User Reliance on AI Support for Collaborative Partner SelectionWhether choosing teammates for a project or partners for everyday life tasks, people constantly decide with whom to work. However, in these decisions, they often overemphasize characteristics that are not directly relevant to task performance. For example, prioritizing a partner’s trustworthiness for a task where competence is more important for good task performance. Artificial intelligence (AI) systems have the potential to mitigate these judgment errors by guiding decision-makers toward placing greater weight on traits that are more predictive of success for the specific task at hand. Although the potential usefulness of such systems is evident, previous work leaves unclear under what conditions and for what type of AI support people are willing to rely on and trust AI systems for such relational decisions (i.e., selecting a collaboration partner). To bridge this gap, our study examined how different forms of AI support shape users’ perceptions of the AI’s intellectual and social capabilities, their sense of autonomy, and their willingness to rely on and trust in AI when selecting a partner for a collaborative task. To do this, a total of 397 participants designed ideal partners for two collaborative tasks while receiving one of three forms of AI support: (1) recommendation, (2) explanation, or (3) knowledge nudges. This was tested in two different tasks: a competency-based task and a trustworthiness-based task. We found that richer AI support (through explanations or nudges) enhances perceived AI’s social and intellectual capabilities, but not autonomy. Perceptions of intellectual capabilities, rather than social capabilities, predict greater reliance. Both perceptions of AI capabilities mediate the effect of the type of AI support on reliance. Overall, the study advances understanding of human–AI collaboration by revealing how AI design features shape user perceptions and reliance when users need to evaluate and select their collaborators.2026THTiffany Matej Hrkalovic et al.Jheronimus Academy of Data ScienceHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationExplainable AI (XAI)IUI
Developer Interaction Patterns with Proactive AI: A Five-Day Field StudyCurrent in-IDE AI coding tools typically rely on time-consuming manual prompting and context management, whereas proactive alternatives that anticipate developer needs without explicit invocation remain underexplored. Understanding when humans are receptive to such proactive AI assistance during their daily work remains an open question in human-AI interaction research. We address this gap through a field study of proactive AI assistance in professional developer workflows. We present a five-day in-the-wild study with 15 developers who interacted with a proactive feature of an AI assistant integrated into a production-grade IDE that offers code quality suggestions based on in-IDE developer activity. We examined 229 AI interventions across 5,732 interaction points to understand how proactive suggestions are received across workflow stages, how developers experience them, and their perceived impact. Our findings reveal systematic patterns in human receptivity to proactive suggestions: interventions at workflow boundaries (e.g., post-commit) achieved 52% engagement rates, while mid-task interventions (e.g., on declined edit) were dismissed 62% of the time. Notably, well-timed proactive suggestions required significantly less interpretation time than reactive suggestions (45.4s versus 101.4s, 𝑊 = 109.00, 𝑟 = 0.533, 𝑝 = 0.0016), indicating enhanced cognitive alignment. This study provides actionable implications for designing proactive coding assistants, including how to time interventions, align them with developer context, and strike a balance between AI agency and user control in production IDEs.2026NKNadine Kuo et al.JetBrainsAI-Assisted Decision-Making & AutomationGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationIUI
SAFELIFT: Safety-Aware Feedback for Ergonomic Lifting & Injury-Free TasksWork-related musculoskeletal disorders, often caused by unsafe lifting techniques, remain a persistent threat to worker health and safety. We present SAFELIFT, a safety-aware recommender system that automatically detects risky lifting behaviors and generates corrective feedback. Using monocular video input, SAFELIFT extracts ergonomic parameters to compute the Lifting Index (LI) from the Revised NIOSH Lifting Equation. When the LI exceeds a safety threshold, the system produces both graphical and textual recommendations to promote safer postural strategies. Unlike prior approaches, SAFELIFT requires no wearable sensors or multi-camera setups, enabling scalable and low-cost deployment in workplace environments. To assess its effectiveness, we conducted a two-phase evaluation: (1) domain experts (ergonomists, occupational safety professionals, medical staff) assessed the accuracy and relevance of the recommendations, and (2) lay users evaluated different presentation formats, judging their clarity, helpfulness, and trustworthiness. By integrating ergonomics with recommender system design, SAFELIFT contributes to a new class of context-aware, safety-oriented recommendation technologies for occupational health.2026GDGaetano Dibenedetto et al.University of Bari Aldo MoroAI-Assisted Decision-Making & AutomationHuman Pose & Activity RecognitionBehavior Change & Reflection TechnologyIUI
