People with visual impairments (PVI) increasingly adopt conversational AI (CAI) in their daily practices. While much existing HCI research has focused on the technical capabilities of CAI, less has examined the societal contexts in which PVI use CAI, particularly from non-Western perspectives. We conducted a study with 14 participants with visual impairments in South Korea using an audio-based probe featuring imagined dialogues between a blind user and a future CAI. Our findings situate CAI use alongside persistent social barriers such as prejudice and restricted employment opportunities that contribute to a lack of social visibility for PVI. These societal conditions shape not only how CAI is used, but also how the potential benefits and limitations of CAI are experienced. We discuss the need for CAI design within the socio-technical realities of PVI, and conclude by discussing the importance of emphasizing social awareness and empowerment in the development of future CAI systems.

Romantic relationships involving ADHD are often challenged when neurocognitive differences are misinterpreted as a lack of care. Most existing tools have focused on correcting the ADHD partner’s behavior, which forces the other into a supervisory role. However, this approach can inadvertently reinforce blame and guilt rather than fostering partnership. In this paper, we investigate how technology can scaffold coordination and mutual understanding in couples including ADHD through interviews with 21 participants and a 7-day diary study with six couples. We found that externalizing expectations and making micro-efforts visible helped partners reframe friction as a coordination challenge rather than a personal failure. While this scaffold lowers the barrier to sensitive communication, it risks becoming a source of daily pressure that couples might eventually try to avoid. We conclude with design implications for neurodiversity-affirming tools that prioritize shared ownership, negotiated norms, and mutual recognition over one-sided correction.

Investment decisions are often driven by time pressure and emotion, leaving investors vulnerable to cognitive biases. Mobile trading apps intensify these tendencies, yet existing interventions rely on external constraints that fail to foster lasting behavioral change. We investigate how reflection-centered approaches support mindful decision-making across a spectrum of investor expertise. We present MindStock, a technology probe providing principle-anchored feedback by integrating user-defined principles with behavioral data mirroring trading patterns. In a 6-week field study with 16 investors, we found that meaningful reflection comes from the tension between principles and behavioral data. Principles give context to otherwise opaque metrics, while data keeps principles from drifting into vague self-assurances. This pattern varied by experience: novices gravitated toward normative rule-setting, while experienced investors used the system to test and refine their own assumptions. We contribute design implications for supporting reflection in high-stakes decision-making contexts.

In China, special education teachers (SETs) for autistic children continuously coordinate complex demands, from intervention planning to behavior interpretation and parent communication. While SETs bear heavy responsibility for these decisions, they often lack the professional feedback loops needed to validate their judgments. Recently, Large Language Model (LLM)–based conversational AI (CAI) has emerged as tools that provide on-demand conversational scaffolding to support teachers’ educational practices. This study examines SETs’ opportunities and challenges in using CAI through a two-week diary study and follow-up interviews with 12 SETs. We found that SETs used CAI as scaffolding tools for interpreting children’s autistic traits, preparing parent communication, and seeking emotional support. Their strategies shifted from task-specific queries to open-ended, experience-driven dialogues that supported reflective sensemaking. However, the need for contextualized guidance often clashed with privacy concerns, making SETs hesitant to share the specific child data required for advice. We conclude with design implications for supporting reflective, responsive, and adaptive trust calibration in the use of CAI.

Understanding emotion for self-awareness requires recognizing not only its type and intensity but also the surrounding context and the insights it provides. While prior work has studied emotion recording, little attention has given to how such records might foster reflection on context. To address this gap, we developed Moodialogue, a system that enables users to personify emotion and engage in dialogue with it. In a six-week field study with nine participants, we found that personified emotion records supported dialogues that uncovered overlooked contexts and coexisting feelings beyond a single entry. Participants also reported savoring positive emotions more deeply and reframing negative ones from new perspectives. These findings point to design opportunities for systems that move beyond recording, enabling post-recording interactions that deepen reflection on emotional context and meaning.

Short-form videos have become one of the most addictive forms of digital media, keeping users endlessly scrolling with little self-regulation. Most existing interventions, screen-time limits or warning messages, are imposed from the outside and often lead to resistance or are simply ignored. To explore a more empowering approach, we designed a self-regulatory intervention that allows users to set their own viewing goals, reflect on what they watch, and receive periodic feedback as well as a summary of their viewing behaviors. We conducted a 4-week field study with 20 participants, spanning baseline, intervention, and withdrawal phases. Participants became more conscious of their viewing and more self-disciplined, finding control over both the viewing quantity and quality. However, sustaining self-regulation proved challenging once the intervention was withdrawn. These findings highlight both the promise and fragility of self-regulatory strategies and point to new design opportunities for interventions that support sustainable self-regulation in short-form videos.

Warning: This article contains stereotypical and offensive content.

Home-based care (HBC) delivers medical and care services in patients’ living environments, offering unique opportunities for patient-centered care. However, patient agency is often inadequately represented in shared HBC planning processes. Through 23 multi-stakeholder interviews with HBC patients, healthcare professionals, and care workers, alongside 60 hours of ethnographic observations, we examined how patient agency manifests in HBC and why this representation gap occurs. Our findings reveal that patient agency is not a static individual attribute but a relational capacity shaped through maintaining everyday continuity, mutual recognition from care providers, and engagement with material home environments. Furthermore, we identified that structured documentation systems filter out contextual knowledge, informal communication channels fragment patient voices, and doctor-centered hierarchies position patients as passive recipients. Drawing on these insights, we propose design considerations to bridge this representation gap and to integrate patient agency into shared HBC plans.

Advances in ubiquitous and wearable sensing and HCI research have made stress monitoring increasingly accessible, enabling the development of personalized stress management technologies. Yet, stress is a subjective and contextual experience, making effective intervention design challenging. Prior studies often isolate stress detection or intervention, without providing an integrated view of how these components connect and are evaluated in real-world use. To address this gap, we conducted a systematic review of 2,152 papers and selected 52 empirical studies where stress tracking informed interventions. Using a framework based on three stress constructs (subjective stress, psycho-physiological stress, and exposure stress), we analyzed how definitions of stress shape detection indicators, intervention design and timing, and evaluation methods. We show that stress conceptualization strongly influences system design, and we propose a conceptual framework linking detection, intervention, and evaluation to guide future user-centered stress management technologies.

