Teaching Spell Checkers to Teach: Pedagogical Program Synthesis for Interactive LearningSpelling taught through memorization often fails many learners, particularly children with language-based learning disorders who struggle with the phonological skills necessary to spell words accurately. Educators such as speech-language pathologists (SLPs) address this instructional gap by using an inquiry-based approach to teach spelling that targets the phonology, morphology, meaning, and etymology of words. Yet, these strategies rarely appear in everyday writing tools, which simply detect and autocorrect errors. We introduce SPIRE(Spelling Inquiry Engine), a spell check system that brings this inquiry-based pedagogy into the act of composition. SPIRE implements Pedagogical Program Synthesis, a novel approach for operationalizing the inherently dynamic pedagogy of spelling instruction. SPIRE represents SLP instructional moves in a domain-specific language, synthesizes tailored programs in real-time from learner errors, and renders them as interactive interfaces for inquiry-based interventions. With SPIRE, spelling errors become opportunities to explore word meanings, word structures, morphological families, word origins, and grapheme-phoneme correspondences, supporting metalinguistic reasoning alongside correction. Evaluation with SLPs and learners shows alignment with professional practice and potential for integration into writing workflows.2026MSMomin Naushad Siddiqui et al.Georgia Institute of TechnologyPredictive Input & AutocorrectAI-Assisted Writing & Text GenerationMotor Impairment Assistive Input TechnologiesIUI
SPICA: Scalable and Personalized Conversational Agent Framework for AAC UsersAugmentative and Alternative Communication (AAC) users face persistent challenges in expressing themselves authentically. The effort required to compose messages and sustain conversational flow often prevents users from fully participating in natural dialogue. Previous works have explored the integration of large language models to reduce effort and accelerate communication. However, these systems often fail to capture the user's personal voice. To address this, researchers have explored fine-tuning with user data, yet these methods remain difficult to scale and generalize poorly beyond biographical content. In this work, we introduce SPICA, a unified framework that addresses two key limitations: (1) the lack of scalable personalization that can adapt to user contexts in real time, and (2) the absence of agentic mechanisms to organize and orchestrate knowledge for conversation. SPICA acts as a lightweight plug-in that dynamically indexes and restructures user-relevant information into a personalized knowledge base. Beyond indexing, SPICA retrieves relevant knowledge on demand to guide conversation. It enables responses that are faithful to the user's identity while remaining flexible for broader communication goals. We validated SPICA extensively through automated evaluations using 200 synthetically generated AAC user profiles, as well as qualitative studies with AAC users in real-world settings. Results demonstrate that SPICA enables faster communication while preserving personalization, producing responses that are contextually grounded and aligned with each user's unique style.2026SPSayantan Pal et al.University at BuffaloAugmentative & Alternative Communication (AAC)Conversational ChatbotsHuman-LLM CollaborationIUI
Does Sequencing Matter? Evaluating AI and Human Simulations for High-Stakes Communication Training in Law EnforcementTraining professionals in high-stakes, trauma-informed communication is critical across domains such as law enforcement, healthcare, and counseling. While live role-play with trained actors remains the gold standard, it is resource-intensive and emotionally demanding. We developed an AI-powered sexual assault victim interview training system and conducted a mixed-methods study with 35 police recruits, each completing both an AI-based and a live, actor-based training session. By varying the sequence (AI-first vs. human-first), we examined differences in self-efficacy, perceptions of the AI system, and perceived learning experience. Although both modalities supported learning, the order in which they were experienced significantly shaped learners’ emotional engagement, sense of preparedness, and interpretation of each simulation’s role. Building on these insights, we introduce a conceptual design framework that identifies social–emotional, temporal, and embodied distance as key pedagogical dimensions, and we offer implications for sequencing hybrid simulations to scaffold preparation, performance, and reflection. Our findings position AI not as a replacement for human realism, but as a complementary modality that expands opportunities for safe, scalable practice in sensitive communication training.2026DWDuo Wang et al.University of Illinois Urbana ChampaignIntelligent Tutoring Systems & Learning AnalyticsTelemedicine & Remote Patient MonitoringRobots in Education & HealthcareCHI