Belief Updating and Delegation in Multi-Task Human–AI Interaction: Evidence from Controlled SimulationsLarge language models (LLMs) increasingly support heterogeneous tasks within a single interface, requiring users to form, update, and act upon beliefs about one system across domains with different reliability profiles. Understanding how such beliefs transfer across tasks and shape delegation is critical for the design of multipurpose AI systems. We report a preregistered experiment (N = 240, 7,200 trials) in which participants interacted with a controlled AI simulation across grammar checking, travel planning, and visual question answering. Delegation was operationalized as a binary reliance decision—accepting the AI’s output versus acting independently—and belief dynamics were evaluated against Bayesian benchmarks. We find three main results. First, participants do not reset beliefs between tasks, instead carrying expectations from prior interactions. Second, within tasks, belief updating follows the Bayesian direction but is substantially conservative. Third, delegation is driven primarily by subjective beliefs about AI accuracy rather than self-confidence, though confidence independently reduces reliance when beliefs are held constant. Based on these results, we discuss implications for expectation calibration, reliance design, and the risks of belief spillovers in deployed LLM-based interfaces.2026SBShreyan Biswas et al.Technical University of DelftHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationExplainable AI (XAI)CHI
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
Connecting Power and Play: Investigating Interactive Energy Harvesting in Battery-Free GamingBattery-free computer gaming offers a vision of sustainable interaction in which games run on hardware that does not require a battery, yet this approach introduces uncertainty due to frequent power failures. Rather than viewing these failures as limitations, this work examines how integrating energy harvesting with application design can encourage users to reimagine and work with such failures, thus shaping behaviour and supporting device use. We present TURNER, a state-of-the-art modular battery-free games console powered by a hand crank and solar cells, created as a research probe to study how energy harvesting mediates the relationship between power and interaction. In a mixed-methods study (N = 60), we explored the influence of energy harvesting on gameplay. Findings show significant variations in harvesting strategies, with interviews surfacing strategies for creating applications that respond to and build on the patterns of system power failure, the ergonomics of energy harvesting, and the value of embedding energy generation into play. Our work offers insights for interactive, sustainable battery-free computers.2026JBJames Scott Broadhead et al.Delft University of TechnologySustainable HCISerious & Functional GamesGamification DesignCHI
When Life Gives You AI, Will You Turn It Into A Market for Lemons? Understanding How Information Asymmetries About AI System Capabilities Affect Market Outcomes and AdoptionAI consumer markets are characterized by severe buyer-supplier market asymmetries. Complex AI systems can appear highly accurate while making costly errors or embedding hidden defects. While there have been regulatory efforts surrounding different forms of disclosure, large information gaps remain. This paper provides the first experimental evidence on the important role of information asymmetries and disclosure designs in shaping user adoption of AI systems. We systematically vary the density of low-quality AI systems and the depth of disclosure requirements in a simulated AI product market to gauge how people react to the risk of accidentally relying on a low-quality AI system. Then, we compare participants’ choices to a rational Bayesian model, analyzing the degree to which partial information disclosure can improve AI adoption. Our results underscore the deleterious effects of information asymmetries on AI adoption, but also highlight the potential of partial disclosure designs to improve the overall efficiency of human decision-making.2026AEAlexander Erlei et al.University of GoettingenExplainable AI (XAI)AI Ethics, Fairness & AccountabilityAlgorithmic Transparency & AuditabilityCHI