Team-based collaboration is a cornerstone of modern creative work. Recent advances in generative AI open possibilities for humans to collaborate with multiple AI agents in distinct roles to address complex creative workflows. Yet, how to form Human-Multi-Agent Teams (HMATs) is underexplored, especially given that inter-agent interactions increase complexity and the risk of unexpected behaviors. In this exploratory study, we aim to understand how to form HMATs for creative work using CrafTeam, a technology probe that allows users to form and collaborate with their teams. We conducted a study with 12 design practitioners, in which participants iterated through a three-step cycle: forming HMATs, ideating with their teams, and reflecting on their teams' ideation. Our findings reveal that while participants initially attempted autonomous team operations, they ultimately adopted team formations in which they directly orchestrated agents. We discuss design considerations for HMAT formation that humans can effectively orchestrate multiple agents.

Large language models (LLMs) are promising tools for scaffolding students’ English writing skills, but their effectiveness in real-time K-12 classrooms remains underexplored. Addressing this gap, our study examines the benefits and limitations of using LLMs as real-time learning support, considering how classroom constraints, such as diverse proficiency levels and limited time, affect their effectiveness. We conducted a deployment study with 157 eighth-grade students in a South Korean middle school English class over six weeks. Our findings reveal that while scaffolding improved students’ ability to compose grammatically correct sentences, this step-by-step approach demotivated lower-proficiency students and increased their system reliance. We also observed challenges to classroom dynamics, where extroverted students often dominated the teacher’s attention, and the system’s assistance made it difficult for teachers to identify struggling students. Based on these findings, we discuss design guidelines for integrating LLMs into real-time writing classes as inclusive educational tools.

AI clones are evolving to include digital representations of real world individuals as chatbots. While often used to replicate famous figures, as the technology becomes more accessible, it is crucial to understand whether everyday users would create their own clones and how they interact with them. In this study, within the scope of AI-generated personas and their role in representing users’ needs and identities, we focus on personas that directly reflect the qualities of real humans. We define this as AI self clones—conversational AI representations that reflect their human creators—and examine how creators construct and engage with them. We conducted a 7-day study in which participants (N=12) created and interacted with their text based AI self clones using CloneBuilder , a web-based authoring interface for configuring and tuning AI self clones. The system enables individuals to create AI representations that encapsulate their unique personality, values, and interaction style. Our findings reveal that each participant developed a clone tailored to their personal circumstances. As the participants iteratively refined and tested their clone, their direction and expectation of AI clones evolved from performing specific roles to evolving entities that facilitated self exploration and relationship formation. Unexpected responses from the clone prompted self reflection and identity questioning. Overall, this paper explores the motivations for creating these clones, the strategies participants use to build and refine them, and the moments of emotional connection and break out experiences that emerge during the crafting process, along with key design implications, challenges, and ethical considerations in developing AI self clones.

Effective feedback, including critique and evaluation, helps designers develop design concepts and refine their ideas, supporting informed decision-making throughout the iterative design process. However, in studio-based design courses, students often struggle to provide feedback due to a lack of confidence and fear of being judged, which limits their ability to develop essential feedback-giving skills. Recent advances in large language models (LLMs) suggest that role-playing with AI agents can let learners engage in multi-turn feedback without the anxiety of external judgment or the time constraints of real-world settings. Yet prior studies have raised concerns that LLMs struggle to behave like real people in role-play scenarios, diminishing the educational benefits of these interactions. Therefore, designing AI-based agents that effectively support learners in practicing and developing intellectual reasoning skills requires more than merely assigning the target persona’s personality and role to the agent. By addressing these issues, we present Feed-O-Meter, a novel system that employs carefully designed LLM-based agents to create an environment in which students can practice giving design feedback. The system enables users to role-play as mentors, providing feedback to an AI mentee and allowing them to reflect on how that feedback impacts the AI mentee’s idea development process. A user study (N=24) indicated that Feed-O-Meter increased participants’ engagement and motivation through role-switching and helped them adjust feedback to be more comprehensible for an AI mentee. Based on these findings, we discuss future directions for designing systems to foster feedback skills in design education.

Social media clones are AI-powered social delegates of ourselves created using our personal data. As our identities and online personas intertwine, these technologies have the potential to greatly enhance our social media experience. If mismanaged however, these clones may also pose new risks to our social reputation and online relationships. To set the foundation for a productive and responsible integration, we set out to understand how social media clones will impact our online behavior and interactions. We conducted a series of semi-structured interviews introducing eight speculative clone concepts to 32 social media users through a design workbook. Applying existing work in AI-mediated communication in the context of social media, we found that although clones can offer convenience and comfort, they can also threaten the user’s authenticity and increase distrust within the online community. As a result users tend to behave more like their clones to mitigate discrepancies and interaction breakdowns. These findings are discussed through the lens of past literature in identity and impression management to highlight challenges in the adoption of social media clones by the general public, and propose design considerations for their successful integration into social media platforms.

Figure 1: Data manipulation is an approach that involves altering the value or structure of self-tracking data.With five data manipulation typology (Insert, Delete, Transform, Increase, and Reduce), we applied data manipulation through exploratory workshops and a one-week field trial.The above cards, which served as key materials for this study, provide an overview of the different types of data manipulation, along with definitions and examples for each type.

Nurturing the inner world of emotions, thoughts, and self is essential to our well-being. However, the abstract and invisible nature of the inner world makes it difficult for people to sense and manage. Then, what if people build their own virtual world where they can introspect inner sides? This pictorial presents the creation process of ‘OWN’, a personal virtual space where users, the OWNers, can explore and interact with their inner world. As part of the co-creation process, each OWNer expresses a personal narrative about themselves and their surroundings, allowing psychologists to gain an in-depth insight into their world. The psychologists’ analysis of the OWNer's inner state suggests various elements that the designer visualizes as space in the virtual world. By designing OWN with expressive activities, OWNers were able to reflect more deeply on their lives. OWN then became a virtual oasis where OWNers could relax, reflect, and improve themselves.