A Meat-Summer Night's Dream: A Tangible Design Fiction Exploration of Eating Biohybrid Flying Robots\textit{What if future dining involved eating robots?} We explore this question through a playful and poetic experiential dinner theater: a tangible design fiction staged as a 2052 Paris restaurant where diners consume a biohybrid flying robot in place of the banned delicacy of ortolan bunting. Moving beyond textual or visual speculation, our “dinner-in-the-drama” combined performance, ritual, and multisensory immersion to provoke reflection on sustainability, ethics, and cultural identity. Six participants from creative industries engaged as diners and role-players, responding with curiosity, discomfort, and philosophical debate. They imagined biohybrids as both plausible and unsettling—raising questions of sentience, symbolism, and technology adoption that extend beyond conventional sustainability framings of synthetic meat. Our contributions to HCI are threefold: (i) a speculative artifact that stages robots as food, (ii) empirical insights into how people negotiate cultural and ethical boundaries in post-natural eating, and (iii) a methodological advance in embodied, multisensory design fiction.2026ZWZiming Wang et al.Chalmers University of TechnologyDesign FictionHuman-Nature Relationships (More-than-Human Design)Digital Art Installations & Interactive PerformanceCHI
Beyond Age-Based Restrictions: Rethinking Children's Online Safety Through Comparing Parent–Child Perspectives of Risks in User-Generated Content GamesExisting HCI literature on the benefits and risks of User-Generated Content (UGC) games for children often focuses on either parents' or child players' views. Bridging these perspectives is critical for identifying the alignment or divergence between children's and parents' concerns, which provides a more comprehensive image of challenges and opportunities children face in these games. Through a mixed-method content analysis of 2000 reviews about Roblox (one of the most popular UGC platforms) from both parents and children, we identify six key risks children face and investigate how parents’ and children’s focuses on different risks may shift across age groups. We also propose design recommendations for advancing trust and safety initiatives on UGC platforms by considering children, parents, and developers as key stakeholders. We contribute to rethinking more nuanced safety models for protecting children that are developmentally responsive and context-sensitive, rather than relying on age-based thresholds (e.g., under-13 vs. 13+).2026RPRuchi Panchanadikar et al.Clemson UniversityYouth Online Safety & PrivacyParent-Child Co-Use of MediaSocial Platform Design & User BehaviorCHI
Beyond the Silence: How Men Navigate Infertility Through Digital Communities and Data SharingMen experiencing infertility face unique challenges navigating Traditional Masculinity Ideologies that discourage emotional expression and help-seeking. This study examines how Reddit's r/maleinfertility community helps overcome these barriers through digital support networks. Using topic modeling (115 topics), network analysis (11 micro-communities), and time-lagged regression on 11,095 posts and 79,503 comments from 8,644 users, we found the community functions as a hybrid space: informal diagnostic hub, therapeutic commons, and governed institution. Medical advice dominates discourse (63.3%), while emotional support (7.4%) and moderation (29.2%) create essential infrastructure. Sustained engagement correlates with actionable guidance and affiliation language, not emotional processing. Network analysis revealed structurally cohesive but topically diverse clusters without echo chamber characteristics. Cross-posters (20% of users) who bridge r/maleinfertility and the gender-mixed r/infertility community serve as navigators and mentors, transferring knowledge between spaces. These findings inform trauma-informed design for stigmatized health communities, highlighting role-aware systems and navigation support.2026TATawfiq Ammari et al.RutgersMental Health Apps & Online Support CommunitiesTelemedicine & Remote Patient MonitoringElectrical Muscle Stimulation (EMS)CHI
