User Compliance and Awareness towards Persuasive XAI: Investigating the Rhetorical Layer of LLM-generated ExplanationsIn high-stakes decision-making, the acceptance of AI recommendations depends not only on system accuracy but also on how decisions are explained. While prior work on explainable AI has largely focused on transparency and interpretability, less attention has been paid to the persuasive dimension of explanations. To address this gap, we investigate how rhetorical strategies drawn from Cialdini's persuasion theory, when embedded in natural language explanations generated by large language models (LLMs), influence user compliance and their ability to recognize persuasive intent. We conducted a controlled survey study with 129 participants in two application domains---finance and healthcare---where participants evaluated both a baseline and a persuasive explanation for an AI-generated decision across favorable and unfavorable outcomes. In a complementary task, participants rated ten short explanations on perceived persuasiveness and factual strength, enabling us to measure awareness of persuasive intent. Our results show that persuasive explanations significantly increased compliance in the healthcare scenario ($p < .001$), whereas baseline explanations were more effective in finance ($p<.001$), regardless of whether the AI decision was positive or negative. A notable proportion of participants rated explanations containing at least one of Cialdini's persuasion techniques as highly persuasive, yet simultaneously judged them to be factually weaker. Importantly, we found no statistically significant evidence that participants' ability to recognize persuasive intent influenced compliance. These findings highlight the dual role of persuasive explanations: they can enhance foster compliance in sensitive contexts such as healthcare but risk undermining trust in domains like finance. For HCI and IUI, our study underscores that explanations are not neutral vessels of information: their rhetorical form substantially shapes how users perceive and engage with AI-assisted decision-making. Designers of explainable AI systems should therefore carefully balance transparency and persuasion when developing interfaces for high-stakes applications.2026DBDacia Braca et al.Interdisciplinary Transformation University AustriaExplainable AI (XAI)AI-Assisted Decision-Making & AutomationAI Ethics, Fairness & AccountabilityIUI
Calls of Care: Materializing Posthuman Personhood with Conversational Agents in Dementia CareIn designing for dementia, the concept of person-centered care (PCC) effectively shifts attention away from deficit orientations. However, it predominantly focuses on the roles of humans in sustaining personhood, leaving the roles of nonhuman actors in care assemblages under-explored. To address this, we propose a theory–method package that extends PCC with posthumanist perspectives. We applied it in a six-month participatory design process to develop a conversational agent (CA) with four people living with dementia (PlwD), their relatives, and care workers. We report both the design process and analysis of conversations between the CA and one PlwD. Tracing the socio-material and ethical assemblages that emerged in these intra-actions, we identify moments of recognition, validation, holding, and facilitation. Situating these within broader discussions of care, process-oriented ethics, and posthuman design, we illustrate opportunities and limits to design for meaningful experiences of personhood between people with dementia, CAs, and other nonhuman actors.2026RVRalf Vetter et al.Interdisciplinary Transformation UniversityElderly Care & Dementia SupportSocial Robot InteractionRobots in Education & HealthcareCHI
Grand Challenges around Designing Computers’ Control Over Our BodiesAdvances in emerging technologies, such as on-body mechanical actuators and electrical muscle stimulation, have allowed computers to take control over our bodies. This presents opportunities as well as challenges, raising fundamental questions about agency and the role of our body when interacting with technology. To advance this research field as a whole, we brought together expert perspectives in a week-long seminar to articulate the grand challenges that should be tackled when it comes to the design of computers’ control over our bodies. These grand challenges span technical, design, user, and ethical aspects. By articulating these grand challenges, we aim to begin initiating a research agenda that positions bodily control not only as a technical feature but as a central, experiential, and ethical concern for future human–computer interaction endeavors.2026FMFlorian 'Floyd' Mueller et al.Monash UniversityElectrical Muscle Stimulation (EMS)Brain-Computer Interface (BCI) & NeurofeedbackEmpathy & Emotional DesignCHI
Decomposing Autonomy: Explaining AI Technology Acceptance Through a Liberty-Based FrameworkHuman autonomy is a core concept that helps explain the acceptance of and interaction with computer systems and AI technology. However, autonomy is often vaguely defined and conflated with related constructs. This paper disentangles autonomy by integrating the dualistic nature of positive and negative liberty from the perspective of political philosophy. Using an online vignette study with N=194 participants, we show that positive and negative liberty act as correlated but distinct dimensions of the autonomy foundation. While negative liberty predicts the sense of agency, positive liberty is a key dimension for people’s willingness to use technology. We argue that this dualistic stand - positive liberty as the freedom to pursue authentic goals, and negative liberty as the freedom from external constraints - offers a valuable and actionable perspective on human autonomy that can inform future system design and better answer the ambivalent question “how much autonomy is enough”?2026DTDinara Talypova et al.IT:U Interdisciplinary Transformation University AustriaExplainable AI (XAI)AI Ethics, Fairness & AccountabilityPrivacy by Design & User ControlCHI