Hyungyu Shin, Jingyu Tang, Yoonjoo Lee, Nayoung Kim, Hyunseung Lim, Ji Yong Cho, Hwajung Hong, Moontae Lee, Juho Kim. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Patent examination remains an ongoing challenge in the NLP literature even after the advent of large language models (LLMs), as it requires an extensive yet nuanced human judgment on whether a submitted $\textit{claim}$ meets the statutory standards of $\textit{novelty}$ and $\textit{non-obviousness}$ against previously granted claims—$\textit{prior art}$—in expert domains. Previous NLP studies have approached this challenge as a prediction task (e.g., forecasting grant outcomes) with high-level proxies such as similarity metrics or classifiers trained on historical labels. However, this approach often overlooks the step-by-step evaluations that examiners must make with profound information, including rationales for the decisions provided in $\textit{office actions}$ documents, which also makes it harder to measure the current state of techniques in patent review processes. To fill this gap, we construct PANORAMA, a dataset of 8,143 U.S. patent examination records that preserves the full decision trails, including original applications, all cited references, $\textit{Non-Final Rejections}$, and $\textit{Notices of Allowance}$. Also, PANORAMA decomposes the trails into sequential benchmarks that emulate patent professionals' patent review processes and allow researchers to examine large language models' capabilities at each step of them. Our findings indicate that, although LLMs are relatively effective at retrieving relevant prior art and pinpointing the pertinent paragraphs, they struggle to assess the novelty and non-obviousness of patent claims. We discuss these results and argue that advancing NLP, including LLMs, in the patent domain requires a deeper understanding of real-world patent examination. Our dataset is openly available at https://huggingface.co/datasets/LG-AI-Research/PANORAMA.

Research promotion enables researchers to share advanced knowledge with pertinent academic communities. The question-and-answer (QA) style articles are effective for researchers to promote their research by enabling readers to understand research on complex subjects. Recent advances in large language models (LLMs) have opened avenues for supporting researchers in creating QA-style articles for research promotion. However, without the authors’ involvement, these models may only partially capture the researcher’s intention and voice. We developed AQUA, a research probe that enables researchers to co-create QA-style articles with LLMs to promote their research papers. A user study (n=12) reveals that LLMs reduced authors’ burden and helped them understand the readers’ perspectives. Nevertheless, LLMs failed to capture the unique intent of the authors, and their automated generation discouraged authors from carefully revising their answers. Based on our findings, we discuss human-LLM interaction design to enable authors to create QA-style articles that reflect their intention.

Autistic individuals often draw on insights from their supportive networks to develop self-help life strategies ranging from everyday chores to social activities. However, human resources may not always be immediately available. Recently emerging conversational agents (CAs) that leverage large language models (LLMs) have the potential to serve as powerful information-seeking tools, facilitating autistic individuals to tackle daily concerns independently. This study explored the opportunities and challenges of LLM-driven CAs in empowering autistic individuals through focus group interviews and workshops (N=14). We found that autistic individuals expected LLM-driven CAs to offer a non-judgmental space, encouraging them to approach day-to-day issues proactively. However, they raised issues regarding critically digesting the CA responses and disclosing their autistic characteristics. Based on these findings, we propose approaches that place autistic individuals at the center of shaping the meaning and role of LLM-driven CAs in their lives, while preserving their unique needs and characteristics.

Large Language Models (LLMs) offer promising opportunities in mental health domains, although their inherent complexity and low controllability elicit concern regarding their applicability in clinical settings. We present MindfulDiary, an LLM-driven journaling app that helps psychiatric patients document daily experiences through conversation. Designed in collaboration with mental health professionals, MindfulDiary takes a state-based approach to safely comply with the experts’ guidelines while carrying on free-form conversations. Through a four-week field study involving 28 patients with major depressive disorder and five psychiatrists, we examined how MindfulDiary facilitates patients’ journaling practice and clinical care. The study revealed that MindfulDiary supported patients in consistently enriching their daily records and helped clinicians better empathize with their patients through an understanding of their thoughts and daily contexts. Drawing on these findings, we discuss the implications of leveraging LLMs in the mental health domain, bridging the technical feasibility and their integration into clinical settings.

Maintaining a daily routine has profound implications for physical, emotional, and social well-being. Autistic individuals may experience various challenges in establishing and maintaining a healthy daily routine due to their tendency to be inactive in daily life combined with their characteristics and preferences. Previous studies employing mobile technology to support autistic individuals have primarily focused on self-help functions, with limited exploration into the detailed needs of these individuals to develop and maintain personalized routines. In this study, we conducted a nine-week field study with 18 autistic individuals using RoutineAid, a gamified app designed to support key routines of autistic individuals (i.e., physical activity, diet, mindfulness, and sleep). Our analysis incorporated five measures of self-evaluation on daily life, app usage logs, Fitbit physical activity data, and interviews. Our findings demonstrate the effectiveness of RoutineAid and highlight its two primary affordances for autistic individuals: (1) promoting self-efficacy and embedding health behavior and (2) refining daily routines for healthier outcomes. We discuss salient design insights for developing daily routine management systems for autistic individuals.

With their generative capabilities, large language models (LLMs) have transformed the role of technological writing assistants from simple editors to writing collaborators. Such a transition emphasizes the need for understanding user perception and experience, such as balancing user intent and the involvement of LLMs across various writing domains in designing writing assistants. In this study, we delve into the less explored domain of personal writing, focusing on the use of LLMs in introspective activities. Specifically, we designed DiaryMate, a system that assists users in journal writing with LLM. Through a 10-day field study (N=24), we observed that participants used the diverse sentences generated by the LLM to reflect on their past experiences from multiple perspectives. However, we also observed that they are over-relying on the LLM, often prioritizing its emotional expressions over their own. Drawing from these findings, we discuss design considerations when leveraging LLMs in a personal writing practice.

