Scaffolded Vulnerability: Chatbot-Mediated Reciprocal Self-Disclosure and Need-Supportive Interaction in CouplesWhile reciprocal self-disclosure drives intimacy, digital tools seldom scaffold autonomy, competence, and relatedness—the motivational underpinnings defined by Self-Determination Theory (SDT) that enable deep exchange. We introduce a chatbot employing dual-layer scaffolding to satisfy these needs: first providing enabling affordances (instrumental support) for vulnerability, then mediating affordances (relational support) for responsiveness. In a randomized study (N = 72; 36 couples) comparing Partner Support (PS: both layers), Direct Support (DS: enabling only), and Basic Prompt (BP: questions only), results reveal a critical distinction. While enabling affordances (PS, DS) were sufficient to deepen disclosure, only mediating affordances (PS) reliably elicited partner-provided need support and increased perceived closeness. Furthermore, controlled motivation decreased across conditions, and scaffolding buffered vitality, which remained stagnant in BP. We contribute empirical evidence that SDT-guided mediation fosters connection, offering a practical framework for designing AI-mediated conversations that support, rather than replace, human intimacy.2026ZJZhuoqun Jiang et al.Singapore University of Technology and DesignAffective Human-Computer DialogueDigital Emotional Expression & TransmissionCHI
Towards Aligning Multimodal LLMs with Human Experts: A Focus on Parent–Child InteractionWhile multimodal large language models (MLLMs) are increasingly applied in human-centred AI systems, their ability to understand complex social interactions remains uncertain. We present an exploratory study on aligning MLLMs with speech–language pathologists (SLPs) in analysing joint attention in parent–child interactions, a key construct in early social–communicative development. Drawing on interviews and video annotations with three SLPs, we characterise how observational cues of gaze, action, and vocalisation inform their reasoning processes. We then test whether an MLLM can approximate this workflow through a two-stage prompting, separating observation from judgment. Our findings reveal that alignment is more robust at the observation layer, where experts share common descriptors, than at the judgement layer, where interpretive criteria diverge. We position this work as a case-based probe into expert–AI alignment in complex social behaviour, highlighting both the feasibility and the challenges of applying MLLMs to socially situated interaction analysis.2026WSWeiyan Shi et al.Singapore University of Technology and DesignHuman-LLM CollaborationHuman Pose & Activity RecognitionChild-Computer Interaction DesignCHI
Large Language Model (LLM)-driven Adversarial Social Influences in Online Information Spread: Risks and InterventionsPeople's online information processing is strongly shaped by social influence, and large language models (LLMs) now enable social bots to manipulate such influence at scale. This paper examines the effects of LLM-driven adversarial social influence—a strategy in which automated agents employ LLMs to distort truth by making misinformation appear credible or by undermining factual news—on how people evaluate and share information. Across two pre-registered, randomized experiments, we first show that exposure to LLM-driven adversarial social influence significantly reduces people's ability to judge the veracity of news and lowers their discernment between sharing true versus false content. We then test two credibility prompts: AI-generated content detectors and warnings, as potential interventions. Results show that both prompts mitigate some harms such as by improving misinformation detection, though their effectiveness were dependent on the context. We conclude by discussing the risks of LLM-driven adversarial social bots and the implications for designing interventions to combat misinformation.2026ZLZhuoran Lu et al.Purdue UniversityHuman-LLM CollaborationMisinformation & Fact-CheckingExplainable AI (XAI)CHI
Words to Describe What I’m Feeling: Exploring the Potential of AI Agents for High Subjectivity Decisions in Advance Care PlanningLoss of decisional capacity, coupled with the increasing absence of reliable human proxies, raises urgent questions about how individuals' values can be represented in Advance Care Planning (ACP). To probe this fraught design space of high-risk, high-subjectivity decision support, we built an experience prototype (\acpagent{}) and asked 15 participants in 4 workshops to train it to be their personal ACP proxy. We analysed their coping strategies and feature requests and mapped the results onto axes of agent autonomy and human control. Our findings show a surprising 86.7\% agreement with \acpagent{}, arguing for a potential new role of AI in ACP where agents act as personal advocates for individuals, building mutual intelligibility over time. We propose that the key areas of future risk that must be addressed are the moderation of users' expectations and designing accountability and oversight over agent deployment and cutoffs.2026KSKellie Yu Hui Sim et al.Singapore University of Technology and DesignAI-Assisted Decision-Making & AutomationAI Ethics, Fairness & AccountabilityTelemedicine & Remote Patient MonitoringCHI
