Guiding, Not Railroading: Design and Evaluation of a Multi-Agent System for Narrative Redirection in Role-playing GamesLarge Language Models (LLMs) are poised to revolutionize interactive storytelling, yet they introduce a fundamental HCI challenge: balancing player agency with the coherence of a pre-authored narrative. Existing LLM-driven Game Masters (GMs) often undermine the player experience by being overly compliant, leading to disjointed stories, or by rigidly "railroading" players, which diminishes their sense of freedom. This paper addresses this tension by introducing SENNA, a novel multi-agent AI system designed to maintain narrative adherence in single-player role-playing games. SENNA operationalizes a prewritten adventure by creating and using a Narrative Graph to model the structure of the story, track the player's progress and ensure key plot points are met while maintaining interactivity. A primary contribution of this work is an empirical investigation of narrative redirection, the methods an AI GM can use to guide players back to the intended arc when they deviate. We designed six redirection strategies inspired by expert human GMs and conducted a user study to evaluate their effectiveness. The study combined live gameplay with a within-subjects comparison of different strategies at key narrative junctions. Our findings show that players strongly prefer redirection techniques grounded in the internal logic of the game, such as offering more information, experiencing consequences in the world, and influence from non-player characters. These methods successfully maintained narrative coherence without sacrificing perceived autonomy or immersion, while simplistic "hard denials" were poorly received. Our work contributes an empirically validated framework and actionable design guidelines to create more robust and human-centered AI-driven narrative experiences.2026NJNicolai Hejlesen Jørgensen et al.Aalborg UniversityRole-Playing & Narrative GamesGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationIUI
Polite But Boring? Trade-offs Between Engagement and Psychological Reactance to Chatbot Feedback StylesAs conversational agents become increasingly common in behaviour change interventions, understanding optimal feedback delivery mechanisms becomes increasingly important. However, choosing a style that both lessens psychological reactance (perceived threats to freedom) while simultaneously eliciting feelings of surprise and engagement represents a complex design problem. We explored how three different feedback styles: 'Direct', 'Politeness', and 'Verbal Leakage' (slips or disfluencies to reveal a desired behaviour) affect user perceptions and behavioural intentions. Matching expectations from literature, the 'Direct' chatbot led to lower behavioural intentions and higher reactance, while the 'Politeness' chatbot evoked higher behavioural intentions and lower reactance. However, 'Politeness' was also seen as unsurprising and unengaging by participants. In contrast, 'Verbal Leakage' evoked reactance, yet also elicited higher feelings of surprise, engagement, and humour. These findings highlight that effective feedback requires navigating trade-offs between user reactance and engagement, with novel approaches such as 'Verbal Leakage' offering promising alternative design opportunities.2026SCSamuel Rhys Cox et al.Aalborg UniversityConversational ChatbotsAffective Human-Computer DialogueBehavior Change & Reflection TechnologyCHI
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
"Alone and Adrift in Analytics" - Insights from Long-term Involvements with stroke Clinicians when Using Care Quality Monitoring SystemsCare quality improvement systems (CQIS) allow hospitals to monitor and improve their care by analysing performance data. However, many CQIS fail at helping clinicians improve as they see decreased use and stagnating care quality. In this paper, we investigate the use of one CQIS through ten qualitative activities carried out across five years. We synthesize and present our results regarding specific problems various stakeholders face, exact data visualization tasks that clinicians struggle with, and concrete solutions described by clinicians. Our research proposes steps that designers can take towards integrating data analysis interfaces and automating tasks in CQIS to assist with concrete data visualization problems identified by the 74 clinicians who participated in our study.2026HZHamzah Ziadeh et al.Aalborg UniversityInteractive Data VisualizationMedical & Scientific Data VisualizationAI-Assisted Decision-Making & AutomationCHI