Should the AI Speak First? Evaluating Proactive vs. Reactive Facilitation in Mixed-Reality Medical TrainingAs AI support tools become more common in immersive medical training, designers face a critical interaction-design question: When should an AI facilitator take initiative, and when should it wait for the learner? To investigate this design tension, we compared two versions of an AI facilitator in a mixed-reality (XR) lumbar puncture simulator training conditions: one in which the AI proactively initiated guidance and encouragement, and another in which the AI responded only when prompted. We examined how medical students (n=22) engaged with, interpreted, and reacted to these two facilitation styles. We found no significant differences in learning outcomes, interaction frequency, or overall experience ratings. However, interviews and behavioral analyses revealed nuanced differences in how learners perceived AI interventions across distinct task phases. AI-initiated support was seen as helpful in some moments and disruptive in others, depending on task phase, cognitive load, and personal preferences. Based on these findings, we contribute a boundary framework which offers actionable design guidance for calibrating AI proactivity in immersive training systems, and extends HCI research on proactive agents and human–AI collaboration within high-cognitive-load environments.2026DWDuo Wang et al.University of Illinois Urbana ChampaignVR Medical Training & RehabilitationAI-Assisted Decision-Making & AutomationImmersion & Presence ResearchCHI
Foody Talk: Exploring Opportunities for Conversational Food JournalingDigital food journaling can help support reflection and improvement of wellbeing relating to eating habits. However, it is often viewed as burdensome, and abandoned before gaining benefits. Advances in conversational user interfaces (CUIs) have the potential to support people journaling in a natural and interactive manner, but we lack understanding of how people would ideally prefer to use CUIs when journaling. We conducted 33 co-design sessions with 18 participants to ideate CUI interactions supportive of their health goals and in everyday situations. Our findings reveal that participants expect CUIs to be adaptive by learning goals and personal references, and support depth in detail and goal alignment while respecting situational constraints and intent. While participants expressed concern around navigating long-term data solely through conversations, they envisioned that CUIs could provide empathetic, non-judgmental feedback. We discuss opportunities for CUIs to support empathetic food journaling and accountability while following guardrails for delegated tasks.2025LSLucas M. Silva et al.University of Iowa, Computer ScienceConversational ChatbotsDiet Tracking & Nutrition ManagementCHI
Peerspective: A Study on Reciprocal Tracking for Self-awareness and Relational InsightPersonal informatics helps individuals understand themselves, but it often struggles to capture non-conscious behaviors such as stress responses, habitual actions, and communication styles. Incorporating social aspects into PI systems offers new perspectives on self-understanding, yet prior research has largely focused on unidirectional approaches that center benefits on the primary tracker. To address this gap, we introduce the Peerspective study, which explores reciprocal tracking---a bidirectional practice where two participants observe and provide feedback to each other, fostering mutual self-understanding and collaboration. In a week-long study with eight peer dyads, we explored how reciprocal observation and feedback influence self-awareness and interpersonal relationships. Our findings reveal that reciprocal tracking not only helps participants uncover blind spots and expand their self-concepts but also enhances empathy, deepens communication, and promotes sustained engagement. We discuss key facilitators and challenges of integrating reciprocity into personal informatics systems and offer design considerations for supporting collaborative tracking in everyday contexts.2025KLKwangyoung Lee et al.KAIST, Department of Industrial DesignCollaborative Learning & Peer TeachingMental Health Apps & Online Support CommunitiesContext-Aware ComputingCHI
