Rules or Weights? Comparing User Understanding of Explainable AI Techniques with the Cognitive XAI-Adaptive ModelRules and Weights are popular XAI techniques for explaining AI decisions. Yet, it remains unclear how to choose between them, lacking a cognitive framework to compare their interpretability. In an elicitation user study on forward and counterfactual decision tasks, we identified 7 reasoning strategies of interpreting three XAI Schemas—weights, rules, and their hybrid. To analyze their capabilities, we propose CoXAM, a Cognitive XAI-Adaptive Model with shared memory representation to encode instance attributes, linear weights, and decision rules. CoXAM employs computational rationality to choose among reasoning processes based on the trade-off in utility and reasoning time, separately for forward or counterfactual decision tasks. In a validation study, CoXAM demonstrated a stronger alignment with human decision-making compared to baseline machine learning proxy models. The model successfully replicated and explained several key empirical findings, including that counterfactual tasks are inherently harder than forward tasks, decision tree rules are harder to recall and apply than linear weights, and the helpfulness of XAI depends on the application data context, alongside identifying which underlying reasoning strategies were most effective. With CoXAM, we contribute a cognitive basis to accelerate debugging and benchmarking disparate XAI techniques.2026LRLouth Bin Rawshan et al.National University of SingaporeExplainable AI (XAI)AI-Assisted Decision-Making & AutomationComputational Methods in HCIIUI
Transferable XAI: Relating Understanding Across Domains with Explanation TransferCurrent Explainable AI (XAI) focuses on explaining a single application, but when encountering related applications, users may rely on their prior understanding from previous explanations. This leads to either overgeneralization and AI overreliance, or burdensome independent memorization. Indeed, related decision tasks can share explanatory factors, but with some notable differences; e.g., body mass index (BMI) affects the risks for heart disease and diabetes at the same rate, but chest pain is more indicative of heart disease. Similarly, models using different attributes for the same task still share signals; e.g., temperature and pressure affect air pollution but in opposite directions due to the ideal gas law. Leveraging transfer of learning, we propose Transferable XAI to enable users to transfer understanding across related domains by explaining the relationship between domain explanations using a general affine transformation framework applied to linear factor explanations. The framework supports explanation transfer across various domain types: translation for data subspace (subsuming prior work on Incremental XAI), scaling for decision task, and mapping for attributes. Focusing on task and attributes domain types, in formative and summative user studies, we investigated how well participants could understand AI decisions from one domain to another. Compared to single-domain and domain-independent explanations, Transferable XAI was the most helpful for understanding the second domain, leading to the best decision faithfulness, factor recall, and ability to relate explanations between domains. This framework contributes to improving the reusability of explanations across related AI applications by explaining factor relationships between subspaces, tasks, and attributes.2026FWFei Wang et al.National University of SingaporeExplainable AI (XAI)AI-Assisted Decision-Making & AutomationAlgorithmic Transparency & AuditabilityIUI
Fit Matters: Format–Distance Alignment Improves Conversational SearchExisting conversational search systems can synthesize information into responses, but they lack principled ways to adapt response formats to users' cognitive states. This paper investigates whether aligning format and distance, which involves matching information granularity and media to users' psychological distance, improves user experience. In a between-subjects experiment (N=464) on travel planning, we crossed two distance dimensions (temporal/spatial × near/far) with four formats varying in granularity (abstract/concrete) and media (text/image-and-text). The experiment established that format-distance alignment reduced users' risk perceptions while increasing decision confidence, perceptions of information usefulness, ease of use, enjoyment, and credibility, and adoption intentions. Concrete formats imposed higher cognitive load, but yielded productive effort when matched to near-distance tasks. Images enhanced concrete but not abstract text, suggesting multimedia benefits depend on complementarity. These findings establish format-distance alignment as a distinctive and important design dimension, enabling systems to tailor response formats to users' psychological distance.2026YYYitian Yang et al.National University of SingaporeConversational ChatbotsConversational Search & QA SystemsAI-Assisted Decision-Making & AutomationCHI