The reliability of online dating algorithms has sparked considerable debate, particularly regarding skepticism about their excessive emphasis on evaluating and getting evaluated, which often overshadows the quest for authentic romantic connections. To understand the multifaceted influence of dating algorithms on end-users and explore avenues for algorithmic features considering the dynamics of human relationships, we conducted a mixed-methods study comprising in-depth interviews (N = 22) and a metaphoric co-design workshop (N = 12) with active users of online dating platforms. Interviews revealed that users perceive and respond to algorithmic evaluations with varied perceptions and behaviors, often expressing concerns about the emotional burden of constant self-presentation and the pursuit of quantitative assessments over genuine connections. In the design workshop, users envisioned desired algorithmic features to overcome investigated challenges, such as prioritizing personal values, tailored matchmaking, and support for personal growth in relationships. This research contributes to unraveling the complex dynamics of human-algorithm interaction in the context of online dating. By aligning algorithmic functions more closely with user desires and relationship goals, this study paves the way for more meaningful and authentic connections in the digital dating landscape.

As online news increasingly include data journalism, there is a corresponding increase in the incorporation of visualization in article thumbnail images. However, little research exists on the design rationale for visualization thumbnails, such as resizing, cropping, simplifying, and embellishing charts that appear within the body of the associated article. Therefore, in this paper we aim to understand these design choices and determine what makes a visualization thumbnail inviting and interpretable. To this end, we first survey visualization thumbnails collected online and discuss visualization thumbnail practices with data journalists and news graphics designers. Based on the survey and discussion results, we then define a design space for visualization thumbnails and conduct a user study with four types of visualization thumbnails derived from the design space. The study results indicate that different chart components play different roles in attracting reader attention and enhancing reader understandability of the visualization thumbnails. We also find various thumbnail design strategies for effectively combining the charts' components, such as a data summary with highlights and data labels, and a visual legend with text labels and Human Recognizable Objects (HROs), into thumbnails. Ultimately, we distill our findings into design implications that allow effective visualization thumbnail designs for data-rich news articles. Our work can thus be seen as a first step toward providing structured guidance on how to design compelling thumbnails for data stories.

Online dating is a space where autistic individuals can find romantic partners with reduced social demands. Autistic individuals are often expected to adapt their behaviors to the social norms underlying the online dating platform to appear as desirable romantic partners. However, given that their autistic traits can lead them to different expectations of dating, it is uncertain whether conforming their behaviors to the norm will guide them to the person they truly want. In this paper, we explored the perceptions and expectations of autistic adults in online dating through interviews and workshops. We found that autistic people desired to know whether they behaved according to the platform’s norms. Still, they expected to keep their unique characteristics rather than unconditionally conform to the norm. We conclude by providing suggestions for designing inclusive online dating experiences that could foster self-guided decisions of autistic users and embrace their unique characteristics.

Implementing structure into our daily lives is critical for maintaining health, productivity, and social and emotional well-being. New norms for routine management have emerged during the current pandemic, and in particular, individuals with autism find it difficult to adapt to those norms. While much research has focused on the use of computer technology to support individuals with autism, little is known about ways of helping them establish and maintain “self-directed” routine structures. In this paper, we identify design requirements for an app that support four key routine components (i.e., physical activity, diet, mindfulness, and sleep) through a formative study and develop RoutineAid, a gamified smartphone app that reflects the design requirements. The results of a two-month field study on design feasibility highlight two affordances of RoutineAid—the establishment of daily routines by facilitating micro-planning and the maintenance of daily routines through celebratory interactions. We discuss salient design considerations for the future development of daily routine management tools for individuals with autism.

The integration of generative AI in the field of education is actively being explored. In particular, ChatGPT has garnered significant interest, offering an opportunity to examine its effectiveness in English as a foreign language (EFL) education. To address this need, we present a novel learning platform called RECIPE (Revising an Essay with ChatGPT on an Interactive Platform for EFL learners). Our platform features two types of prompts that facilitate conversations between ChatGPT and students: (1) a hidden prompt for ChatGPT to take an EFL teacher role and (2) an open prompt for students to initiate a dialogue with a self-written summary of what they have learned. We deployed this platform for 213 undergraduate and graduate students enrolled in EFL writing courses and seven instructors. For this study, we collect students' interaction data from RECIPE, including students' perceptions and usage of the platform, and user scenarios are examined with the data. We also conduct a focus group interview with six students and an individual interview with one EFL instructor to explore design opportunities for leveraging generative AI models in the field of EFL education.

Virtual reality (VR) has become a valuable tool for social and educational purposes for autistic people, as it provides flexible environmental support to create a variety of experiences. A growing body of recent research has examined the behaviors of autistic people using sensor-based data to better understand autistic people and investigate the effectiveness of VR. Comprehensive analysis of the various signals that can be easily collected in the VR environment can promote understanding of autistic people. While this quantitative evidence has the potential to help both autistic people and others (e.g., autism experts) to understand behaviors of autistic people, existing studies have focused on single signal analysis and have not determined the acceptability of signal analysis results from the autistic person’s point of view. To facilitate the use of multiple sensor signals in VR for autistic people and experts, we introduce V-DAT (Virtual Reality Data Analysis Tool), designed to support a VR sensor data handling pipeline. V-DAT takes into account four sensor modalities—head position and rotation, eye movement, audio, and physiological signals—that are actively used in current VR research for autistic people. We explain the characteristics and processing methods of the data for each modality as well as the analysis with comprehensive visualizations of V-DAT. We also conduct a case study to investigate the feasibility of V-DAT as a way of broadening understanding of autistic people from the perspectives of both autistic people and autism experts. Finally, we discuss issues with the process of V-DAT development and complementary measures for the applicability and scalability of a sensor data management system for autistic people.