Meditating Together: Practices, Benefits and Challenges of Meditation on Social Virtual RealityMeditation and mind-body practices offer many benefits for both mental and physical well-being. Recently, social virtual reality (VR) has emerged as a promising platform to support well-being activities. While Human-Computer Interaction (HCI) research has explored technologies for meditation, little is known about how users appropriate social VR for meditation, particularly group practice, and how it shapes their experiences. To bridge this gap, we interviewed 13 regular social VR meditators to explore their practices, perceived benefits, and challenges. We found that meditators utilized platform features to engage in community-driven group practices, manage session flow, employ avatars and body tracking for kinetic practices, and experiment with novel forms of meditation. Participants reported benefits and challenges related to the individual and social aspects of their meditation experiences. Based on these findings, we discuss the implications of using social VR for meditation, including how avatars and virtual others positively affect the practice, as well as emerging tensions and opportunities.2025LLLika Haizhou Liu et al.University of California Irvine, InformaticsSocial & Collaborative VRImmersion & Presence ResearchMental Health Apps & Online Support CommunitiesCHI
Towards Hormone Health: An Autoethnography of Long-Term Holistic Tracking to Manage PCOSPolycystic ovary syndrome (PCOS) is a common hormonal disorder affecting 11-13% of women of reproductive age, characterized by a wide range of symptoms (e.g., menstrual irregularity, acne, and obesity) that varies among individuals. While self-tracking tools help PCOS patients to monitor their symptoms and find personalized treatment, they often focus on regular periods of healthy women with inadequate support for the 1) personalization and 2) long-term holistic tracking necessary for managing complex chronic conditions like PCOS. To bridge this gap, the first author (who has PCOS) conducted an autoethnographic study of holistic self-tracking over a period of ten months in an effort to manage her condition. Our results highlight the challenges of personalized, holistic, long-term tracking in medical, socio-cultural, temporal, technical, and spatial contexts. Based on these insights, we provide design implications for tracking tools that are more inclusive and sustainable.2025DKDaye Kang et al.Cornell, Information ScienceChronic Disease Self-Management (Diabetes, Hypertension, etc.)Diet Tracking & Nutrition ManagementCHI
HAIGEN: Towards Human-AI Collaboration for Facilitating Creativity and Style Generation in Fashion DesignJiang等人提出人机协作框架HAIGEN，利用AI辅助时尚设计师创意生成与风格探索，提升设计效率与创新多样性。2024JJJianan Jiang et al.Generative AI (Text, Image, Music, Video)AI-Assisted Creative WritingUbiComp
LoCal: An Automatic Location Attribute Calibration Approach for Large-Scale Deployment of mmWave-based Sensing SystemsZhang 等人提出 LoCal 自动位置校准方法，解决大规模毫米波感知系统部署中的位置属性标定难题，降低系统部署成本与复杂度2024DZDuo Zhang et al.Context-Aware ComputingUbiquitous ComputingUbiComp
LT-Fall: The Design and Implementation of a Life-threatening Fall Detection and Alarming SystemFalls are the leading cause of fatal injuries to elders in modern society, which has motivated researchers to propose various fall detection technologies. We observe that most of the existing fall detection solutions are diverging from the purpose of fall detection: timely alarming the family members, medical staff or first responders to save the life of the human with severe injury caused by fall. Instead, they focus on detecting the behavior of human falls, which does not necessarily mean a human is in real danger. The real critical situation is when a human cannot get up without assistance and is thus lying on the ground after the fall because of losing consciousness or becoming incapacitated due to severe injury. In this paper, we define a life-threatening fall as a behavior that involves a falling down followed by a long-lie of humans on the ground, and for the first time point out that a fall detection system should focus on detecting life-threatening falls instead of detecting any random falls. Accordingly, we design and implement LT-Fall, a mmWave-based life-threatening fall detection and alarming system. LT-Fall detects and reports both fall and fall-like behaviors in the first stage and then identifies life-threatening falls by continuously monitoring the human status after fall in the second stage. We propose a joint spatio-temporal localization technique to detect and locate the micro-motions of the human, which solves the challenge of mmWave's insufficient spatial resolution when the human is static, i.e., lying on the ground. Extensive evaluation on 15 volunteers demonstrates that compared to the state-of-the-art work (92% precision and 94% recall), LT-Fall achieves zero false alarms as well as a precision of 100% and a recall of 98.8%. https://dl.acm.org/doi/10.1145/35808352023DZDuo Zhang et al.Elderly Care & Dementia SupportBiosensors & Physiological MonitoringUbiComp