Remini: Leveraging Chatbot-Mediated Mutual Reminiscence for Promoting Positive Affect and Feeling of Connectedness among Loved OnesMutual reminiscence, defined as revisiting shared positive memories through reciprocal self-disclosure, strengthens emotional bonds, enhances well-being, and deepens intimacy. However, most technology-mediated reminiscence tools emphasize individual reflection or one-way storytelling, which overlooks the dynamic, interactive dialogue essential for meaningful mutual reminiscence. To address this limitation, we introduce Remini, a chatbot designed to support reciprocal self-disclosure between close partners such as couples, friends, or family members. Grounded in the Social Functions of Autobiographical Memory (SFAM) framework, Remini uses conversational AI to guide emotionally rich exchanges through five narrative phases: rapport building, memory narration, elaboration, reflection, and summary. In a mixed-method, both between- and within-subjects study (N = 48, 24 dyads), we compare Remini to a baseline chatbot that offers minimal memory-trigger prompts. Our findings show that structured guidance from Remini significantly improves positive affect, feeling of connection, and engagement. It also fosters more detailed narrative co-construction and greater reciprocal self-disclosure. Participant feedback highlights the practical value, perceived benefits, and design considerations of chatbot-mediated reminiscence. We contribute empirically grounded design implications for conversational agents that strengthen human connection through mutual reminiscence.2025ZJZhuoqun Jiang et al.Connecting FamiliesCSCW
Enhancing Deliberativeness: Evaluating the Impact of Multimodal Reflection NudgesNudging participants with text-based reflective nudges enhances deliberation quality on online deliberation platforms. The effectiveness of multimodal reflective nudges, however, remains largely unexplored. Given the multi-sensory nature of human perception, incorporating diverse modalities into self-reflection mechanisms has the potential to better support various reflective styles. This paper explores how presenting reflective nudges of different types (direct: persona and indirect: storytelling) in different modalities (text, image, video and audio) affects deliberation quality. We conducted two user studies with 20 and 200 participants respectively. The first study identifies the preferred modality for each type of reflective nudges, revealing that text is most preferred for persona and video is most preferred for storytelling. The second study assesses the impact of these modalities on deliberation quality. Our findings reveal distinct effects associated with each modality, providing valuable insights for developing more inclusive and effective online deliberation platforms.2025SYShunYi Yeo et al.Singapore University of Technology and DesignParticipatory DesignInteractive Narrative & Immersive StorytellingCHI
Understanding End-User Perception of Transfer Risks in Smart ContractsBlockchain smart contracts are increasingly used in critical use cases (e.g., financial transactions). Thus, it is pertinent to ensure that their end-users understand risks in attempting token transfers. Addressing this, we investigate end-user comprehension of five transfer risks (e.g. the end-user being blacklisted) in the most popular Ethereum contract, USD Tether (USDT), and their prevalence in other top ERC-20 contracts. First, we conducted a user study investigating end-user comprehension of transfer risks in USDT with 110 participants. Second, we performed source code analysis of the next top (78) ERC-20 smart contracts to identify the prevalence of these risks. Study results show that the majority of end-users do not comprehend some real risks, and confuse real and fictitious risks. This holds regardless of participants’ self-rated programming and Web3 proficiency. Source code analysis demonstrates that examined risks are prevalent in up to 19.2% of the top ERC-20 contracts.2025YPYustynn Panicker et al.Singapore University of Technology and DesignPrivacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
Help Me Reflect: Leveraging Self-Reflection Interface Nudges to Enhance Deliberativeness on Online Deliberation PlatformsThe deliberative potential of online platforms has been widely examined. However, little is known about how various interface-based reflection nudges impact the quality of deliberation. This paper presents two user studies with 12 and 120 participants, respectively, to investigate the impacts of different reflective nudges on the quality of deliberation. In the first study, we examined five distinct reflective nudges: persona, temporal prompts, analogies and metaphors, cultural prompts and storytelling. Persona, temporal prompts, and storytelling emerged as the preferred nudges for implementation on online deliberation platforms. In the second study, we assess the impacts of these preferred reflectors more thoroughly. Results revealed a significant positive impact of these reflectors on deliberative quality. Specifically, persona promotes a deliberative environment for balanced and opinionated viewpoints while temporal prompts promote more individualised viewpoints. Our findings suggest that the choice of reflectors can significantly influence the dynamics and shape the nature of online discussions.2024SYShunYi Yeo et al.Singapore University of Technology and DesignSocial Platform Design & User BehaviorParticipatory DesignCHI
CollabCoder: A Lower-barrier, Rigorous Workflow for Inductive Collaborative Qualitative Analysis with Large Language ModelsCollaborative Qualitative Analysis (CQA) can enhance qualitative analysis rigor and depth by incorporating varied viewpoints. Nevertheless, ensuring a rigorous CQA procedure itself can be both complex and costly. To lower this bar, we take a theoretical perspective to design a one-stop, end-to-end workflow, CollabCoder, that integrates Large Language Models (LLMs) into key inductive CQA stages. In the independent open coding phase, CollabCoder offers AI-generated code suggestions and records decision-making data. During the iterative discussion phase, it promotes mutual understanding by sharing this data within the coding team and using quantitative metrics to identify coding (dis)agreements, aiding in consensus-building. In the codebook development phase, CollabCoder provides primary code group suggestions, lightening the workload of developing a codebook from scratch. A 16-user evaluation confirmed the effectiveness of CollabCoder, demonstrating its advantages over the existing CQA platform. All related materials of CollabCoder, including code and further extensions, will be included in: https://gaojie058.github.io/CollabCoder/.2024JGJie Gao et al.Singapore University of Technology and DesignHuman-LLM CollaborationUser Research Methods (Interviews, Surveys, Observation)Prototyping & User TestingCHI