Socially assistive robots can support people in making behavior changes by socially engaging in or moderating certain behaviors, such as physical exercise and snacking. However, there has not been much work on designing social robots that aim to support both social engagement and behavior moderation, i.e., offering social interactions for engaging in behaviors without over-engagement. This work explores how social robots can moderate alcohol consumption while socially engaging them in a solitary drinking context. As alcohol consumption can have benefits when done in moderation, this companion robot aims to guide the user toward moderate drinking by using social engagement (i.e., creating an enjoyable atmosphere) and drinking moderation (i.e., regulating the drinking pace). Our preliminary user study (n=20) reveals that the robot is perceived as a friendly companion, and its human-likeness is partly attributed to the robot's intervention. Most participants followed the robot's guidance and perceived it as an intelligent friend due to its social interactions and behavior tracking features. We discuss the benefit of physical interactions for social engagement, utilizing interaction rituals for enjoyable but moderate commensality, and ethical considerations in solitary drinking contexts.

Many digital wellbeing tools help users monitor and control social media use on their smartphones by tracking and setting limits on their usage time. Tracking is typically done at the granularity of phone- or app-level; however, recent social media apps provide various features such as direct messaging, comment reading/posting, and content uploading/viewing. While it is possible to track and analyze within-app feature usage, little is known about the effect of granularity on smartphone interventions. We designed and developed FinerMe to explore how the granularity of interventions (app-level vs. feature-level) affects the usage of popular social media such as Instagram and YouTube on smartphones. We conducted a field study with 56 participants over 16 days that consisted of three phases: baseline collection, self-reflection, and self-reflection with restrictive interventions. The results showed that while both app-level and feature-level interventions similarly reduced social media use, feature-level interventions enabled users to spend less time on passive app features related to content consumption (e.g., following feed on Instagram, and viewing comments on YouTube) than app-level interventions. Moreover, when self-reflection is combined with restrictive interventions at the feature-level, users were more reflective on their usage behavior than when done at the app-level.

YouTube is a space where people with disabilities can reach a wider online audience to present what it is like to have disabilities. Thus, it is imperative to understand how content creators with disabilities strategically interact with algorithms to draw viewers around the world. However, considering that the algorithm carries the risk of making less inclusive decisions for users with disabilities, whether the current algorithmic experiences (AXs) on video platforms is inclusive for creators with disabilities is an open question. To address that, we conducted semi-structured interviews with eight YouTubers with disabilities. We found that they aimed to inform the public of diverse representations of disabilities, which led them to work with algorithms by strategically portraying disability identities. However, they were disappointed that the way the algorithms work did not sufficiently support their goals. Based on findings, we suggest implications for designing inclusive AXs that could embrace creators’ subtle needs.

Reflecting on stress-related data is critical in addressing one’s mental health. Personal Informatics (PI) systems augmented by algorithms and sensors have become popular ways to help users collect and reflect on data about stress. While prediction algorithms in the PI systems are mainly for diagnostic purposes, few studies examine how the explainability of algorithmic prediction can support user-driven self-insight. To this end, we developed MindScope, an algorithm-assisted stress management system that determines user stress levels and explains how the stress level was computed based on the user’s everyday activities captured by a smartphone. In a 25-day field study conducted with 36 college students, the prediction and explanation supported self-reflection, a process to re-establish preconceptions about stress by identifying stress patterns and recalling past stress levels and patterns that led to coping planning. We discuss the implications of exploiting prediction algorithms that facilitate user-driven retrospection in PI systems.

Social Network Services (SNSs) evoke diverse affective experiences. While most are positive, many authors have documented both the negative emotions that can result from browsing SNS and their impact: Facebook depression is a common term for the more severe results. However, while the importance of the emotions experienced on SNSs is clear, methods to catalog them, and systems to detect them, are less well developed. Accordingly, this paper reports on two studies using a novel contextually triggered Experience Sampling Method to log surveys immediately after using Instagram, a popular image-based SNS, thus minimizing recall biases. The first study improves our understanding of the emotions experienced while using SNSs. It suggests that common negative experiences relate to appearance comparison and envy. The second study captures smartphone sensor data during Instagram sessions to detect these two emotions, ultimately achieving peak accuracies of 95.78% (binary appearance comparison) and 93.95% (binary envy).

We demonstrate StressTrendmeter, a mobile app that targets college students for anonymously sharing the source of stress via the form of hashtags, viewing stress topics based on trends, and providing social support through the empathy button and hashtag-based chat.

Pervasive symptoms of people with autism spectrum disorder (ASD), such as a lack of social and communication skills, are major challenges to be embraced in the workplace. Although much research has proposed VR training programs, their effectiveness is somewhat unclear, since they provide limited, one-sided interactions through fixed scenarios or do not sufficiently reflect the characteristics of people with ASD (e.g., preference for predictable interfaces, sensory issues). In this paper, we present VISTA, a VR-based interactive social skill training system for people with ASD. We ran a user study with 10 people with ASD and 10 neurotypical people to evaluate user experience in VR training and to examine the characteristics of people with ASD based on their physical responses generated by sensor data. The results showed that ASD participants were highly engaged with VISTA and improved self-efficacy after experiencing VISTA. The two groups showed significant differences in sensor signals as the task complexity increased, which demonstrates the importance of considering task complexity in eliciting the characteristics of people with ASD in VR training. Our findings not only extend findings (e.g., low ROI ratio, EDA increase) in previous studies but also provide new insights (e.g., high utterance rate, large variation of pupil diameter), broadening our quantitative understanding of people with ASD.

People experience various stressful events in their daily lives. Receiving social support, especially from peers who went through a similar experience, helps individuals cope with such stress. We propose StressTrendmeter, a mobile application that targets college students for anonymously sharing the source of stress via the form of hashtags, viewing stress topics based on trends, and providing social support through the empathy button and hashtag-based chat. We deployed StressTrendmeter to 222 students from two universities for five weeks. With hashtags and trending features, students found StressTrendmeter (i)helpful to spontaneously yet concisely articulate their stress topics and (ii) easy to browse through and become aware of issues around the campus. Our study reveals that social sharing with StressTrendmeter brought awareness, resonance, and accountability as students empathized and expressed support. Based on our study, we share design implications for social support systems with community awareness.