SignRing: Continuous American Sign Language Recognition Using IMU Rings and Virtual IMU Data"Sign language is a natural language widely used by Deaf and hard of hearing (DHH) individuals. Advanced wearables are developed to recognize sign language automatically. However, they are limited by the lack of labeled data, which leads to a small vocabulary and unsatisfactory performance even though laborious efforts are put into data collection. Here we propose SignRing, an IMU-based system that breaks through the traditional data augmentation method, makes use of online videos to generate the virtual IMU (v-IMU) data, and pushes the boundary of wearable-based systems by reaching the vocabulary size of 934 with sentences up to 16 glosses. The v-IMU data is generated by reconstructing 3D hand movements from two-view videos and calculating 3-axis acceleration data, by which we are able to achieve a word error rate (WER) of 6.3% with a mix of half v-IMU and half IMU training data (2339 samples for each), and a WER of 14.7% with 100% v-IMU training data (6048 samples), compared with the baseline performance of the 8.3% WER (trained with 2339 samples of IMU data). We have conducted comparisons between v-IMU and IMU data to demonstrate the reliability and generalizability of the v-IMU data. This interdisciplinary work covers various areas such as wearable sensor development, computer vision techniques, deep learning, and linguistics, which can provide valuable insights to researchers with similar research objectives." https://doi.org/10.1145/36108812023JLJiyang Li et al.Hand Gesture RecognitionFoot & Wrist InteractionDeaf & Hard-of-Hearing Support (Captions, Sign Language, Vibration)UbiComp
VibPath: Two-Factor Authentication with Your Hand’s Vibration Response to Unlock Your Phone"Technical advances in the smart device market have fixated smartphones at the heart of our lives, warranting an ever more secure means of authentication. Although most smartphones have adopted biometrics-based authentication, after a couple of failed attempts, most users are given the option to quickly bypass the system with passcodes. To add a layer of security, two-factor authentication (2FA) has been implemented but has proven to be vulnerable to various attacks. In this paper, we introduce VibPath, a simultaneous 2FA scheme that can understand the user's hand neuromuscular system through touch behavior. VibPath captures the individual's vibration path responses between the hand and the wrist with the attention-based encoder-decoder network, authenticating the genuine users from the imposters unobtrusively. In a user study with 30 participants, VibPath achieved an average performance of 0.98 accuracy, 0.99 precision, 0.98 recall, 0.98 f1-score for user verification, and 94.3% accuracy for user identification across five passcodes. Furthermore, we also conducted several extensive studies, including in-the-wile, permanence, vulnerability, usability, and system overhead studies, to assess the practicability and viability of the VibPath from multiple aspects." https://doi.org/10.1145/36108942023SCSeokmin Choi et al.Vibrotactile Feedback & Skin StimulationPasswords & AuthenticationUbiComp
SmartASL: “Point-of-Care” Comprehensive ASL Interpreter Using WearablesSign language builds up an important bridge between the d/Deaf and hard-of-hearing (DHH) and hearing people. Regrettably, most hearing people face challenges in comprehending sign language, necessitating sign language translation. However, state-of-the-art wearable-based techniques mainly concentrate on recognizing manual markers (e.g., hand gestures), while frequently overlooking non-manual markers, such as negative head shaking, question markers, and mouthing. This oversight results in the loss of substantial grammatical and semantic information in sign language. To address this limitation, we introduce SmartASL, a novel proof-of-concept system that can 1) recognize both manual and non-manual markers simultaneously using a combination of earbuds and a wrist-worn IMU, and 2) translate the recognized American Sign Language (ASL) glosses into spoken language. Our experiments demonstrate the SmartASL system's significant potential to accurately recognize the manual and non-manual markers in ASL, effectively bridging the communication gaps between ASL signers and hearing people using commercially available devices. https://dl.acm.org/doi/10.1145/35962552023YJYINCHENG JIN et al.Foot & Wrist InteractionDeaf & Hard-of-Hearing Support (Captions, Sign Language, Vibration)Augmentative & Alternative Communication (AAC)UbiComp