Chaplains' Reflections on the Design and Usage of AI for Conversational CareDespite growing recognition that responsible AI requires domain knowledge, current work on conversational AI primarily draws on clinical expertise that prioritises diagnosis and intervention. However, much of everyday emotional support needs occur in non-clinical contexts, and therefore requires different conversational approaches. We examine how chaplains, who guide individuals through personal crises, grief, and reflection, perceive and engage with conversational AI. We recruited eighteen chaplains to build AI chatbots. While some chaplains viewed chatbots with cautious optimism, the majority expressed limitations of chatbots’ ability to support everyday well-being. Our analysis reveals how chaplains perceive their pastoral care duties and areas where AI chatbots fall short, along the themes of Listening, Connecting, Carrying, and Wanting. These themes resonate with the idea of attunement, recently highlighted as a relational lens for understanding the delicate experiences care technologies provide. This perspective informs chatbot design aimed at supporting well-being in non-clinical contexts.2026JWJoel Wester et al.University of CopenhagenConversational ChatbotsAgent Personality & AnthropomorphismMental Health Apps & Online Support CommunitiesCHI
Impact of Explanation Techniques and Representations on Users Comprehension and Confidence in Explainable AILocal explainability, an important sub-field of eXplainable AI, focuses on describing the decisions of AI models for individual use cases by providing the underlying relationships between a model's inputs and outputs. While the machine learning community has made substantial progress in improving explanation accuracy and completeness, these explanations are rarely evaluated by the final users. In this paper, we evaluate the impact of various explanation and representation techniques on users' comprehension and confidence. Through a user study on two different domains, we assessed three commonly used local explanation techniques---feature-attribution, rule-based, and counterfactual---and explored how their visual representation---graphical or text-based---influences users' comprehension and trust. Our results show that the choice of explanation technique primarily affects user comprehension, whereas the graphical representation impacts user confidence.2025JDJulien Delaunay et al.Explainable AI (XAI)CSCW
Cognitive Forcing for Better Decision-Making: Reducing Overreliance on AI Systems Through Partial ExplanationsIn AI-assisted decision-making, explanations aim to enhance transparency and user trust but can also lead to negligence. In two separate studies, we explore the use of partial explanations to activate cognitive forcing and increase user engagement. In Study~I ($N = 264$), we present participants with weighted graphs and ask them to identify the shortest paths. In Study~II ($N = 210$), participants correct spelling and grammar mistakes in short text segments. In both studies, we provide a solution suggestion accompanied by either no explanation, a full explanation, or a partial explanation. Our results show that partial explanations reduce overreliance on incorrect AI suggestions, performing significantly better than the baseline but not as well as full explanations. Individuals with a high need for cognition benefit more from AI explanations and consequently perform better. Our work suggests that partial explanations can be valuable in domains where reducing overreliance on AI is critical, like medical diagnosis. It also underscores the need to consider explanation effectiveness across different task difficulties, a factor often overlooked in contemporary human-AI studies.2025SJSander de Jong et al.Humans vs. AI for Decision MakingCSCW
The Collaborative Work of Stewardship in Waste Management in Multi-tenant Apartment BuildingsThis paper examines the collaborative work of residents, housing associations, and property owners, in a multi-apartment housing complex, to manage household waste. Framed within the feminist ecological perspective of digital environmental stewardship - that is, how diverse actors, motivations, and capacities producing care for the environment that can be digitally mediated - we unpack how the many actors involved work together to keep waste in place, maintain the local waste system, and call on `responsibility' as a means to produce sustainable actions and accountability. We frame these practices of waste management within the mundane work of sociotechnical innovation. Borrowing from Jackson's notion of repair work, we weave together an argument for the novel and valuable contribution to sustainability research of CSCW approaches grounded in the everyday contingent emergencies of environmental care. We argue for approaches to sustainability that reflect the work to maintain sustainability ––not just produce it-- and the `good enough', a locally and reflexively produced equilibrium between maintenance and repair, which can frame the design of sociotechnical interventions mediating practices of waste management.2025CRChiara Rossitto et al.Infrastructure StudiesCSCW