Visual Sampling Behavior Does not Explain Risk Perception: A Data-Driven xAI InvestigationHow do drivers perceive risk? Understanding what situations and factors cause drivers to perceive situations as critical can improve our understanding of road user behavior and inform automated driving technology. To investigate the factors that shape drivers’ risk perception, we conducted an eye-tracking study with 27 participants who watched dashcam videos and continuously rated the perceived risk of various driving situations. Using the resulting dataset, we developed a computer vision-based machine learning approach that generates explainable predictions of perceived risk from video and eye-tracking data. Our SHAP analysis reveals that the proximity of objects and number of cars in a scene are the most significant contributors to perceived criticality. Most interestingly, while people tend to sample similar objects in critical situations, their risk perception remains highly personal making visual sampling behavior a weak predictor of perceived risk. Overall, our explanations reveal non-linear insights beyond previous work, suggesting that risk perception is not only shaped by visual input, but primarily by cognitive processes which is in line with theoretical models of Situation Awareness. The dataset, source code, and a comprehensive usage guide are publicly available: https://osf.io/cwd6h/?view_only=31a8173570de4b0383f55d52dc784492.2025MLMartin Lorenz et al.Eye Tracking & Gaze InteractionExplainable AI (XAI)AI-Assisted Decision-Making & AutomationAutoUI
SPAT: Situational Prosocial and Aggressive Behavior Perception in Traffic ScaleAutomated vehicles (AVs) reached technological maturity and will soon arrive on streets as traffic participants. Human traffic participants such as drivers, pedestrians, or cyclists will be increasingly confronted with the presence of AVs within their environment, not necessarily knowing or understanding what to expect and how to interact with them. Although AVs are designed to act safely, effective interaction in mixed traffic scenarios will depend on successful communication, interaction, or even negotiation beyond static rules and regulations. Prosocial behavior, such as yielding one's right of way, will be needed to resolve unclear traffic situations or foster traffic flow. However, what are the characteristics of such prosocial behavior, and how to measure this not only for automated vehicles but for all road users? Here, we describe a new scale to measure perceived social behavior in urban traffic scenarios. Through an online survey on \textit{N} = 318 individuals and a validation study, we developed the Situational Prosocial and Aggressive Behavior in Traffic Scale and assessed it psychometrically.2025HİHatice Şahin İppoliti et al.Teleoperated DrivingV2X (Vehicle-to-Everything) Communication DesignAI-Assisted Decision-Making & AutomationAutoUI
Hackathons in Designing Robotic Technology in Dementia Care - Navigating Needs and RelationsDesigning for dementia care is challenging due to its complexity, relational nature, and diverse needs. Meaningful design outcomes require bridging a deep understanding of dementia care and the experiences of people with dementia with technological possibilities and constraints. This paper explores hackathons for facilitating such translations in the design of robotic technologies. We conducted two hackathons - one with researchers and care professionals, and another with HCI Master’s students - integrating ethnographic insights, theoretical framings, first-hand experiences, and designerly knowledge. Participants' reflections highlight how the format fostered open-ended exploration, interdisciplinary collaboration, and mutual learning through tailored inspiration materials and structured design processes. Evaluating the concepts using Kitwood’s framework of person-centred care needs, we find that the hackathons generated meaningful concepts for human and non-human care relations. However, the outcomes also surface ethical considerations related to these relations, emphasising the need for further participatory design processes to refine and situate the outcomes.2025RVRalf Vetter et al.Aging-in-Place Assistance SystemsRobots in Education & HealthcareEmpowerment of Marginalized GroupsDIS
"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
MetaFormer: Domain-Adaptive WiFi Sensing with Only One Labelled Target SampleSheng 等人提出 MetaFormer 框架，通过元学习仅使用一个标注目标样本实现 WiFi 感知的跨域迁移，有效降低标注成本。2024BSBiyun Sheng et al.Context-Aware ComputingUbiquitous ComputingUbiComp
Orientation-Aware 3D SLAM in Alternating Magnetic Field from PowerlinesWang 等人提出基于电力线工频交替磁场的 3D SLAM 方法，实现 Orientation-Aware 定位，解决复杂电磁环境下的导航难题。2024RWRongrong Wang et al.Context-Aware ComputingUbiComp
Parent and Educator Concerns on the Pedagogical Use of AI-Equipped Social RobotsPerella-Holfeld 等人研究家长和教育者对课堂中 AI 社交机器人应用的担忧，探讨其对教学实践和儿童发展的潜在影响。2024FPFrancisco Perella-Holfeld et al.Mental Health Apps & Online Support CommunitiesSocial Robot InteractionInclusive DesignUbiComp