Challenging behaviors significantly impact learning and socialization of autistic children and can stress and burden their caregivers. Documentation of challenging behaviors is fundamental for identifying what environmental factors influence them, such as how others respond to a child's such behaviors. Caregiver-tracked data on their child's challenging behaviors can help clinical experts make informed recommendations about how to manage such behaviors. To support caregivers in recording their children's challenging behaviors, we developed GeniAuti, a mobile-based data-collection tool built upon a clinical data collection form to document challenging behaviors and other clinically relevant contextual information such as place, duration, intensity, and what triggers such behaviors. Through an open-ended deployment with 19 parent-child pairs and three expert collaborators, caregivers found GeniAuti valuable for (1) becoming more attentive and reflective to behavioral contexts, including their own response strategies, (2) discovering positive aspects of their children's behaviors, and (3) promoting collaboration with clinical experts around the caregiver-tracked data to develop tailored intervention strategies for their children. However, participant experiences surface challenges of logging behaviors in social circumstances, conflicting views between caregivers and clinical experts around the structured recording process, and emotional struggles resulting from recording and reflecting on intensely negative experiences. Considering the complex nature of caregiver-based health tracking and caregiver--clinician collaboration, we suggest design opportunities for facilitating negotiations between caregivers and clinicians and accounting for caregivers' emotional needs.

A growing number of organizations are hiring autistic individuals as they start to recognize the value of a neurodiverse workforce. Despite this trend, the lack of support for autistic employees in workplaces complicates their employment. However, little is known about how people around autistic individuals can support them to create pleasant employment experiences. In this work, we develop the concept of the Workplace Playbook VR to investigate how virtual reality (VR) can help autistic people develop their work-related social communication skills in partnership with people in their support network. Using a video prototype to present the concept, we interviewed 28 participants, including 10 autistic people and 18 members of their support networks, which included family members and professionals. Our interviews revealed that the Workplace Playbook VR program can provide common ground for autistic people and members of their support network to participate in more empathetic communication regarding workplace challenges. Despite the benefits, we identified the potential misuse of social communication skills training features of the VR program to correct the personal characteristics of autistic individuals. Furthermore, to cultivate inclusive workplace environments, we found the needs of VR development not only for autistic people but also for neurotypical employees to promote their understanding of autism and empathy toward autistic employees. We suggest VR designs that promote a sense of agency and self-advocacy for autistic employees, and autism awareness and acceptance training for neurotypical employees.

Contact tracing, a major way to curb COVID-19 and other epidemics, has been employed worldwide, with human interviewing and proximity tracing technology as two major approaches. While previous research has contributed some understanding of people's perspectives on contact tracing technology, much of this is based in single countries or regions where technology has been deployed. To understand how culture influences people's perceptions toward human tracing and digital tracing, we replicated a mixed-methods survey study conducted in the U.S. in South Korea and compared participants' perspectives. South Korean participants preferred digital tracing to human tracing, contrasting with the U.S. context where no strong preference was observed. We discuss how observed differences in perspective align and contrast with the country's typical cultural dimensions, such as high power distance, informing the perspective that human tracing will have greater accuracy. We emphasize the need for culturally designing contact tracing technology to highlight personal benefits regardless of cultural dimensions, and leverage technology to support social interaction in human tracing.

A literature review is a critical task in performing research. However, even browsing an academic database and choosing must-read items can be daunting for novice researchers. In this paper, we introduce Papers101, an interactive system that supports novice researchers' discovery of papers relevant to their research topics. Prior to system design, we performed a formative study to investigate what difficul-ties novice researchers often face and how experienced researchers address them. We found that novice researchers have difficulty in identifying appropriate search terms, choosing which papers to read first, and ensuring whether they have examined enough candidates. In this work, we identified key requirements for the system dedicated to novices: prioritizing search results, unifying the contexts of multiple search results, and refining and validating the search queries. Accordingly, Papers101 provides an opinionated perspective on selecting important metadata among papers. It also visualizes how the priority among papers is developed along with the users' knowledge discovery process. Finally, we demonstrate the potential usefulness of our system with the case study on the metadata collection of papers in visualization and HCI community.

Voice-based Conversational Agents (VCA) have served as personal assistants that support individuals with special needs. Adolescents with Autism Spectrum Disorder (ASD) may also benefit from VCAs to deal with their everyday needs and challenges, ranging from self-care to social communications. In this study, we explored how VCAs could encourage adolescents with ASD in navigating various aspects of their daily lives through the two-week use of VCAs and a series of participatory design workshops. Our findings demonstrated that VCAs could be an engaging, empowering, emancipating tool that supports adolescents with ASD to address their needs, personalities, and expectations, such as promoting self-care skills, regulating negative emotions, and practicing conversational skills. We propose implications of using off-the-shelf technologies as a personal assistant to ASD users in Assistive Technology design. We suggest design implications for promoting positive opportunities while mitigating the remaining challenges of VCAs for adolescents with ASD.

A commitment device, an attempt to bind oneself for a successful goal achievement, has been used as an effective strategy to promote behavior change. However, little is known about how commitment devices are used in the wild, and what aspects of commitment devices are related to goal achievements. In this paper, we explore a large-scale dataset from stickK, an online behavior change support system that provides both financial and social commitments. We characterize the patterns of behavior change goals (e.g., topics and commitment setting) and then perform a series of multilevel regression analyses on goal achievements. Our results reveal that successful goal achievements are largely dependent on the configuration of financial and social commitment devices, and a mixed commitment setting is considered beneficial. We discuss how our findings could inform the design of effective commitment devices, and how large-scale data can be leveraged to support data-driven goal elicitation and customization.

