Information Retrieval (IR) increasingly relies on subjective, graded, and natural-language notions of relevance, motivating the development of reproducible test collections for ranking and recommendation. In affect-sensitive music domains, however, such resources with human-validated relevance signals remain scarce. We introduce CalmSet, a test collection for emotion-tagged music retrieval and recommendation in a therapeutic context for children with Autism Spectrum Disorder (ASD). CalmSet contains 432 modular music tracks instantiated from four purposefully composed base songs with controlled provenance, each formed by distinct combinations of seven active musical layers. Each track is annotated with ranked top-3 therapeutic intent labels and natural-language descriptions. Annotations are produced via a hybrid human-in-the-loop pipeline: CLAP proposes candidate intent labels, a large language model generates auxiliary semantic descriptions, and crowd workers provide ranked judgments without exposure to model outputs; final labels are aggregated using a Borda-based procedure. As initial baselines, we evaluate five one-vs-rest multi-label classifiers over CLAP audio embeddings, observing moderate micro-F1 scores (up to 0.60) but low exact-match accuracy (<0.10), while top-3 label overlap is substantially higher (Jaccard@3 up to 0.48), motivating graded-relevance evaluation. CalmSet supports both sparse (e.g., BM25) and dense audio–text retrieval models using therapeutic labels or natural-language descriptions as queries.

Children with Autism Spectrum Disorder (ASD) often face challenges with musical engagement due to unique sensory and neural processing needs.To address this, we introduce uCue, a musical interface designed to enhance active musical engagement.This interactive playback system offers modular arrangements of children's songs at an accessible tempo, enabling listeners to manipulate musical layers and create personalized renditions in real-time.Deployed in listening sessions with seven parent-child dyads, uCue facilitated self-expression through singing and gestures while fostering emotional regulation and sensory engagement.Participants quickly adapted to the interface, preferred soothing sounds, and expressed interest in more rhythmic layers over time.Our findings suggest that uCue has the potential to enhance musical interactions by allowing children to explore and control auditory experiences.We also discuss how uCue and data from its logs might support therapeutic goals in music therapy for children with ASD and provide design recommendations for similar technologies.

Large Language Models (LLMs) can analyze large datasets semantically. However, research on applying LLMs for mental health text classification is relatively new and developing. Existing methods often use supervised, deep, and reinforcement learning, which rely heavily on fine-tuning and reward models. To investigate whether LLMs can assist in recommending mental health apps based on user reviews, our study collected approximately 200k user reviews from 73 mental health mobile applications. We instructed selected LLMs to classify individual reviews into 1-5 star ratings, subsequently averaging these results to derive an overall rating for each app reflecting current user feedback. While the best supervised learning method in our experiments achieved an F1-Score of 0.79 which required significantly more human effort, the GPT-4 and Gemini 1.5 Pro delivered a strong ‘out-of-the-box’ performance with an overall F1-Score of 0.76. We provide further statistical comparisons and discussions of the performance of these models for the text classification task. Using a crowdsourcing platform to determine agreement levels, we observed that human ratings align closely with GPT ratings. In addition, we analyze specific features and concerns highlighted in mental health app reviews. Alongside our analysis, we make our data available for further experimentation and benchmarking.

Traditional history education often fails to cultivate historical empathy due to rigid curricula and limited opportunities for personalized, emotionally resonant engagement. We explore the potential of LLM-based historical personas to address these gaps by enabling students to engage in real-time, conversational interactions with simulated historical figures. A formative study with teachers and students surfaced key challenges and expectations around AI-mediated historical dialogue, informing the development of Baseline and Experimental HistoChat, AI persona systems featuring differing prompting strategies. A subsequent user study showed that these interactions fostered deeper inquiry, curiosity, and emotional engagement-while also revealing key limitations. From a CSCW perspective, this work expands the role of AI from task assistant to epistemic partner, contributing to ongoing discourse on how dialogic systems can support meaning-making, empathy, and co-constructed learning in educational settings. Our findings yield valuable insights into the impact of tailored AI interactions on personalized and empathetic history education.

