Balancing Efficiency and Empathy: Healthcare Providers' Perspectives on AI-Supported Workflows for Serious Illness Conversations in the Emergency DepartmentSerious Illness Conversations (SICs)—discussions about values and care preferences for patients with life-threatening illness—rarely occur in Emergency Departments (EDs), despite evidence that early conversations improve care alignment and reduce unnecessary interventions. We interviewed 11 ED providers to identify challenges in SICs and opportunities for technology support, with a focus on AI. Our analysis revealed a four-stage SIC workflow (identification, preparation, conduction, documentation) and barriers at each stage, including fragmented patient information, limited time and space, lack of conversational guidance, and burdensome documentation. Providers expressed interest in AI systems for synthesizing information, supporting real-time conversations, and automating documentation, but emphasized concerns about preserving human connection and clinical autonomy. This tension highlights the need for technologies that enhance efficiency without undermining the interpersonal nature of SICs. We propose design guidelines for ambient and peripheral AI systems to support providers while preserving the essential humanity of these conversations.2026MZMenglin Zhao et al.Northeastern UniversityAI-Assisted Decision-Making & AutomationMental Health Apps & Online Support CommunitiesTelemedicine & Remote Patient MonitoringCHI
Dark Patterns Meet GUI Agents: LLM Agent Susceptibility to Manipulative Interfaces and the Role of Human OversightThe dark patterns, deceptive interface designs manipulating user behaviors, have been extensively studied for their effects on human decision-making and autonomy. Yet, with the rising prominence of LLM-powered GUI agents that automate tasks from high-level intents, understanding how dark patterns affect agents is increasingly important. We present a two-phase empirical study examining how agents, human participants, and human-AI teams respond to 16 types of dark patterns across diverse scenarios. Phase 1 highlights that agents often fail to recognize dark patterns, and even when aware, prioritize task completion over protective action. Phase 2 revealed divergent failure modes: humans succumb due to cognitive shortcuts and habitual compliance, while agents falter from procedural blind spots. Human oversight improved avoidance but introduced costs such as attentional tunneling and cognitive load. Our findings show neither humans nor agents are uniformly resilient, and collaboration introduces new vulnerabilities, suggesting design needs for transparency, adjustable autonomy, and oversight.2026JTJingyu Tang et al.University of Notre DameDark Patterns RecognitionHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationCHI
Through the Lens of Human-Human Collaboration: An Configurable Research Platform for Exploring Human-Agent CollaborationIntelligent systems have traditionally been designed as tools rather than collaborators, often lacking critical characteristics that collaboration partnerships require. Recent advances in large language model (LLM) agents open new opportunities for human-LLM-agent collaboration by enabling natural communication and various social and cognitive behaviors. Yet it remains unclear whether principles of computer-mediated collaboration established in HCI and CSCW persist, change, or fail when humans collaborate with LLM agents. To support systematic investigations of these questions, we introduce an open and configurable research platform for HCI researchers. The platform's modular design allows seamless adaptation of classic CSCW experiments and manipulation of theory-grounded interaction controls. We demonstrate the platform's research efficacy and usability through three case studies: (1) two Shape Factory experiments for resource negotiation with 16 participants, (2) one Hidden Profile experiment for information pooling with 16 participants, and (3) a participatory cognitive walkthrough with five HCI researchers to refine workflows of researcher interface for experiment setup and analysis.2026BYBingsheng Yao et al.Northeastern UniversityHuman-LLM CollaborationParticipatory DesignPrototyping & User TestingCHI
LLM-based Embodied Conversational Agent for Reducing Foreign Language Speaking Anxiety in Social VRForeign language speaking anxiety (FLSA) poses a major challenge for English-language learners, suppressing confidence and triggering a cycle of avoidance that hinders language acquisition. To address this, we explored the use of LLM-based embodied conversational agents (ECA) in social virtual reality (VR), which provide personalized support and multimodal interaction in a contextualized environment. We developed three English-language learning scenarios in social VR and conducted a five-day mixed-methods study where participants (N=20) engaged in daily 30-minute role-play practice with an LLM-based ECA to evaluate the efficacy of the system. Quantitative results showed a significant reduction in self-reported FLAS after 3 days, along with subtle gains in speaking proficiency measures. Qualitatively, learners perceived increased confidence, attributing it to the LLM-based ECA's non-judgmental stance, linguistic scaffolding, affective encouragement, and adaptive feedback. Our findings suggest the potential of LLM-based ECAs in social VR for language learning and offer considerations for future agent design.2026MPMengxu Pan et al.Northeastern UniversityHuman-LLM CollaborationSocial & Collaborative VRImmersion & Presence ResearchCHI