Although audiobooks are increasingly being used, people tend to perceive audiobook experiences as 'not real reading' due to its intangibility and ephemerality. In this paper, we developed ADIO, a device augmenting audiobook experience through representing personal listening state in the form of an interactive physical bookshelf. ADIO displays a user's listening progress through a pendant's changing length and the user's digital audiobook archive titles. The result of our four-week in-field study with six participants revealed that ADIO provided proof of the user's listening-to, which brought a sense of reading and gave a trigger for recalling the listened-to audiobook content. Additionally, audiobooks' improved visibility reminded participants to listen to them, and ADIO's physical interaction allowed participants to form personal patterns for listening to audiobooks. Our findings proposed new methods for augmenting the audiobook listening experience at three stages and further implications for designing physical curation on users’ digital archives.

Various contact tracing approaches have been applied to help contain the spread of COVID-19, with technology-based tracing and human tracing among the most widely adopted. However, governments and communities worldwide vary in their adoption of digital contact tracing, with many instead choosing the human approach. We investigate how people perceive the respective benefits and risks of human and digital contact tracing through a mixed-methods survey with 291 respondents from the United States. Participants perceived digital contact tracing as more beneficial for protecting privacy, providing convenience, and ensuring data accuracy, and felt that human contact tracing could help provide security, emotional reassurance, advice, and accessibility. We explore the role of self-tracking technologies in public health crisis situations, highlighting how designs must adapt to promote societal benefit rather than just self-understanding. We discuss how future digital contact tracing can better balance the benefits of human tracers and technology amidst the complex contact tracing process and context.

Social translucence theory argues that online collaboration systems should make contributors' activities visible to better achieve a common goal. Currently in medical crowdfunding sites, various non-monetary contributions integral to the success of a campaign, such as campaign promotions and offline support, are less visible than monetary contributions. Our work investigates ways to enrich social translucence in medical crowdfunding by aggregating and visualizing non-monetary contributions that reside outside of the current crowdfunding space. Three different styles of interactive visualizations were built and evaluated with medical crowdfunding beneficiaries and contributors. Our results reveal the perceived benefits and challenges of making the previously invisible non-monetary contributions visible using various design features in the visualizations. We discuss our findings based on the social translucence framework--visibility, awareness, and accountability--and suggest design guidelines for crowdfunding platform designers.

New parents often experience significant stress as they take on new roles and responsibilities. Stress management and mental wellbeing are two areas in which personal informatics (PI) research has gained attention, and there is an opportunity to investigate how parenting stress can be mitigated through PI practices. In this paper, we present the results of a co-designed technology probe study through which we deployed individualized self-trackers with new parents. We investigate the stress management topics new parents are interested in tracking and how — and with what goals---they engage in self-directed PI practices. Our findings indicate that PI practices can potentially enable parents to: re-discover positive aspects of their everyday lives; identify better-suited stress management strategies; and facilitate spousal communication about shared responsibilities. We discuss how self-tracking experiences for the mental wellness of parents can be better designed.

Helping others can have a positive effect on both the giver and the receiver. However, supporting someone with depression can be complicated and overwhelming. To address this, we proposed a Facebook-based social bot displaying depressive symptoms and disclosing vulnerable experiences that allows users to practice providing reactions online. We investigated how 55 college students interacted with the social bot for three weeks and how these support-giving experiences affected their mental health and stigma. By responding to the bot, the participants reframed their own negative experiences, reported reduced feelings of danger regarding an individual with depression and increased willingness to help the person, and presented favorable attitudes toward seeking treatment for depression. We discuss design opportunities for accessible social bots that could help users to keep practicing peer support interventions without fear of negative consequences.

Stress is caused by a variety of events in our daily lives. By anticipating stressful situations, we can prepare and better cope with stressors when they actually occur. However, many past-centric personal informatics (PI) tools focus on capturing events that already happened and analyzing the data. In this work, we examine how anticipation — a future-centric self-tracking practice — could be used to manage daily stress levels. To address this, we built MindForecaster, a calendar- mediated stress anticipation application that allows users to expect stressful events in advance, generates activities to mitigate stress, and evaluates actual stress levels compared to previously estimated stress levels. In a 30-day deployment with 47 users, the users who explicitly planned and executed coping interventions reported reduced stress more than those who only expected stressful events. We suggest design implications for stress management by incorporating the properties of anticipation into current PI models.

Many parents of young children find it challenging to deal with their children's eating problems, and parent--child mealtime interaction is fundamental in forming children's healthy eating habits. In this paper, we present the results of a three-week study through which we deployed a mealtime assistant application, MAMAS, for monitoring parent--child mealtime conversation and food intake with 15 parent--child pairs. Our findings indicate that the use of MAMAS helped 1) increase children's autonomy during mealtime, 2) enhance parents' self-awareness of their words and behaviors, 3) promote the parent--child relationship, and 4) positively influence the mealtime experiences of the entire family. The study also revealed some challenges in eating behavior interventions due to the complex dynamics of childhood eating problems. Based on the findings, we discuss how a mealtime assistant application can be better designed for parents and children with challenging eating behaviors.

People desire to present themselves favorably to others. However, medical crowdfunding beneficiaries are often expected to present their dire medical conditions and financial straits to solicit financial support. To investigate how beneficiaries convey their situation on medical crowdfunding pages and how contributors perceive the presented information, we interviewed both medical crowdfunding beneficiaries and contributors. While beneficiaries emphasized the serious of their medical situations to signal their deservedness of support, contributor participants gave less attention to that content. Rather, they focused on their impression of the beneficiary's character formed by various features of contributions such as the contributor's names, messages, and shared pictures. These contribution features further signaled common connections between the beneficiary and contributors and each contributor's unique involvement in the beneficiary's medical journey. However, the contribution amount resulted in judgement about other contributors. We suggest design opportunities and challenges that apply these results to the design of medical crowdfunding interfaces.

Mental wellness is a desirable health outcome for students. However, current personal informatics systems do not adequately support students in creating concrete mental health-related goals and turning them into actionable plans. In this paper, we introduce MindNavigator - a workshop in which groups of college students were invited to generate behavioral change goals to manage daily life stress and practice personalized interventions for two weeks. We describe the manner in which participants identified both stressors and pleasures to create actionable, engaging, and open-ended behavioral plans that aided in stress relief. We found that the social nature of the workshop helped participants understand themselves and execute self-intervention in new ways. Through this practice, we build on prior studies to propose an analytical framework of personal informatics for mental wellness.