Surveying Phone Anxiety Patterns, Perceptions, and CopingMany people suffer from anxiety, commonly manifesting in avoidance behavior and stress. For example, they might not open letters because they suspect them to contain bad news. With the phone having taken on a central role in communication and service access, anxiety and avoidance also stem from and impact its use, such as when emails remain unread. However, while phone addition had been studied in depth, avoidance behavior related to phones has not. This necessitates a broader understanding in which ways anxiety impacts and results from phone use. To develop this understanding, we surveyed user stories on the anxiety they felt around interacting with their phones. From 197 stories, we identify eight different kinds of phone anxiety. We supplement this analysis in two ways: (1) a survey with 81 participants to analyze the external perceptions of phone anxiety, and (2) interviews of 12 individual users on their phone anxiety coping strategies.2025FBFlorin-Alexandru Bursuc et al.Notification & Interruption ManagementWorkplace Wellbeing & Work StressMobileHCI
The Impact of a Chatbot's Ephemerality-Framing on Self-Disclosure PerceptionsSelf-disclosure, the sharing of one's thoughts and feelings, is affected by the perceived relationship between individuals. While chatbots are increasingly used for self-disclosure, the impact of a chatbot's framing on users' self-disclosure remains under-explored. We investigated how a chatbot’s description of its relationship with users, particularly in terms of ephemerality, affects self-disclosure. Specifically, we compared a \textsc{Familiar} chatbot, presenting itself as a companion remembering past interactions, with a \textsc{Stranger} chatbot, presenting itself as a new, unacquainted entity in each conversation. In a mixed factorial design, participants engaged with either the \textsc{Familiar} or \textsc{Stranger} chatbot in two sessions across two days, with one conversation focusing on \textsc{Emotional}- and another \textsc{Factual}-disclosure. When \textsc{Emotional}-disclosure was sought in the first chatting session, \textsc{Stranger}-condition participants felt more comfortable self-disclosing. However, when \textsc{Factual}-disclosure was sought first, these differences were replaced by more enjoyment among \textsc{Familiar}-condition participants. Qualitative findings showed \textsc{Stranger} afforded anonymity and reduced judgement, whereas \textsc{Familiar} sometimes felt intrusive unless rapport was built via low-risk \textsc{Factual}-disclosure.2025SCSamuel Rhys Cox et al.Conversational ChatbotsAgent Personality & AnthropomorphismCUI
Beyond Productivity: Rethinking the Impact of Creativity Support ToolsCreativity Support Tools (CSTs) are widely used across diverse creative domains, with generative AI recently increasing the abilities of CSTs. To better understand how the success of CSTs is determined in the literature, we conducted a review of outcome measures used in CST evaluations. Drawing from (n=173) CST evaluations in the ACM Digital Library, we identified the metrics commonly employed to assess user interactions with CSTs. Our findings reveal prevailing trends in current evaluation practices, while exposing underexplored measures that could broaden the scope of future research. Based on these results, we argue for a more holistic approach to evaluating CSTs, encouraging the HCI community to consider not only user experience and the quality of the generated output, but also user-centric aspects such as self-reflection and well-being as critical dimensions of assessment. We also highlight a need for validated measures specifically suited to the evaluation of generative AI in CSTs.2025SCSamuel Rhys Cox et al.Generative AI (Text, Image, Music, Video)Creative Collaboration & Feedback SystemsC&C
General Practitioners’ Perspectives on a Pre-Consultation Chatbot for Shared Decision-MakingGeneral practitioner (GP) consultations are the typical starting point for a patient's healthcare journey. Here, GPs aim to support and inform patients to enable a shared decision-making process. In this work we explore how an interactive chatbot, designed to prepare patients for their GP consultation, is perceived by GPs to impact patient consultations, patient-GP interaction, and their work. We conducted an in-depth evaluation and interview with 15 GPs from 12 different practices. Our findings provide insights into common challenges in shared decision-making, GP perspectives on the role of chatbots in preparing patients, and how chatbot technology could impact and transform general practice. Finally, we reflect on patient and GP agency in shared decision-making and the impact of technology on this complex relationship.2025MSMana Samiee et al.Conversational ChatbotsHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationDIS
The Art of Mechamimicry: Designing Prototyping Tools for Human-Robot InteractionThis research investigates the application of tangible and embodied prototyping methods integrated with virtual simulation in Human-Robot Interaction (HRI). We present the development of the “kinematic puppet,” a reliable, reusable, adaptable, and accessible prototyping tool designed to facilitate stakeholder engagement in early-stage HRI research and development without requiring significant financial or time investments. The potential of this methodological approach is illustrated through a formative co-design workshop in Robotic Assisted Surgery (RAS), where the kinematic puppet, simple props and a low-fidelity anatomical model enabled stakeholders to externalise tacit knowledge through role-play scenarios. The case study suggests that combining physical and virtual approaches can support stakeholders in expressing concrete ideas for improving or changing the interaction, making abstract concepts tangible, with virtual simulation enabling rich data capture for further design development. This work contributes to the rapidly expanding toolbox of design approaches in HRI.2025JDJames L Dwyer et al.Shape-Changing Interfaces & Soft Robotic MaterialsHuman-Robot Collaboration (HRC)Prototyping & User TestingDIS
Prompt Machine: A Tangible Generative AI Tool for Supporting Children's Learning and LiteracyGenerative AI technologies are moving into school settings. However, there is confusion about how, when, and why these technologies should be used. Our aim has been to provide insights on how AI technology can be meaningfully integrated into schools, with a specific focus on secondary school education. Informed by ten teachers, we developed Prompt Machine, a tangible learning tool that serves three central purposes; 1) scaffold curriculum learning, 2) support development of AI literacy, and 3) act as a focal point among pupils and teachers for discussing possibilities and limitations of AI. Based on a study with 33 pupils and their teachers, we present findings on tangible and collaborative AI interactions, facilitation of AI, and integration of AI into curricula. Additionally, we reflect on challenges and opportunities for AI in education from the perspective of teachers and learners and discuss future steps for tangible AI.2025MLMartin Lindrup et al.Generative AI (Text, Image, Music, Video)K-12 Digital Education ToolsIntelligent Tutoring Systems & Learning AnalyticsDIS
Involvement of Autistic Adults in the Participatory Design of Technology: A Scoping ReviewResearch in HCI and autism has become more focused on involving autistic adults in technological design. In this paper, we present the results of a scoping review analysis of 11 projects across 18 papers that focused on including autistic adults in the design of technology that impacts their lives. This paper contributes a deeper understanding of how autistic adults were involved in participatory design processes. Our findings reveal mixed positions on how the lived autistic perspective was harnessed to direct the application of topics and technologies chosen. Most projects employed infrastructures to enhance participation (e.g., providing multiple modes to participate or employing a tailored methodology). We pose future opportunities for autistic involvement, for example, in topics and technologies where autistic research is employed (e.g., autism diagnosis and machine learning), reviewing the importance of formal diagnosis for inclusion, and harnessing the multiple expertise of autistic adults.2025LMLaura Maye et al.University College Cork, School of Computer Science and Information TechnologyCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Participatory DesignCHI
Coordination Mechanisms in AI Development: Practitioner Experiences on Integrating UX ActivitiesSoftware development relies on collaboration and alignment between a variety of roles, including software developers and user experience designers. The increasing focus on artificial intelligence in today's development projects has given rise to new challenges in this collaboration. We extend previous work on the process of designing human-AI systems by analysing collaborative practices between UX designers and AI developers through Mintzberg's theory on coordination mechanisms. We conducted 15 in-depth interviews with UX designers and AI developers currently working on AI projects. We contribute by identifying how coordination mechanisms impact the UX design process when developing AI systems, inter-team (a)symmetries in power relations, and a growing need for tools and cross-disciplinary knowledge to support these collaborative efforts. In particular, we outline the risks of coordinating AI development work through the standardisation of output and skills in separately organised UX and AI development teams.2025ABAnders Bruun et al.Computer Science, Aalborg UniversityHuman-LLM CollaborationKnowledge Worker Tools & WorkflowsImpact of Automation on WorkCHI