NeuralGait: Assessing Brain Health Using Your Smartphone"Brain health attracts more recent attention as the population ages. Smartphone-based gait sensing and analysis can help identify the risks of brain diseases in daily life for prevention. Existing gait analysis approaches mainly hand-craft temporal gait features or developing CNN-based feature extractors, but they are either prone to lose some inconspicuous pathological information or are only dedicated to a single brain disease screening. We discover that the relationship between gait segments can be used as a principle and generic indicator to quantify multiple pathological patterns. In this paper, we propose NeuralGait, a pervasive smartphone-cloud system that passively captures and analyzes principle gait segments relationship for brain health assessment. On the smartphone end, inertial gait data are collected while putting the smartphone in the pants pocket. We then craft local temporal-frequent gait domain features and develop a self-attention-based gait segment relationship encoder. Afterward, the domain features and relation features are fed to a scalable RiskNet in the cloud for brain health assessment. We also design a pathological hot update protocol to efficiently add new brain diseases in the RiskNet. NeuralGait is practical as it provides brain health assessment with no burden in daily life. In the experiment, we recruit 988 healthy people and 417 patients with a single or combination of PD, TBI, and stroke, and evaluate the brain health assessment using a set of specifically designed metrics including global accuracy, exact accuracy, sensitivity, and false alarm rate. We also demonstrate the generalization (e.g., analysis of feature effectiveness and model efficiency) and inclusiveness of NeuralGait. https://dl.acm.org/doi/10.1145/3569476"2023HLHuining Li et al.Fitness Tracking & Physical Activity MonitoringBiosensors & Physiological MonitoringUbiComp
WavoID: Robust and Secure Multi-modal User Identification via mmWave-voice MechanismWith the increasing deployment of voice-controlled devices in homes and enterprises, there is an urgent demand for voice identification to prevent unauthorized access to sensitive information and property loss. However, due to the broadcast nature of sound wave, a voice only system is vulnerable to adverse conditions and malicious attacks. We observe that the cooperation of millimeter waves (mmWave) and voice signals can significantly improve the effectiveness and security of user identification. Based on the properties, we propose a multi-modal user identification system (named WavoID) by fusing the uniqueness of mmWave sensed vocal vibration and mic-recorded voice of users. To estimate fine-grained waveforms, WavoID splits signals and adaptively combines useful decomposed signals according to correlative contents in both mmWave and voice. An elaborated anti-spoofing module in WavoID comprising biometric bimodal information defend against attacks. WavoID produces and fuses the response maps of mmWave and voice to improve the representation power of fused features, benefiting accurate identification, even facing adverse circumstances. We evaluate WavoID using commercial sensors on extensive experiments. WavoID has significant performance on user identification with over 98% accuracy on 100 user datasets.2023TLTiantian Liu et al.Eye Tracking & Gaze InteractionBrain-Computer Interface (BCI) & NeurofeedbackPasswords & AuthenticationUIST
“I Don't Even Remember What I Read”: How Design Influences Dissociation on Social MediaMany people have experienced mindlessly scrolling on social media. We investigated these experiences through the lens of normative dissociation: total cognitive absorption, characterized by diminished self-awareness and reduced sense of agency. To explore user experiences of normative dissociation and how design affects the likelihood of normative dissociation, we deployed Chirp, a custom Twitter client, to 43 U.S. participants. Experience sampling and interviews revealed that sometimes, becoming absorbed in normative dissociation on social media felt like a beneficial break. However, people also reported passively slipping into normative dissociation, such that they failed to absorb any content and were left feeling like they had wasted their time. We found that designed interventions--including custom lists, reading history labels, time limit dialogs, and usage statistics--reduced normative dissociation. Our findings demonstrate that interaction designs intended to capture attention likely do so by harnessing people’s natural inclination to seek normative dissociation experiences. This suggests that normative dissociation may be a more productive framing than addiction for discussing social media overuse.2022ABAmanda Baughan et al.University of WashingtonPrivacy by Design & User ControlOnline Harassment & Counter-ToolsSocial Platform Design & User BehaviorCHI