DataDive: Supporting Readers' Contextualization of Statistical Statements with Data ExplorationStatistical statements that refer to data to support narratives or claims are commonly used to inform readers about the magnitude of social issues. While contextualizing statistical statements with relevant data supports readers in building their own interpretation of statements, the complexity of finding contextual information on the web and linking statistical statements with it impedes readers' efforts to do so. We present DataDive, an interactive tool for contextualizing statistical statements for the readers of online texts. Based on users' selections of statistical statements, our tool uses an LLM-powered pipeline to generate candidates of relevant contexts and poses them as guiding questions to the user as potential contexts for exploration. When the user selects a question, DataDive employs visualizations to further help the user compare and explore contextually relevant data. A technical evaluation shows that DataDive generates important and diverse questions that facilitate exploration around statistical statements and retrieves relevant data for comparison. Moreover, a user study with 21 participants suggests that DataDive facilitates users to explore diverse contexts and to be more aware of how statistical data could relate to the text.2024HKHyunwoo Kim et al.Interactive Data VisualizationData StorytellingVisualization Perception & CognitionIUI
GlassMessaging: Towards Ubiquitous Messaging Using OHMDshttps://doi.org/10.1145/36109312023NJNuwan Janaka et al.Context-Aware ComputingUbiquitous ComputingUbiComp
VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft PrototypingIn argumentative writing, writers must brainstorm hierarchical writing goals, ensure the persuasiveness of their arguments, and revise and organize their plans through drafting. Recent advances in large language models (LLMs) have made interactive text generation through a chat interface (e.g., ChatGPT) possible. However, this approach often neglects implicit writing context and user intent, lacks support for user control and autonomy, and provides limited assistance for sensemaking and revising writing plans. To address these challenges, we introduce VISAR, an AI-enabled writing assistant system designed to help writers brainstorm and revise hierarchical goals within their writing context, organize argument structures through synchronized text editing and visual programming, and enhance persuasiveness with argumentation spark recommendations. VISAR allows users to explore, experiment with, and validate their writing plans using automatic draft prototyping. A controlled lab study confirmed the usability and effectiveness of VISAR in facilitating the argumentative writing planning process.2023ZZZheng Zhang et al.Human-LLM CollaborationAI-Assisted Creative WritingUIST
Does Mode of Digital Contact Tracing Affect User Willingness to Share Information? A Quantitative StudyDigital contact tracing can limit the spread of infectious diseases. Nevertheless, barriers remain to attain sufficient adoption. In this study, we investigate how willingness to participate in contact tracing is affected by two critical factors: the modes of data collection and the type of data collected. We conducted a scenario-based survey study among 220 respondents in the United States (U.S.) to understand their perceptions about contact tracing associated with automated and manual contact tracing methods. The findings indicate a promising use of smartphones and a combination of public health officials and medical health records as information sources. Through a quantitative analysis, we describe how different modalities and individual demographic factors may affect user compliance when participants are asked to provide four key information pieces for contact tracing.2022CZCamellia Zakaria et al.University of Massachusetts AmherstPrivacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
Nudge for Reflection: More than Just a Channel to Political KnowledgeReflection, a process that organizes information into a structure that incorporates both own and others’ perspectives, was previously believed to function mainly as an antecedent of political knowledge. In this paper, we first design a simple interface nudge to encourage users to reflect on their views on political issues. Second, we use an experimental study to show that reflection works in a way more than leading to political knowledge. Results from a between-subjects online experiment (N = 168) covering one crucial public issue in Singapore (i.e., fertility) showed that (a) reflection interacts with information access to influence perceived issue knowledge; (b) reflection enhances perceived attitude certainty, including perceived attitude clarity and perceived attitude correctness; (c) reflection promotes willingness to express opinions in private settings.2021WZWeiyu Zhang et al.National University of SingaporeActivism & Political ParticipationInclusive DesignCHI