Exploring Collaboration Breakdowns Between Provider Teams and Patients in Post-Surgery CarePost-surgery care involves ongoing collaboration between provider teams and patients, which starts from post-surgery hospitalization through home recovery after discharge. While prior HCI research has primarily examined patients’ challenges at home, less is known about how provider teams coordinate discharge preparation and care handoffs, and how breakdowns in communication and care pathways may affect patient recovery. To investigate this gap, we conducted semi-structured interviews with 13 healthcare providers and 4 patients in the context of gastrointestinal (GI) surgery. We found coordination boundaries between in- and out-patient teams, coupled with complex organizational structures within teams, impeded the “invisible work” of preparing patients’ home care plans and triaging patient information. For patients, these breakdowns resulted in inadequate preparation for home transition and fragmented self-collected data, both of which undermine timely clinical decision-making. Based on these findings, we outline design opportunities to formalize task ownership and handoffs, contextualize co-temporal signals, and align care plans with home resources.2026BYBingsheng Yao et al.Northeastern UniversityTelemedicine & Remote Patient MonitoringElderly Care & Dementia SupportCHI
Secret Use of Large Language Model (LLM)The advancements of Large Language Models (LLMs) have decentralized the responsibility for the transparency of AI usage. Specifically, LLM users are now encouraged or required to disclose the use of LLM-generated content for varied types of real-world tasks. However, an emerging phenomenon, users' secret use of LLM, raises challenges in ensuring end users adhere to the transparency requirement. Our study used mixed-methods with an exploratory survey (125 real-world secret use cases reported) and a controlled experiment among 300 users to investigate the contexts and causes behind the secret use of LLMs. We found that such secretive behavior is often triggered by certain tasks, transcending demographic and personality differences among users. Task types were found to affect users’ intentions to use secretive behavior, primarily through influencing perceived external judgment regarding LLM usage. Our results yield important insights for future work on designing interventions to encourage more transparent disclosure of the use of LLMs or other AI technologies.2025ZZZhiping Zhang et al.Toward More Ethical and Transparent Systems and EnvironmentsCSCW
"Mango Mango, How to Let The Lettuce Dry Without A Spinner?'': Exploring User Perceptions of Using An LLM-Based Conversational Assistant Toward Cooking PartnerThe rapid advancement of Large Language Models (LLMs) has created numerous potentials for integration with conversational assistants (CAs) assisting people in their daily tasks, particularly due to their extensive flexibility. However, users' real-world experiences interacting with these assistants remain unexplored. In this research, we chose cooking, a complex daily task, as a scenario to explore people's successful and unsatisfactory experiences while receiving assistance from an LLM-based CA, Mango Mango. We discovered that participants value the system's ability to offer customized instructions based on context, provide extensive information beyond the recipe, and assist them in dynamic task planning. However, users expect the system to be more adaptive to oral conversation and provide more suggestive responses to keep them actively involved. Recognizing that users began treating our LLM-CA as a personal assistant or even a partner rather than just a recipe-reading tool, we propose five design considerations for future development.2025RCReina Szeyi Chan et al.Getting Things Done With AICSCW
Characterizing LLM-Empowered Personalized Story Reading and Interaction for Children: Insights From Multi-Stakeholders' PerspectivePersonalized interaction is highly valued by parents in their story-reading activities with children. While AI-empowered story-reading tools have been increasingly used, their abilities to support personalized interaction with children are still limited. Recent advances in large language models (LLMs) show promise in facilitating personalized interactions, but little is known about how to effectively and appropriately use LLMs to enhance children's personalized story-reading experiences. This work explores this question through a design-based study. Drawing on a formative study, we designed and developed StoryMate, an LLM-empowered personalized interactive story-reading tool for children, following an empirical study with children, parents, and education experts. Our participants valued the personalized features in StoryMate, and also highlighted the need to support personalized content, guiding mechanisms, reading context variations, and interactive interfaces. Based on these findings, we propose a series of design recommendations for better using LLMs to empower children's personalized story reading and interaction.2025JCJiaju Chen et al.East China Normal UniversityHuman-LLM CollaborationEarly Childhood Education TechnologyInteractive Narrative & Immersive StorytellingCHI