“What I’m interested in is something that violates the law”: Regulatory practitioner views on automated detection of deceptive design patternsAlthough deceptive design patterns are subject to growing regulatory oversight, enforcement races to keep up with the scale of the problem. One promising solution is automated detection tools, many of which are developed within academia. We interviewed nine experienced practitioners working within or alongside regulatory bodies to understand their work against deceptive design patterns, including the use of supporting tools and the prospect of automation. Computing technologies have their place in regulatory practice, but not as envisioned in research. For example, investigations require utmost transparency and accountability in all the activities we identify as accompanying dark pattern detection, which many existing tools cannot provide. Moreover, tools need to map interfaces to legal violations to be of use. We thus recommend conducting user requirement research to maximize research impact, supporting ancillary activities beyond detection, and establishing practical tech adoption pathways that account for the needs of both scientific and regulatory activities.2026ARArianna Rossi et al.DIRPOLIS, Sant'Anna School of Advanced StudiesDark Patterns RecognitionPrivacy by Design & User ControlExplainable AI (XAI)CHI
Gold Standard or Gold-Plated? Human Practices of Triple Verification in CSAM TakedownChild sexual abuse material (CSAM) presents a critical challenge for online safety, yet the verification procedures that determine which items are classified as CSAM remain poorly understood. Triple verification (requiring three reviewers to agree) is promoted as a safeguard, but little is known about how it is implemented, how it is perceived by experts, and how voting conditions affect reliability. We address this gap through a mixed-methods study. We interviewed 14 experts from seven organizations (e.g., law enforcement, hotlines, etc.) to map current verification practices, then ran an inter-reliability experiment with Dutch National Police experts who reviewed 2,031 images and videos under different voting conditions (blind vs. non-blind, varied order). Finally, we held a focus group to explore the reasons behind disagreements. We find that practices vary widely, perceptions of triple verification reflect both safeguards and burdens, and expert agreement depends on voting conditions and content type.2026MRMelissa Rottier et al.Delft University of TechnologyOnline Harassment & Counter-ToolsContent Moderation & Platform GovernanceCHI
Designing with Fallibility: Examining the Knowledge Politics of Agency, Methods, and Motivations in Robot Failure ResearchA line of research in HCI and HRI has started to consider robot failures, errors, and breakdowns not as problems to be eliminated, but as opportunities to inform and enrich design. This shift has led to growing interest in how robotic fallibility affects user trust, interaction quality, and system acceptance. In this paper, we inquire into what it means to design with fallibility. Drawing on feminist technoscience, we examine how current approaches frame the roles of designers and users (agency), how research methods shape the phenomena they study (performativity), and how underlying research goals carry ethical and epistemological implications (motivation). In recognizing robotic fallibility as a sociotechnical phenomenon and design research as a world-making practice, we provide design considerations that promote more reflexive, inclusive, and politically aware engagements with (robot) failure in HRI and HCI.2026DMDmitry Muravyov et al.TU DelftHuman-Robot Collaboration (HRC)Technology Ethics & Critical HCIParticipatory DesignCHI
Skill-Adaptive Ghost Instructors: Enhancing Retention and Reducing Over-Reliance in VR Piano LearningMotor-skill learning systems in XR rely on persistent cues. However, constant cueing can induce overreliance and erode memorization and skill transfer. We introduce a skill-adaptive, dynamically transparent ghost instructor whose opacity adapts in real time to learner performance. From a first-person perspective, users observe a ghost hand executing piano fingering with either static or performance-adaptive transparency in a VR piano training. We conducted a within-subjects study (N=30), where learners practiced with traditional Static (fixed-transparency) and our proposed Dynamic (performance-adaptive) modes and were tested without guidance immediately and after a 10-minute retention interval. Relative to Static, the Dynamic mode yielded higher pitch and fingering accuracy and limited error increases. These findings suggest adaptive transparency helps learners internalize fingerings, reducing dependency on external cues and improving short-term skill retention in immersive learning. We discuss design implications for motor-skill learning and outline extensions of this approach to long-term retention and complex tasks.2026THTzu-Hsin Hsieh et al.Delft University of TechnologyVR Medical Training & RehabilitationFull-Body Interaction & Embodied InputImmersion & Presence ResearchCHI
"Tell Them They Are a Responsible Entity, Not a Customer": Understanding Practitioner Challenges in Sector CSIRTsIn this paper, we study the experiences of practitioners in sectoral Computer Security Incident Response Teams (CSIRTs)—specialized teams that mediate between national cybersecurity authorities and the sector constituency. Through interviews with 18 professionals connected to the Informatiebeveiligingsdienst (IBD-CSIRT) for Dutch local governments, we uncover tensions in how key services are valued. For vulnerability notifications, while the CSIRT staff consider them a core service, many constituents hardly mention them, and systemic gaps in information forwarding mean that crucial alerts often never arrive. We extend these insights with 5 interviews across other sector CSIRTs and a validation workshop with 7 participants, all security officers from sector CSIRTs, revealing shared challenges in balancing technical expertise with sector knowledge, building trust-based relationships, and navigating institutional bottlenecks. Our findings contribute the first systematic account of how sector CSIRT professionals understand and perform their role, highlighting the tensions in providing sector-wide support to professionals with differing security needs.2026AEAksel Ethembabaoglu et al.Delft University of TechnologyCybersecurity Training & AwarenessPrivacy Perception & Decision-MakingIoT Device PrivacyCHI
