenie apps.2024JYJackie (Junrui) Yang et al.Stanford UniversityVoice User Interface (VUI) DesignGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationCHI
Artful Path to Healing: Using Machine Learning for Visual Art Recommendation to Prevent and Reduce Post-Intensive Care Syndrome (PICS)Staying in the intensive care unit (ICU) is often traumatic, leading to post-intensive care syndrome (PICS), which encompasses physical, psychological, and cognitive impairments. Currently, there are limited interventions available for PICS. Studies indicate that exposure to visual art may help address the psychological aspects of PICS and be more effective if it is personalized. We develop Machine Learning-based Visual Art Recommendation Systems (VA RecSys) to enable personalized therapeutic visual art experiences for post-ICU patients. We investigate four state-of-the-art VA RecSys engines, evaluating the relevance of their recommendations for therapeutic purposes compared to expert-curated recommendations. We conduct an expert pilot test and a large-scale user study (n=150) to assess the appropriateness and effectiveness of these recommendations. Our results suggest all recommendations enhance temporal affective states. Visual and multimodal VA RecSys engines compare favourably with expert-curated recommendations, indicating their potential to support the delivery of personalized art therapy for PICS prevention and treatment.2024BYBereket A. YILMA et al.University of LuxembourgAI-Assisted Decision-Making & AutomationRecommender System UXMedical & Scientific Data VisualizationCHI
Is it just a score? Understanding Training Load Management Practices Beyond Sports Tracking Training Load Management (TLM) is crucial for achieving optimal athletic performance and preventing chronic sports injuries. Current sports trackers provide runners with data to manage their training load. However, little is known about the extent and the way sports trackers are used for TLM. We conducted a survey (N=249) and interviews (N=24) with runners to understand sports tracker use in TLM practices. We found that runners possess some understanding of training load and generally trust their trackers to provide accurate training load-related data. Still, they hesitate to strictly follow trackers’ suggestions in managing their training load, often relying on their intuitions and body signals to determine and adapt training plans. Our findings contribute to SportsHCI research by shedding light on how sports trackers are incorporated into TLM practices and providing implications for developing trackers that better support runners in managing their training load.2024AKArmağan Karahanoğlu et al.University of TwenteMental Health Apps & Online Support CommunitiesFitness Tracking & Physical Activity MonitoringCHI
Grand Challenges in SportsHCIThe field of Sports Human-Computer Interaction (SportsHCI) investigates interaction design to support a physically active human being. Despite growing interest and dissemination of SportsHCI literature over the past years, many publications still focus on solving specific problems in a given sport. We believe in the benefit of generating fundamental knowledge for SportsHCI more broadly to advance the field as a whole. To achieve this, we aim to identify the grand challenges in SportsHCI, which can help researchers and practitioners in developing a future research agenda. Hence, this paper presents a set of grand challenges identified in a five-day workshop with 22 experts who have previously researched, designed, and deployed SportsHCI systems. Addressing these challenges will drive transformative advancements in SportsHCI, fostering better athlete performance, athlete-coach relationships, spectator engagement, but also immersive experiences for recreational sports or exercise motivation, and ultimately, improve human well-being.2024DEDon Samitha Elvitigala et al.Monash UniversityGame UX & Player BehaviorSerious & Functional GamesMental Health Apps & Online Support CommunitiesCHI
Participation Patterns of Interactive Playful Museum Exhibits: Evaluating the Participant Journey Map through Situated ObservationsThe Participant Journey Map (PJM) provides structured insight into participation with interactive play in (semi-)public environments. It supports understanding of participants’ behavior and was developed based on experiences with previously developed playful interfaces, related research and expert interviews. We apply the PJM to interactive playful museum exhibits and evaluate and refine it based on its usage in a situated context. We observed 672 play sessions with 6 interactive playful museum exhibits. The observation data was visualized and analyzed using the PJM. This study shows that the PJM provides a realistic representation of participant behaviour, can be used to identify stagnations and progressions in participation flow, and support identification of influencing design and contextual factors. With this paper we contribute by presenting the PJM as a well-grounded, valuable and realistic framework for evaluating and understanding participation with situated interactive play, based on post-hoc evaluation of multiple interfaces with many users.2023DMDanica Mast et al.Museum & Cultural Heritage DigitizationInteractive Narrative & Immersive StorytellingDIS
Goals for Goal setting: A Scoping Review on Personal InformaticsResearch has extensively explored how personal informatics tools can support people’s health goal setting practices. To understand the current state and reflect on the future of goal setting in personal informatics, we report the results of a scoping review of 51 papers that use and provide design implications for implementing goal setting. Our review highlights six implications for using goal setting in personal informatics tools (clarity, transparency, flexibility, framing and reframing, personalization, and reflection). We find that goal setting is becoming increasingly complex as the number of goals and their characteristics increase. We discuss these insights and point towards the importance of supporting self-efficacy during goal setting, showing adaptive goal evolution over time, reducing burden during goal setting, and framing goals to understand the complexity of health goals and support a holistic view on goal setting.2023TETina Ekhtiar et al.Fitness Tracking & Physical Activity MonitoringSleep & Stress MonitoringDIS
Design Resources in Movement-based Design Methods: a Practice-based CharacterizationMovement-based design methods are increasingly adopted to help design rich embodied experiences. While there are well-known methods in the field, there is no systematic overview to help designers choose among them, adapt them, or create their own. We collected 41 methods used by movement design researchers and employed a practice-based, bottom-up approach to analyze and characterize their properties. We found 17 categories and arranged them into five main groups: Design Resources, Activities, Delivery, Framing, and Context. In this paper, we describe these groups in general and then focus on Design Resources containing the categories of Movement, Space, and Objects. We ground the characterization with examples from empirical material provided by the design researchers and references to previous work. Additionally, we share recommendations and action points to bring these into practice. This work can help novice and seasoned design researchers who want to employ movement-based design methods in their practice.2023JVJosé Manuel Vega-Cebrián et al.Full-Body Interaction & Embodied InputDance & Body Movement ComputingDIS