Promoting Prosociality via Micro-acts of Joy: A Large-Scale Well-Being Intervention StudyProsociality has been well-documented to positively impact mental, social, and physical well-being. However, existing studies of interventions for promoting prosociality have limitations such as small sample sizes or unclear benchmarks. To address this gap, we conducted a global-scale well-being intervention deployment study, BIGJOY, with more than 18,000 participants from 172 countries and regions. The week-long BIGJOY intervention consists of seven daily micro-acts (i.e., brief actions that require minimal effort), each adapted from validated positive psychology interventions. The analyses of large-scale intervention data reveal unique insights into the impact of well-being micro-acts across diverse populations, patterns of responses, effectiveness of specific micro-acts and their nuanced impacts across different populations, linkages between improvements in prosociality and in well-being, as well as the potential for machine learning to predict changes in prosociality. This study offers valuable insights into a set of design guidelines for future well-being and prosociality interventions. We envision our work as a stepping stone towards future large-scale prosociality interventions that foster a more unified and compassionate world.2025HGHitesh Goel et al.International Institute of Information Technology HyderabadMental Health Apps & Online Support CommunitiesEmpowerment of Marginalized GroupsCHI
CardioAI: A Multimodal AI-based System to Support Symptom Monitoring and Risk Prediction of Cancer Treatment-Induced CardiotoxicityDespite recent advances in cancer treatments that prolong patients' lives, treatment-induced cardiotoxicity (i.e., the various heart damages caused by cancer treatments) emerges as one major side effect. The clinical decision-making process of cardiotoxicity is challenging, as early symptoms may happen in non-clinical settings and are too subtle to be noticed until life-threatening events occur at a later stage; clinicians already have a high workload focusing on the cancer treatment, no additional effort to spare on the cardiotoxicity side effect. Our project starts with a participatory design study with 11 clinicians to understand their decision-making practices and their feedback on an initial design of an AI-based decision-support system. Based on their feedback, we then propose a multimodal AI system, CardioAI, that can integrate wearables data and voice assistant data to model a patient's cardiotoxicity risk to support clinicians' decision-making. We conclude our paper with a small-scale heuristic evaluation with four experts and the discussion of future design considerations.2025SWSiyi Wu et al.University of Toronto, Department of Computer ScienceEV Charging & Eco-Driving InterfacesAI-Assisted Decision-Making & AutomationBiosensors & Physiological MonitoringCHI
Live-Streaming-Based Dual-Teacher Classes for Equitable Education: Insights and Challenges From Local Teachers' Perspective in Disadvantaged AreasEducational inequalities in disadvantaged areas have long been a global concern. While Information and Communication Technologies (ICTs) have shown great potential in addressing this issue, the unique challenges in disadvantaged areas often hinder the practical effectiveness of such technologies. This paper examines live-streaming-based dual-teacher classes (LSDC) through a qualitative study in disadvantaged regions of China. Our findings indicate that, although LSDC offers students in these regions access to high-quality educational resources, its practical implementation is fraught with challenges. Specifically, we foreground the pivotal role of local teachers in mitigating these challenges. Through a series of situated efforts, local teachers contextualize high-quality lectures to the local classroom environment, ensuring the expected educational outcomes. Based on our findings, we argue that greater recognition and support for the situational practices of local teachers is essential for fostering a more equitable, sustainable, and scalable technology-driven educational model in disadvantaged areas.2025YSYuling Sun et al.Fudan UniversityK-12 Digital Education ToolsCollaborative Learning & Peer TeachingCHI
Examining Student and Teacher Perspectives on Undisclosed Use of Generative AI in Academic WorkWith the widespread adoption of Generative Artificial Intelligence (GenAI) tools, ethical issues are being raised around the disclosure of their use in publishing, journalism, or artwork. Recent research has found that college students are increasingly using GenAI tools; however, we know less about when, why, and how they choose to hide or disclose their use of GenAI in academic work. To address this gap, we conducted an online survey (n=97) and interviews with fifteen college students followed by interviews with nine teachers who had experience with students' undisclosed use of GenAI. Our findings elucidate the strategies students employ to hide their GenAI use and their justifications for doing so, alongside the strategies teachers follow to manage such non-disclosure. We unpack students' non-disclosure of GenAI through the lens of cognitive dissonance and discuss practical considerations for teachers and students regarding ways to promote transparency in GenAI use in higher education.2025RARudaiba Adnin et al.Northeastern University, Khoury College of Computer SciencesAI Ethics, Fairness & AccountabilityResearch Ethics & Open ScienceCHI
Talk2Care: An LLM-based Voice Assistant for Communication between Healthcare Providers and Older AdultsYang等人开发Talk2Care，基于大型语言模型为医疗工作者与老年人提供语音沟通助手，改善老年护理交流。2024ZYZiqi Yang et al.Intelligent Voice Assistants (Alexa, Siri, etc.)Mental Health Apps & Online Support CommunitiesUbiComp