VisceroHaptics: Investigating the Effects of Gut-based Audio-Haptic Feedback on Gastric Feelings and Gastric Interoceptive BehaviorGastric interoception influences eating behavior and emotions, making its modulation valuable for healthcare and human-computer-interaction applications. However, whether gastric interoception can be modulated noninvasively in humans remains unclear. While previous research indicates that abdominal-sound-driven haptic feedback resembles gut sensations, its impact on gastric feelings and gastric interoceptive behavior is unknown. We conducted three experiments totalling 55 participants to investigate how gut-sound-driven audio-haptic feedback applied to the stomach (1) affects user's feelings (2) influences perception of hunger and satiety levels and (3) influences gastric interoceptive behavior, quantified with Water Load Test-II. Results revealed that audio-haptic feedback patterns (a) induced the feelings of hunger, fullness, thirst, stomach upset, (b) increased hunger level, and (c) significantly increased volumes of ingested water. This work provides the first evidence that audio-haptic stimulation can alter gastric interoceptive behavior, motivating the use of noninvasive methods to influence users' feelings and behaviors in future applications.2026MNMia Huong Nguyen et al.National University of SingaporeVibrotactile Feedback & Skin StimulationAffective Feedback & Emotion Regulation InterfacesEmotion-Sensing WearablesCHI
Enhancing Children's Self-Reporting in Chatbot Diaries through Rhyming StyleExisting children’s self-reporting tools like surveys and diaries often feel restrictive, leading to disengagement and low-quality responses. LLM-powered chatbots can adapt with simplified wording or empathetic tone, but such adaptations remain insufficient: responses may be adult-centered, complex, or formulaic, undermining engagement and response quality. We explore rhyme as a child-centered conversational style. In a co-design workshop with 35 children, participants envisioned dialogue that was short, playful, and soothing. Building on these insights, we designed a voice-based sleep diary in rhyming style and conducted a within-subjects study (rhyming vs. prose) with 42 children. Rhyming prompts improved response quality across question types, while maintaining high engagement even among children who preferred prose. We contribute empirical evidence and design insights showing how rhyme can exemplify broader child-centered strategies beyond capability adaptation. Although limited to short-term lab sessions, this work provides a first step toward conversational style as a design lever for children’s self-reporting.2026SCShanshan Chen et al.Eindhoven University of TechnologyChild-Computer Interaction DesignAffective Human-Computer DialogueMental Health Apps & Online Support CommunitiesCHI
EUREXA: End-User Reconfiguration of Environment with eXplainable Augmentation for Generative FabricationAugmentation allows rapid reconfiguration of passive physical interfaces to improve accessibility, support independent living through domestic automation, and more. However, its potential is largely unrealized for novice users due to several key barriers. First, users rarely identify latent interaction problems within their built environments. Second, they often lack the knowledge to clearly express design intent. Third, many innovative solutions remain in research prototypes, limiting access. We introduce EUREXA, an agentic AI system to share the spirit of discovery (“Eureka!”). EUREXA supports end-users through a \textit{diagnose–discover–describe} workflow: from input with varying ambiguity and complexity, it surfaces latent interaction challenges, presents reconfiguration opportunities through augmentations, and produces interpretable designs. Its novelty is a dual search across public augmentation repositories and research articles, enabling reusable designs even when no design libraries or parametric tools exist. EUREXA transforms non-parametric models into parametric ones or directly generates fully explainable designs. To evaluate EUREXA across varied user inputs, complexities, and clarity levels, we define ambiguity metrics, conduct a user study, and report critical factors for advancing generative AI to help end-users readily augment physical interfaces through fabrication.2026AAAbul Al Arabi et al.Texas A&M UniversityShape-Changing Interfaces & Soft Robotic MaterialsCustomizable & Personalized ObjectsGenerative AI (Text, Image, Music, Video)CHI