The Bots of Persuasion: Examining How Conversational Agents' Linguistic Expressions of Personality Affect User Perceptions and DecisionsLarge Language Model-powered conversational agents (CAs) are increasingly capable of projecting sophisticated personalities through language, but how these projections affect users is unclear. We thus examine how CA personalities expressed linguistically affect user decisions and perceptions in the context of charitable giving. In a crowdsourced study, 360 participants interacted with one of eight CAs, each projecting a personality composed of three linguistic aspects: attitude (optimistic/pessimistic), authority (authoritative/submissive), and reasoning (emotional/rational). While the CA's composite personality did not affect participants' decisions, it did affect their perceptions and emotional responses. Particularly, participants interacting with pessimistic CAs felt lower emotional state and lower affinity towards the cause, perceived the CA as less trustworthy and less competent, and yet tended to donate more toward the charity. Perceptions of trust, competence, and situational empathy significantly predicted donation decisions. Our findings emphasize the risks CAs pose as instruments of manipulation, subtly influencing user perceptions and decisions.2026HGHüseyin Uğur Genç et al.TU DelftAgent Personality & AnthropomorphismHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationCHI
Self-management for Chronic Illness: A Scoping Review on Designing Virtual Assistants for Patient-Centered CareChronic illnesses (CI) are increasing worldwide, positioning virtual assistants (VAs) as valuable tools for supporting patients in self-management. As effective self-management relies on holistic, patient-centered practices, AI is increasingly integrated into VAs to provide more personalized support. Yet, it is essential that VA design processes remain grounded in participatory approaches prioritizing patients’ values, needs, and lived experiences. To assess the current state of VA design processes, we conducted a scoping review of 55 papers examining how care is framed and patients are involved. Our findings reveal AI-driven VAs prioritize reductionist approaches over holistic care with minimal patient involvement. This highlights a gap between the potential of patient-centered care technology and current implementation practices. Our contributions include (1) a mapping of care dimensions currently implemented in VAs, (2) a categorization of patient roles in the design process, and (3) design implications to expand care dimensions and patient involvement in AI-driven VAs.2026ALAriane Lucchini et al.Delft University of TechnologyChronic Disease Self-Management (Diabetes, Hypertension, etc.)AI-Assisted Decision-Making & AutomationParticipatory DesignCHI
Sex after Cancer: Co-Designing Bespoke Care Technologies for Post-Cancer BodiesCancer treatment leaves survivors with sexual difficulties that extend beyond physical symptoms and permeate many aspects of life, yet these concerns remain neglected in current cancer care. This paper responds to this gap by exploring how bespoke co-designed care technologies can support survivors when grounded in their lived sexual experiences. We conducted trauma-informed, generative workshops with two cancer survivors. The workshops surfaced four themes: gaps in anticipatory care, shifts from lovers to carers, unsettled bodies and selfhood, and navigating fragmented support. Through co-designing, we created Lived Experiences Archive (a ‘zine series of anonymous survivor stories) and BodyTalk (a sensory couple game for rebuilding emotional and physical intimacy). Beyond the artefacts, we contribute a methodological account of co-designing as care and empirical insights into post-cancer sexuality. We demonstrate the epistemic potential of bespoke intimate health technologies to generate situated forms of care and knowledge often overlooked in conventional health technology design.2026COCéline Offerman et al.Delft University of TechnologyMental Health Apps & Online Support CommunitiesEmpathy & Emotional DesignReproductive & Women's HealthCHI