Chatbots for Data Collection in Surveys: A Comparison of Four Theory-Based Interview ProbesSurveys are a widespread method for collecting data at scale, but their rigid structure often limits the depth of qualitative insights obtained. While interviews naturally yield richer responses, they are challenging to conduct across diverse locations and large participant pools. To partially bridge this gap, we investigate the potential of using LLM-based chatbots to support qualitative data collection through interview probes embedded in surveys. We assess four theory-based interview probes: descriptive, idiographic, clarifying, and explanatory. Through a split-plot study design (N=64), we compare the probes' impact on response quality and user experience across three key stages of HCI research: exploration, requirements gathering, and evaluation. Our results show that probes facilitate the collection of high-quality survey data, with specific probes proving effective at different research stages. We contribute practical and methodological implications for using chatbots as research tools to enrich qualitative data collection.2025RJRune Møberg Jacobsen et al.Aalborg University, Department of Computer ScienceConversational ChatbotsHuman-LLM CollaborationCHI
Enhancing Self-Efficacy in Health Self-Examination through Conversational Agent's EncouragementHealth self-examination, such as checking for changes to skin moles, is key to identifying potential negative changes to one's body. A major barrier to initiating a self-examination is a perceived lack of confidence or knowledge. In this study, we use a 2 x 2 between-subjects design to evaluate the effect of an AI conversational agent (CA) on participant self-efficacy and trust. We manipulated both participants' perceived skill in self-examination (based on prior perceived Success vs. Failure) and the CA's verbal persuasions (Encouraging vs. Neutral), with participants asked to complete a series of skin self-assessment tasks. Our findings show that participants' self-efficacy increased when exposed to encouraging CA persuasion. Additionally, we observed that an encouraging CA significantly increased participants’ trust scores in perceived benevolence compared to a neutral-sounding CA. Our results inform the design of CAs to support users' independent self-examination.2025NKNaja Kathrine Kollerup et al.Department of Computer ScienceConversational ChatbotsMental Health Apps & Online Support CommunitiesCHI
Visual Augmentations for Ultrasound Assessment Training of Medical StudentsUltrasound assessments are key in assessing traumatic injuries to the human body during urgent medical emergencies. Obtaining proficiency in conducting ultrasound assessments is challenging, and relies on hands-on, individually instructed training provided by a scarce number of ultrasound experts. We investigate how to support medical students’ learning of ultrasound assessment through visual augmentations. By enhancing the learning process, we seek to support medical students in reaching higher proficiency in ultrasound assessments. We followed an ultrasound assessment course to identify the primary challenges faced by medical students learning to conduct ultrasound assessments. Based on our findings, we designed four distinct visual augmentations in collaboration with a course educator that guide students in achieving better ultrasound image quality.We evaluated these visual augmentations in a mixed-method study with 15 medical students. Our findings provide insights on the use of digital technology in supporting clinical training, and the possibilities of bridging existing training practices.2025HDHelena Bøjer Djernæs et al.Aalborg University, Department of Computer ScienceMedical & Scientific Data VisualizationSurgical Assistance & Medical TrainingCHI
How Do Hackathons Foster Creativity? Towards Automated Evaluation of Creativity at ScaleHackathons have become popular collaborative events for accelerating the development of creative ideas and prototypes. There are several case studies showcasing creative outcomes across domains such as industry, education, and research. However, there are no large-scale studies on creativity in hackathons which can advance theory on how hackathon formats lead to creative outcomes. We conducted a computational analysis of 193,353 hackathon projects. By operationalizing creativity through usefulness and novelty, we refined our dataset to 10,363 projects, allowing us to analyze how participant characteristics, collaboration patterns, and hackathon setups influence the development of creative projects. The contribution of our paper is twofold: We identified means for organizers to foster creativity in hackathons. We also explore the use of large language models (LLMs) to augment the evaluation of creative outcomes and discuss challenges and opportunities of doing this, which has implications for creativity research at large.2025JFJeanette Falk et al.Aalborg University, Computer ScienceGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationCrowdsourcing Task Design & Quality ControlCHI