Self-tracking technologies have been developed to understand the self. Emotions are critical to understanding one's daily life; however, tracking the emotion is challenging due to the implicit form of data. In this paper, we introduce MindTracker, an approach for tracking emotion through a tangible interaction with plasticine clay. We explored the benefits and challenges of MindTracker via a two-week data collection study with 16 college students as well as via interviews with three clinical mental health experts. MindTracker is designed for users to craft a form that represents emotion using clay and to describe the experience that evokes the emotion using a diary. We found that the tangible modality of MindTracker motivated the participants to express various aspects of emotions. In addition, MindTracker's data collection and reflection process could have therapeutic properties, such as expressive therapy, self-soothing, and emotional self-regulation. We conclude this paper by discussing the design features of emotion-tracking tools and opportunities to use MindTracker to promote mental health.

Medical crowdfunding helps patients receive financial support from their distributed social networks online. However, little is known about who the patient's supporters are, what support they provide, and why. To address this, we interviewed fifteen people involved in medical crowdfunding, including both beneficiaries and supporters. We found that support networks were larger than beneficiaries expected, with strangers offering support. Supporters offered not only monetary but also volunteering contributions including campaign creation, promotion, and external support. However, the emphasis medical crowdfunding interfaces place on monetary contributions led to social issues. Beneficiaries' close friends felt pressured to donate money they could not afford to give. And beneficiaries promoting the campaign worried they would be judged for requesting money. To mitigate these concerns, we suggest making the variety of volunteering contributions more visible and discuss the design challenges of including such signals in existing systems.

Traditional medical fundraising charities have been relying on third-party watchdogs and carefully crafting their reputation over time to signal their credibility to potential donors. As medical fundraising campaigns migrate to online platforms in the form of crowdfunding, potential donors can no longer rely on the organization's traditional methods for achieving credibility. Individual fundraisers must establish credibility on their own. Potential donors, therefore, seek new factors to assess the credibility of crowdfunding campaigns. In this paper, we investigate current practices in assessing the credibility of online medical crowdfunding campaigns. We report results from a mixed-methods study that analyzed data from social media and semi-structured interviews. We discovered eleven factors associated with the perceived credibility of medical crowdfunding. Of these, three communicative/emotional factors were unique to medical crowdfunding. We also found a distinctive validation practice, the collective endorsement. Close-connections' online presence and external online communities come together to form this collective endorsement in online medical fundraising campaigns. We conclude by describing how fundraisers can leverage collective endorsements to improve their campaigns' perceived credibility.

Difficulty in navigating daily life can lead to frustration and decrease independence for people with autism. While they turn to online autism communities for information and advice for coping with everyday challenges, these communities may present only a limited perspective because of their in-group nature. Obtaining support from out-group sources beyond the in-group community may prove valuable in dealing with challenging situations such as public anxiety and workplace conflicts. In this paper, we explore the value of supplementary out-group support from crowdsourced responders added to in-group support from a community of members. We find that out-group sources provide relatively rapid, concise responses with direct and structured information, socially appropriate coping strategies without compromising emotional value. Using an autism community as a motivating example, we conclude by providing design implications for combining in-group and out-group resources that may enhance the question-and-answer experience.

Building social support networks is crucial both for less-independent individuals with autism and for their primary caregivers. In this paper, we describe a four-week exploratory study of a social network service (SNS) that allows young adults with autism to garner support from their family and friends. We explore the unique benefits and challenges of using SNSs to mediate requests for help or advice. In particular, we examine the extent to which specialized features of an SNS can engage users in communicating with their network members to get advice in varied situations. Our findings indicate that technology-supported communication particularly strengthened the relationship between the individual and extended network members, mitigating concerns about over-reliance on primary caregivers. Our work identifies implications for the design of social networking services tailored to meet the needs of this special needs population.

Independence is key to a successful transition to adulthood for individuals with autism. Social support is a crucial factor for achieving adaptive self-help life skills. In this paper we describe the results of a formative design exercise with young adults with autism and their caregivers to uncover opportunities for social networks to promote independence and facilitate coordination. We propose the concept of SocialMirror, a device connected to an online social network that allows the young adult to seek advice from a trusted and responsive network of family, friends and professionals. Focus group discussions reveal the potential for SocialMirror to increase motivation to learn everyday life skills among young adults with autism and to foster collaboration among a distributed care network. We present design considerations to leverage a small trusted network that balances quick response with safeguards for privacy and security of young adults with autism.

We present the findings from a cross-cultural study of the expectations and perceptions of individuals with autism and other intellectual disabilities (AOID) in Kuwait, Pakistan, South Korea, and the United States. Our findings exposed cultural nuances that have implications for the design of assistive technologies. We develop a framework, based on three themes; 1) lifestyle; 2) socio-technical infrastructure; and 3) monetary and informational resources within which the cultural implications and opportunities for assistive technology were explored. The three key contributions of this work are: 1) the development of a framework that outlines how culture impacts perceptions and expectations of individuals with social and intellectual disabilities; 2) a mapping of how this framework leads to implications and opportunities for assistive technology design; 3) the presentation of concrete examples of how these implications impact the design of three emerging assistive technologies.

People are ready to change themselves to adopt more eco-friendly habits such as conserving electricity when they are aware of the possible problems of their lifestyle. In this sense, ambient display, which users experience occasionally without its interfering with their primary tasks, is well suited to provide the feedback of their personal activities in a more subtle manner than direct information presentation. We present the results of user studies with two ambient displays in different visualization styles. Participants showed diverse usage behaviors of ambient displays according to their motivational level of sustainable lifestyle. In addition, iconic metaphor of eco-visualization can trigger more emotional attachment while indexical representation helps retrospective functions. Finally, we suggest design requirements for ambient displays that support different stages of persuasion from raising awareness to motivating to change behaviors and to maintaining desired habits.