Social media platforms have transformed global communication and interaction, with TikTok emerging as a critical tool for education, connection, and social impact, including in contexts where infrastructural resources are limited. Amid growing political discussions about banning platforms like TikTok, such actions can create significant ripple effects, particularly impacting marginalized communities. We present a study on Nepal, where a TikTok ban was recently imposed and lifted. As a low-resource country in transition where digital communication is rapidly evolving, TikTok enables a space for community engagement and cultural expression. In this context, we conducted an online survey ( N=108 ) to explore user values, experiences, and strategies for navigating online spaces post-ban. By examining these transitions, we aim to improve our understanding of how digital technologies, policy responses, and cultural dynamics interact globally and their implications for governance and societal norms. Our results indicate that users express skepticism toward platform bans but often passively accept them without active opposition. Findings suggest the importance of institutionalizing collective governance models that encourage public deliberation, nuanced control, and socially resonant policy decisions.

Misinformation permeates online news media, making it hard for users to trust and verify content. Building upon prior work that highlights online news challenges and the importance of digital literacy skills, we examine user digital media skills, online news consumption behaviors, and perceptions of current misinformation tools. To better understand these dynamics, we conducted a formative, mixed methods study (n=34) that included a survey, two weeks of browser-based application logging using a Chrome plugin, and follow-up semi-structured interviews. Contradictions in the survey and log results indicate that participants in our sample often overestimate their news consumption habits. While information and news media literacy scores are generally high, less than half (47%, 16/34) exhibit lateral reading. We provide insights into users' challenges in navigating today's information landscape and propose effective integrated solutions. Interview findings inform the design of online news interventions and personal informatics tools to further improve media literacy while maintaining user privacy.

Large Language Models (LLMs) have gained attention in research and industry, aiming to streamline processes and enhance text analysis performance. Thematic Analysis (TA), a prevalent qualitative method for analyzing interview content, often requires at least two human experts to review and analyze data. This study demonstrates the feasibility of LLM-Assisted Thematic Analysis (LATA) using GPT-4 and Gemini. Specifically, we conducted semi-structured interviews with 14 researchers to gather insights on their experiences generating and analyzing Online Social Network (OSN) communications datasets. Following Braun and Clarke's six-phase TA framework with an inductive approach, we initially analyzed our interview transcripts with human experts. Subsequently, we iteratively designed prompts to guide LLMs through a similar process. We compare and discuss the manually analyzed outcomes with responses generated by LLMs and achieve a cosine similarity score up to 0.76, demonstrating a promising prospect for LATA. Additionally, the study delves into researchers' experiences navigating the complexities of collecting and analyzing OSN data, offering recommendations for future research and application designers.

With advances in electronic-skin and wearable technologies, it is possible to continuously measure stress markers from the skin and sweat to monitor and improve wellbeing and health. Understandably, the sensor’s engineering and resolution are important towards its function. However, we find that people looking for an e-skin stress sensor may look beyond measurement precision, demanding a private and stealth design to reduce, for example, social stigmatization. We introduce the idea of a stress sensing "wear index," created from the combination of human-centered design (n=24), physiological (n=10), and biochemical (n=16) data. This wear index can inform the design of stress wearables to fit specific applications, e.g., human factors may be relevant for a wellbeing application, versus a relapse prevention application that may require more sensing precision. Our wear index idea can be further generalized as a method to close gaps between design and engineering practices.