AI Personalization Paradox: Reading Highlights for Personalized AI-Assisted Writing Increases Engagement but Undermines Autonomy and OwnershipAI-assisted writing raises concerns about autonomy and ownership when benefiting writers. Personalization has been proposed as an effective solution while also risking writers' reliance on AI and behavior shifting. For better personalization design, existing studies rely on interaction and information solely within the writing phase; however, few studies have examined how reading behaviors can inform personalized writing. This study investigates the effects of integrating reading highlights for personalization on AI-assisted writing. A between-subjects study with 46 participants revealed that the personalization condition encouraged participants to produce more highlights. However, highlighting unexpectedly shifted from a sense-making strategy to an instrumental act of "feeding the AI," leading to significant reliance on AI and declines in writers' sense of autonomy, ownership, and self-credit. These findings indicate personalization risks in AI-assisted writing, emphasize the importance of personalization strategies, and provide design implications.2026PQPeinuan Qin et al.National University of SingaporeHuman-LLM CollaborationAI-Assisted Writing & Text GenerationBehavior Change & Reflection TechnologyCHI
Beyond PII: How Users Attempt to Estimate and Mitigate Implicit LLM InferenceLarge Language Models (LLMs) such as ChatGPT can infer personal attributes from seemingly innocuous text, raising privacy risks beyond memorized data leakage. While prior work has demonstrated these risks, little is known about how users estimate and respond. We conducted a survey with 240 U.S. participants who judged text snippets for inference risks, reported concern levels, and attempted rewrites to block inference. We compared their rewrites with those generated by ChatGPT and Rescriber, a state-of-the-art sanitization tool. Results show that participants struggled to anticipate inference, performing a little better than chance. User rewrites were effective in just 28\% of cases - better than Rescriber but worse than ChatGPT. We examined our participants' rewriting strategies, and observed that while paraphrasing was the most common strategy it is also the least effective; instead abstraction and adding ambiguity were more successful. Our work highlights the importance of inference-aware design in LLM interactions.2026SWSynthia Qia Wang et al.University of ChicagoExplainable AI (XAI)Privacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
Editable XAI: Toward Bidirectional Human-AI Alignment with Co-Editable Explanations of Interpretable AttributesWhile Explainable AI (XAI) helps users understand AI decisions, misalignment in domain knowledge can lead to disagreement. This inconsistency hinders understanding, and because explanations are often read-only, users lack the control to improve alignment. We propose making XAI editable, allowing users to write rules to improve control and gain deeper understanding through the generation effect of active learning. We developed CoExplain, leveraging a neural network for universal representation and symbolic rules for intuitive reasoning on interpretable attributes. CoExplain explains the neural network with a faithful proxy decision tree, parses user-written rules as an equivalent neural network graph, and collaboratively optimizes the decision tree. In a user study (N=43), CoExplain and manually editable XAI improved user understanding and model alignment compared to read-only XAI. CoExplain was easier to use with fewer edits and less time. This work contributes Editable XAI for bidirectional AI alignment, improving understanding and control.2026HCHaoyang Chen et al.National University of SingaporeExplainable AI (XAI)AI-Assisted Decision-Making & AutomationCHI
iRULER: Intelligible Rubric-Based User-Defined LLM Evaluation for RevisionLarge Language Models (LLMs) have become indispensable for evaluating writing. However, text feedback they provide is often unintelligible, generic, and not specific to user criteria. Inspired by structured rubrics in education and intelligible AI explanations, we propose iRULER following identified design guidelines to \textit{scaffold} the review process by \textit{specific} criteria, providing \textit{justification} for score selection, and offering \textit{actionable} revisions to target different quality levels. To \textit{qualify} user-defined criteria, we recursively used iRULER with a rubric-of-rubrics to iteratively \textit{refine} rubrics. In controlled experiments on writing revision and rubric creation, iRULER most improved validated LLM-judged review scores and was perceived as most helpful and aligned compared to read-only rubric and text-based LLM feedback. Qualitative findings further support how iRULER satisfies the design guidelines for user-defined feedback. This work contributes interactive rubric tools for intelligible LLM-based review and revision of writing, and user-defined rubric creation.2026JBJingwen Bai et al.National University of SingaporeHuman-LLM CollaborationAI-Assisted Writing & Text GenerationParticipatory DesignCHI