Entangled Life and Code: A Computational Design Taxonomy for Synergistic Bio-Digital SystemsBio-digital systems that merge microbial life with technology promise new modes of computation, combining biological adaptability with digital precision. Yet realizing this potential symbiotically -- where biological and digital agents co-adapt and co-process -- remains elusive, largely due to the absence of a shared vocabulary bridging biology and computing. Consequently, microbes are often constrained to uni-directional roles, functioning as sensors or actuators rather than as active, computational partners in bio-digital systems. In response, we propose a taxonomy and pathways that articulate and expand the roles of biological and digital entities for synergetic bio-digital computation. Using this taxonomy, we analysed 70 systems across HCI, design, and engineering, identifying how biological mechanisms can be mapped onto computational abstractions. We argue that such mappings enable computationally actionable directions that foster richer and reciprocal relationships in bio-digital systems, supporting regenerative ecologies across time and scale while inspiring new paradigms for computation in HCI.2026ZBZoë Breed et al.Delft University of TechnologyShape-Changing Interfaces & Soft Robotic MaterialsEcological Design & Green ComputingComputational Methods in HCICHI
Reflective AI: A Slow Technology Approach for Design EducationThe proliferation of efficiency-focused AI tools in creative processes threatens to undermine critical, reflective practices foundational to design education. This approach can lead to creativity exhaustion and diminished agency among designers and students. As an antidote, we propose Reflective AI: an approach grounded in slow technology principles that reframes AI not as a production tool, but as a medium for reflecting on the creative process itself. This paper presents the Objective Portrait Workshop where design students engaged in slowed data collection, annotation, and model finetuning. Our contribution is threefold: we (1) document a methodology for implementing Reflective AI in design education; (2) provide empirical evidence that slow engagement cultivates reflection on creative processes and technical understanding of AI; and (3) propose material and temporal disentanglement as core mechanisms for Reflective AI practice. This work offers a practical alternative to "fast'' AI, providing methodology that cultivates critical capabilities essential to design.2026VBVera van der Burg et al.Technical University DelftHuman-LLM CollaborationDesign FictionProgramming Education & Computational ThinkingCHI
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
“Are Compliments Bad Now?”: Comparing LLMs and Human Interpretations of Gender Microaggressions in the WorkplaceGender microaggressions are subtle yet persistent forms of discrimination in workplace interactions. While LLMs can detect them in written texts, it remains poorly understood how their interpretations align or diverge from human perspectives and experiences. We present a mixed-method study comparing how LLMs and humans differing in gender identity and lived experience, interpret gender microaggressions in the workplace. Using short dialogues adapted from real-world accounts, we asked 141 participants to rate the likelihood that a scenario contains a microaggression and provide a rationale for their answers. The same tasks were completed by 7 different LLM models. Our analysis reveals significant differences in how humans and LLMs interpret microaggressions, captured in both ratings and rationales, and more interestingly, the effect of gender and lived experience on human interpretations. These findings highlight the need for systems detecting microaggressions to embrace interpretive plurality, and support reflection and awareness while accounting for ambiguity.2026CRCatalina Lagos Rojas et al.Delft University of TechnologyAI Ethics, Fairness & AccountabilityAlgorithmic Fairness & BiasTechnology Ethics & Critical HCICHI
“I followed what felt right, not what I was told”: Autonomy, Coaching, and Recognizing Bias Through AI-Mediated DialogueAbleist microaggressions remain pervasive in everyday interactions, yet interventions to help people recognize them are limited. We present an experiment testing how AI-mediated dialogue influences recognition of ableism. 160 participants completed a pre-test, intervention, and a post-test across four conditions: AI nudges toward bias (Bias-Directed), inclusion (Neutral-Directed), unguided dialogue (Self-Directed), and a text-only non-dialogue (Reading). Participants rated scenarios on standardness of social experience and emotional impact; those in dialogue-based conditions also provided qualitative reflections. Quantitative results showed dialogue-based conditions produced stronger recognition than Reading, though trajectories diverged: biased nudges improved differentiation of bias from neutrality but increased overall negativity. Inclusive or no nudges remained more balanced, while Reading participants showed weaker gains and even declines. Qualitative findings revealed biased nudges were often rejected, while inclusive nudges were adopted as scaffolding. We contribute a validated vignette corpus, an AI-mediated intervention platform, and design implications highlighting trade-offs conversational systems face when integrating bias-related nudges.2026ATAtieh Taheri et al.Carnegie Mellon UniversityAgent Personality & AnthropomorphismMental Health Apps & Online Support CommunitiesExplainable AI (XAI)CHI