On-demand mental health services-including counseling, crisis hotlines, and peer support programs-are vital to the healthcare system, providing acute and ongoing support through telephone, online chats, and text messaging. Although such services have proven effective at reducing hopelessness, psychological pain, and suicidality, they put the providers of these services at high risk of burnout, secondary traumatic stress, and compassion fatigue. Our interviews with professionals from four mental health organizations revealed that while these workers have a strong motivation to help clients with mental health care needs, they face various challenges themselves, particularly regarding heavy caseloads, difficult crisis clients, and coping with repeated exposure to abuse and harassment. To overcome challenges, participants identify the need to be self-reliant and engage in self-care practices ranging from socializing with coworkers to yoga and meditation. Although organizations spend significant time training workers prior to their involvement with clients, the training typically lacks components on self-compassion and self-care. Designers might see technology as an opportunity to promote such practices; however, while technology is an integral part of their work routine, participants, irrespective of age, had misapprehensions regarding technology use in the mental health care space, including for managing their psychological well-being. We recommend design guidelines for HCI researchers, including developing contextualized just-in-time adaptive interventions to promote self-compassion and educating workers regarding the use of various technologies to manage their well-being.

Modern stress management techniques have been shown to be effective, particularly when applied systematically and with the supervision of an instructor. However, online workers usually lack sufficient support from therapists and learning resources to self-manage their stress. To better assist these users, we implemented a browser-based application, Home Sweet Office (HSO), to administer a set of stress micro-interventions which mimic existing therapeutic techniques, including somatic, positive psychology, meta cognitive, and cognitive behavioral categories. In a four-week field study, we compared random and machine-recommended interventions to interventions that were self-proposed by participants in order to investigate effective content and recommendation methods. Our primary findings suggest that both machine-recommended and self-proposed interventions had significantly higher momentary efficacy than random selection, whereas machine-recommended interventions offer more activity diversity compared to self-proposed interventions. We conclude with reflections on these results, discuss features and mechanisms which might improve efficacy, and suggest areas for future work.

Underperforming, degraded, and missing insulation in US residential buildings is common. Detecting these issues, however, can be difficult. Using thermal cameras during energy audits can aid in locating potential insulation issues, but prior work indicates it is challenging to determine their severity using thermal imagery alone. In this work, we present an easy-to-deploy, temporal thermographic sensor system designed to support residential energy audits through quantitative analysis of building envelope performance. We then offer an evaluation of the system through two studies: (i) a one-week, in-home field study in five homes and (ii) a semi-structured interview study with five professional energy auditors. Our results show our system helps raise awareness, improves homeowners' ability to gauge the severity of issues, and provides opportunities for new interactions between homeowners, building data, and professional auditors.

Parents use mobile monitoring software to observe and restrict their children's activities in order to minimize the risks associated with Internet-enabled mobile devices. As children are stakeholders in such technologies, recent research has called for their inclusion in its design process. To investigate children's perceptions of parental mobile monitoring technologies and explore their interaction preferences, we held two co-design sessions with 12 children ages 7-12. Children first reviewed and redesigned an existing mobile monitoring application. Next, they designed ways children could use monitoring software when they encounter mobile risks (e.g., cyberbullying, inappropriate content). Results showed that children acknowledged safety needs and accepted certain parental controls. They preferred and designed controls that emphasized restriction over monitoring, taught risk coping, promoted parent-child communication, and automated interactions. Our results benefit designers looking to develop parental mobile monitoring technologies in ways that children will both accept and can actively benefit from.

The emergence of low-cost thermographic cameras for mobile devices provides users with new practical and creative prospects. While recent work has investigated how novices use thermal cameras for energy auditing tasks in structured activities, open questions remain about "in the wild" use and the challenges or opportunities therein. To study these issues, we analyzed 1,000 YouTube videos depicting everyday uses of thermal cameras by non-professional, novice users. We coded the videos by content area, identified whether common misconceptions regarding thermography were present, and analyzed questions within the comment threads. To complement this analysis, we conducted an online survey of the YouTube content creators to better understand user behaviors and motivations. Our findings characterize common thermographic use cases, extend discussions surrounding the challenges novices encounter, and have implications for the design of future thermographic systems and tools.