Rememo: A Research-through-Design Inquiry Towards an AI-in-the-loop Therapist’s Tool for Dementia ReminiscenceReminiscence therapy (RT) is a common non-pharmacological intervention in dementia care. Recent technology-mediated interventions have largely focused on people with dementia through solutions that replace human facilitators with conversational agents. However, the relational work of facilitation is critical in the effectiveness of RT. Hence, we developed Rememo, a therapist-oriented tool that integrates Generative AI to support and enrich human facilitation in RT. Our tool aims to support the infrastructural and cultural challenges that therapists in Singapore face. In this research, we contribute the Rememo system as a therapist’s tool for personalized RT developed through sociotechnically-aware research-through-design. Through studying this system in-situ, our research extends our understanding of human-AI collaboration for care work. We discuss the implications of designing AI-enabled systems that respect the relational dynamics in care contexts, and argue for a rethinking of synthetic imagery as a therapeutic support for memory rather than a record of truth.2026CSCeleste Seah et al.National University of SingaporeVR Medical Training & RehabilitationAI-Assisted Decision-Making & AutomationMental Health Apps & Online Support CommunitiesCHI
Beyond Descriptions: A Generative Scene2Audio Framework for Blind and Low-Vision Users to Experience Vista LandscapesCurrent scene perception tools for Blind and Low Vision (BLV) individuals rely on spoken descriptions but lack engaging representations of visually pleasing distant environmental landscapes (Vista spaces). Our proposed Scene2Audio framework generates comprehensible and enjoyable nonverbal audio using generative models informed by psychoacoustics, and principles of scene audio composition. Through a user study with 11 BLV participants, we found that combining the Scene2Audio sounds with speech creates a better experience than speech alone, as the sound effects complement the speech making the scene easier to imagine. A mobile app “in-the-wild” study with 7 BLV users for more than a week further showed the potential of Scene2Audio in enhancing outdoor scene experiences. Our work bridges the gap between visual and auditory scene perception by moving beyond purely descriptive aids, addressing the aesthetic needs of BLV users.2026CGChitralekha Gupta et al.National University of SingaporeAudio Accessibility (Captions, Sign Language, Vibration)Emotion-Sensing WearablesVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)CHI
Comparables XAI: Faithful Example-based AI Explanations with Counterfactual Trace AdjustmentsExplaining with examples is an intuitive way to justify AI decisions. However, it is challenging to understand how a decision value should change relative to the examples with many features differing by large amounts. We draw from real estate valuation that uses Comparables—examples with known values for comparison. Estimates are made more accurate by hypothetically adjusting the attributes of each Comparable and correspondingly changing the value based on factors. We propose Comparables XAI for relatable example-based explanations of AI with Trace adjustments that trace counterfactual changes from each Comparable to the Subject, one attribute at a time, monotonically along the AI feature space. In modelling and user studies, Trace-adjusted Comparables achieved the highest XAI faithfulness and precision, user accuracy, and narrowest uncertainty bounds compared to linear regression, linearly adjusted Comparables, or unadjusted Comparables. This work contributes a new analytical basis for using example-based explanations to improve user understanding of AI decisions.2026YZYifan Zhang et al.National University of SingaporeExplainable AI (XAI)AI-Assisted Decision-Making & AutomationAlgorithmic Transparency & AuditabilityCHI