"You Cannot Offer Such a Suggestion": Designing for Family Caregiver Input in Home Care SystemsPrevious work has looked closely at the challenges of using patient-generated data to enable remote assessment and monitoring by healthcare professionals. In this paper, we examine family caregivers who act as proxies for patients who may not have the capacity of capturing the necessary data. We worked with occupational therapists to develop an application for remote assessment of the safety of patients' homes by occupational therapists with the assistance of family caregivers. We evaluated the application with family caregivers and found two features unique to communication between family caregivers and healthcare professionals: Caregivers want to be able to direct healthcare professionals' attention to support problem-solving at home, and they include their perspective on how to best meet the patient's health needs. We discuss the importance of these findings for home systems in the domain of long-term chronic care.2020PFPin Sym Foong et al.National University of SingaporeElderly Care & Dementia SupportAging-in-Place Assistance SystemsCHI
Nudge for Deliberativeness: How Interface Features Influence Online DiscourseCognitive load is a significant challenge to users for being deliberative. Interface design has been used to mitigate this cognitive state. This paper surveys literature on the anchoring effect, partitioning effect and point-of-choice effect, based on which we propose three interface nudges, namely, the word-count anchor, partitioning text fields, and reply choice prompt. We then conducted a 2×2×2 factorial experiment with 80 participants (10 for each condition), testing how these nudges affect deliberativeness. The results showed a significant positive impact of the word-count anchor. There was also a significant positive impact of the partitioning text fields on the word count of response. The reply choice prompt showed a surprisingly negative affect on the quantity of response, hinting at the possibility that the reply choice prompt induces a fear of evaluation, which could in turn dampen the willingness to reply.2020SMSanju Menon et al.National University of SingaporePrivacy by Design & User ControlSocial Platform Design & User BehaviorUser Research Methods (Interviews, Surveys, Observation)CHI
Understanding Digitally-Mediated Empathy: An Exploration of Visual, Narrative, and Biosensory Informational CuesDigitally sharing our experiences engages a process of empathy shaped by available informational cues. Biosensory data is one informative cue, but the relationship to empathy is underexplored. In this study, we investigate this process by showing a video of a "target'' person's visual perspective watching a virtual reality film to sixty "observers''. We vary information available to observers via three experimental conditions: a baseline unmodified video, video with narrative text, or with a graph of electrodermal activity (EDA) of the target. Compared to baseline, narrative text increased empathic accuracy (EA) while EDA had an opposite, negative effect. Qualitatively, observers describe their empathic processes as using their own feelings supplemented with the information presented depending on the interpretability of that information. Both narration and EDA prompted observers to reconsider assumptions about another's experience. Our findings lead to a discussion of digitally-mediated empathy with implications for associated research and product development.2019MCMax T. Curran et al.University of California, BerkeleyVisualization Perception & CognitionBiosensors & Physiological MonitoringCHI
GestAKey: Touch Interaction on Individual KeycapsConventionally, keys on a physical keyboard have only two states: ``released’’ and ``pressed’’. As such, various techniques, such as hotkeys, are designed to enhance the keyboard expressiveness. Realizing that user inevitably perform touch actions during keystrokes, we propose GestAKey, leveraging location and motion of the touch on individual keycaps to augment the functionalities of existing keystrokes. With a log study, we collected touch data for both normal usage (typing and hotkeys) and while performing touch gestures (location and motion), which are analyzed to assess the viability of augmenting keystrokes with simultaneous gestures. A controlled experiment was conducted to compare GestAKey with existing keyboard interaction techniques, in terms of efficiency and learnability. The results show that GestAKey has comparable performance with hotkey. We further discuss the insights of integrating such touch modality into existing keyboard interaction, and demonstrate several usage scenarios.2018YSYilei Shi et al.Singapore University Of Technology and DesignHand Gesture RecognitionHuman-LLM CollaborationCHI
Supporting Rhythm Activities of Deaf Children using Music-Sensory-Substitution SystemsRhythm is the first musical concept deaf people learn in music classes. However, hearing loss limits the amount of information that allows a deaf person to evaluate his or her performance and stay in sync with other musicians. In this paper, we investigated how a visual and vibrotactile music-sensory-substitution device, MuSS-Bits++, affects rhythm discrimination, reproduction, and expressivity of deaf people. We conducted a controlled study with 11 deaf children and found that most participants felt more confident wearing the device in vibration mode even when it did not objectively improve their accuracy. Furthermore, we studied how MuSS-Bits++ can be used in music classes at deaf schools and what challenges and opportunities arise in such a setting. Based on these studies, we discuss insights and future directions that support the design and development of music-sensory-substitution systems for music making.2018BPBenjamin Petry et al.Singapore University of Technology and DesignVibrotactile Feedback & Skin StimulationDeaf & Hard-of-Hearing Support (Captions, Sign Language, Vibration)CHI