The recent integration of thermal cameras with commodity smartphones presents an opportunity to engage the public in evaluating energy-efficiency issues in the built environment. However, it is unclear how novice users without professional experience or training approach thermographic energy auditing activities. In this paper, we recruited 10 participants for a four-week field study of end-user behavior exploring novice approaches to semi-structured thermographic energy auditing tasks. We analyze thermographic imagery captured by participants as well as weekly surveys and post-study debrief interviews. Our findings suggest that while novice users perceived thermal cameras as useful in identifying energy-efficiency issues in buildings, they struggled with interpretation and confidence. We characterize how novices perform thermographic-based energy auditing, synthesize key challenges, and discuss implications for design.

The direct gains children perceive from their membership on Participatory Design (PD) teams are seldom the focus of research studies. Yet, how HCI practitioners choose to include children in PD methods may influence the value participants see in their participation, and thereafter the outcomes of PD processes. To understand what gains former child members of a PD team perceive from their participation we conducted a two-part study. In Study 1 we surveyed and interviewed child alumni of a PD team to determine gains that are perceived first-hand. In Study 2 we obtained a secondary perspective by surveying and interviewing parents of alumni. We report on the perceived gains to former participants that were identified and described in these two studies-including collaboration, communication, design process knowledge, and confidence. We reflect on our findings through discussions of the continued applicability of gains, new opportunities, and implications for PD practitioners and methods.

Participatory Design (PD) gives users a voice in the design of technologies they are meant to use. When PD methods are adapted for research with children, design teams need to address additional issues of ethical accountability (e.g., adult-child power relations). While researchers have taken measures to ensure ethical accountability in PD research with children, to our knowledge there has been no work examining how former child design partners view ethical issues surrounding their participation. In this work we ask: How do children view ethical issues around their role on Participatory Design teams? We present findings from surveys and interviews with 12 former child design partners. Findings, identified by the former participants themselves, outline: (i) balancing attribution and anonymity, (ii) promoting ongoing consent and dissent, and (iii) cultivating a balanced design partnership. From these findings we recommend practices for researchers and designers of children's technologies that align with participant views.

The building sector accounts for 41% of primary energy consumption in the US, contributing an increasing portion of the country's carbon dioxide emissions. With recent sensor improvements and falling costs, auditors are increasingly using thermography-infrared (IR) cameras-to detect thermal defects and analyze building efficiency. Research in automated thermography has grown commensurately, aimed at reducing manual labor and improving thermal models. Though promising, we could find no prior work exploring the professional auditor's perspectives of thermography or reactions to emerging automation. To address this gap, we present results from two studies: a semi-structured interview with 10 professional energy auditors, which includes design probes of five automated thermography scenarios, and an observational case study of a residential audit. We report on common perspectives, concerns, and benefits related to thermography and summarize reactions to our automated scenarios. Our findings have implications for thermography tool designers as well as researchers working on automated solutions in robotics, computer science, and engineering.

Internal organs are hidden and untouchable, making it difficult for children to learn their size, position, and function. Traditionally, human anatomy (body form) and physiology (body function) are taught using techniques ranging from worksheets to three-dimensional models. We present a new approach called BodyVis, an e-textile shirt that combines biometric sensing and wearable visualizations to reveal otherwise invisible body parts and functions. We describe our 15-month iterative design process including lessons learned through the development of three prototypes using participatory design and two evaluations of the final prototype: a design probe interview with seven elementary school teachers and three single-session deployments in after-school programs. Our findings have implications for the growing area of wearables and tangibles for learning.

Group exercise has multiple benefits including greater adherence to fitness regimens, increased enjoyment among participants, and enhanced workout intensity. While a large number of technology tools have emerged to support real-time feedback of individual performance, tools to support group fitness are limited. In this paper, we present a set of wearable e-textile displays for running groups called Social Fabric Fitness (SFF). SFF provides a glanceable, shared screen on the back of the wearer's shirt to increase awareness and motivation of group fitness performance. We discuss parallel prototyping of three designs-one flexible e-ink and two flexible LED-based displays; the selection and refinement of one design; and two evaluations'a field study of 10 running groups and two case studies of running races. Our qualitative findings indicate that SFF improves awareness of individual and group performance, helps groups stay together, and improves in-situ motivation. We close with reflections for future athletic e-textile displays.