ChatLearn: Leveraging Non-Native Speaker Communication Challenges as Language Learning OpportunitiesNon-native speakers (NNSs) face significant language barriers in multilingual communication with native speakers (NSs). While AI-mediated communication (AIMC) tools offer efficient one-time assistance, they often overlook opportunities for NNSs' continuous language acquisition. We introduce ChatLearn, an enhanced AIMC system that leverages NNSs' communication difficulties as learning opportunities. Beyond comprehension and expression assistance, ChatLearn simultaneously captures NNSs' language challenges, and subsequently provides them with spaced review as the conversation progresses. We conducted a mixed-methods study using a communication task with 43 NNS-NS pairs, after which ChatLearn NNSs recalled significantly more expressions than the baseline group, while there was no substantial decline in communication experience. Our findings highlight the value of contextual learning in NNS-NS communication, providing a new direction for AIMC systems that foster both immediate collaboration and continuous language development.2026PQPeinuan Qin et al.National University of SingaporeMultilingual & Cross-Cultural Voice InteractionHuman-LLM CollaborationIntelligent Tutoring Systems & Learning AnalyticsCHI
Understanding Older Adults’ Experiences of Support, Concerns, and Risks from Kinship-Role AI-Generated InfluencersAI-generated influencers are rapidly gaining popularity on Chinese short-video platforms, often adopting kinship-based roles such as "AI grandchildren'' to attract older adults. Although this trend has raised public concern, little is known about the design strategies behind these influencers, how older adults experience them, and the benefits and risks involved. In this study, we combined social media analysis with interviews to unpack the above questions. Our findings show that influencers use both visual and conversational cues to enact kinship roles, prompting audiences to engage in kinship-based role-play. Interviews further show that these cues arouse emotional resonance, help fulfill older adults’ informational and emotional needs, while also raising concerns about emotional displacement and unequal emotional investment. We highlight the complex relationship between virtual avatars and real family ties, shaped by broader sociocultural norms, and discuss how AI might strengthen social support for older adults while mitigating risks within cultural contexts.2026TSTianqi Song et al.National University of SingaporeAgent Personality & AnthropomorphismSocial Robot InteractionElderly Care & Dementia SupportCHI
Signals of Aggression: Modelling Multimodal Cues and Perceptual Effects in Virtual AgentsAggression is a socially complex behaviour that intelligent virtual agents (IVAs) must convincingly convey in applications such as customer service and conflict training. Despite its importance, aggression remains understudied: prior work has focused on basic emotions and unimodal cues, providing little insight into how aggression can be modelled multimodally or systematically scaled by intensity. We present a psychologically grounded model that parametrises language, voice, body movement and facial expressions, across four aggression levels. We evaluated the model in two studies with 38 flight attendants. Experiment 1 tested unimodal cues, showing all modalities except language conveyed aggression gradients. Experiment 2 extended this by combining modalities, demonstrating that coordinated multimodal integration stabilised weaker language cues and produced perceptually robust aggression levels (low, mid, and high) with body and facial cues carrying most weight. Our work contributes the first validated multimodal, multi-level aggression model for IVAs, offering design principles for broader socially expressive agents.2026SOShaun Jing Heng Ong et al.National University of SingaporeAffective Human-Computer DialogueSocial Robot InteractionEmpathy & Emotional DesignCHI
VisGuardian: A Lightweight Group-based Visual Privacy Control Technique For Smart Glasses in Home EnvironmentsAlways-on sensing of AI applications on AR glasses makes traditional permission techniques inefficient for context-dependent private visual data within home environments. Home presents a challenging privacy context due to massive sensitive objects and the intimate nature of daily routines. We propose VisGuardian, a fine-grained content-based visual permission technique for AR glasses. VisGuardian features a group-based control mechanism that enables users to efficiently manage permissions for multiple private objects. VisGuardian detects objects using YOLO and adopts a pre-classified schema to group them. By selecting a single object, users can obscure groups of related objects based on criteria including privacy sensitivity, object category, or spatial proximity. A technical evaluation shows VisGuardian achieves mAP50 of 0.6704 with only 14.0 ms latency and a 1.7% increase in battery consumption per hour. Furthermore, a user study (N=24) comparing VisGuardian to slider-based and object-based baselines found it to be significantly faster for setting permissions and was preferred by users for its efficiency, effectiveness, and ease of use.2026SZShuning Zhang et al.Tsinghua UniversitySmart Home Privacy & SecurityPrivacy by Design & User ControlAR Navigation & Context AwarenessCHI
Constructing Everyday Well-Being: Insights from God-Saeng (God生) for Personal InformaticsWhile Personal Informatics (PI) systems support behavior change, everyday well-being involves more than achieving individual target behaviors. It is shaped by cultural narratives that give actions meaning. In South Korea, the God-Saeng (God生) phenomenon—encompassing disciplined, collective, and publicly documented self-improvement practices—offers a lens into how well-being is negotiated in daily life. We conducted a 10-day probe (N=24) with bite-sized missions to examine how young adults engaged in God-Saeng. Participants relied on planning practices, accountability infrastructures, and datafication to stabilize themselves, yet these same routines also intensified pressures toward self-monitoring and performance. They navigated tensions between consistency and flexibility, authenticity and visibility, and productivity and broader values such as relationships, and reinterpreted ordinary activities through sociocultural contexts. These insights suggest design opportunities for PI systems that move beyond tracking, toward digital instruments that help users negotiate tensions, make meaning, and reflexively understand how technologies participate in their culturally and existentially situated well-being.2026ISInhwa Song et al.Princeton UniversityBehavior Change & Reflection TechnologyData-Driven Personal Decision-MakingInclusive DesignCHI
From Awareness to Intent: Mitigating Silent Driving System Failures through Prospective Situation Awareness Enhancing InterfacesSilent automation failures, where a system fails to detect a hazard without warning, pose a critical safety challenge for partially automated vehicles. While research has mostly focused on takeover requests, how to support a driver in silent failure remains underexplored. We conducted a multi-modal driving simulator study with 48 participants to investigate how different Prospective Situation Awareness Enhancement (PSAE) interfaces, delivered via augmented reality head-up display, affect takeover performance. By integrating behavioral, subjective psychological, and physiological data, our analysis suggests that situational awareness (SA) serves as an important moderating factor through which PSAE interfaces improve takeover performance. Further, we found that providing perceptual cues was most effective in enhancing SA, while communicating system intent was superior for building trust. Finally, we identified a potential correlate of SA in the neuroactivity. Overall, this paper contributes to understanding how transparency-oriented interfaces may support drivers and provides design insights into HMI design for silent failures.2026JWJiyao Wang et al.The Hong Kong University of Science and Technology (Guangzhou)Automated Driving Interface & Takeover DesignHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)In-Vehicle Haptic, Audio & Multimodal FeedbackCHI
I Can’t Keep Up: Accessibility Barriers in Video-Based Learning for Individuals with Borderline Intellectual FunctioningVideo-based learning (VBL) has become a dominant method for learning practical skills, yet accessibility guidelines provide limited guidance for users with cognitive differences. In particular, challenges that individuals with Borderline Intellectual Functioning (BIF) encounter in video-based learning remain largely underexplored, despite VBL's potential to support their learning through features like self-paced viewing and visual demonstration. To address this gap, we conducted series of studies with BIF individuals and caretakers to comprehensively understand their VBL challenges. Our analysis revealed challenges stemming from misalignment between user cognitive characteristics and video elements (e.g., overwhelmed by pacing and density, difficulty inferring omitted content), and experiential factors intensifying challenges (e.g., low self-efficacy). While participants employed coping strategies such as repetitive viewing to address these challenges, these strategies could not overcome fundamental gaps with video. We further discuss the design implications on both content and UI-level features for BIF and broader groups with cognitive diversities.2026HCHyehyun Chu et al.KAISTCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Special Education TechnologyOnline Learning & MOOC PlatformsCHI