Despite increasing interest in culturally-sensitive health technologies, medical mistrust remains largely unexplored within human-centered computing. Considered a social determinant of health, medical mistrust is the belief that healthcare providers or institutions are acting against one’s best interest. This is a rational, protective response based on historical context, structural inequities, and discrimination. To center race-based medical mistrust and the lived experiences of Black older adults with low income, we conducted interviews within publicly subsidized housing in the Southern United States. Our reflexive themes describe community perspectives on health care and medical mistrust, including accreditation and embodiment, skepticism of financial motivations, and the intentions behind health AI. We provide a reflective exercise for researchers to consider their positionality in relation to community engagements, and reframe our findings through Black Feminist Thought to propose design principles for health self-management technologies for communities with historically grounded medical mistrust.

Psychotherapy delivery relies on a negotiation between patient self-reports and clinical intuition. Growing evidence for technological support of psychotherapy suggests opportunities to aid the mediation of this tension. To explore this prospect, we designed a prototype of a clinical decision support system (CDSS) for treating veterans with post-traumatic stress disorder in a Prolonged Exposure (PE) therapy intensive outpatient program. We conducted a two-phase interview study to collect perspectives from practicing PE clinicians and former PE patients who are United States veterans. Our analysis distills opportunities for a CDSS (e.g., offering homework review at a glance, aiding patient conceptualization) and larger challenges related to context and deployment (e.g., navigating Veterans Affairs). By reframing our findings through three human-centered perspectives (distributed cognition, situated learning, infrastructural inversion), we highlight the complexities of designing a CDSS for psychotherapists in this context and offer theory-aligned design considerations.

Centering humans in explainable artificial intelligence (XAI) research has primarily focused on AI model development and highstake scenarios.However, as AI becomes increasingly integrated into everyday applications in often opaque ways, the need for explainability tailored to end-users has grown more urgent.To address this gap, we explore end-users' perspectives on embedding XAI into daily AI application scenarios.Our findings reveal that XAI is not naturally accepted by end-users in their daily lives.When users seek explanations, they envision XAI design that promotes contextualized understanding, empowers adoption and adaption to AI systems, and considers multistakeholders' values.We further discuss supporting users' agency in XAI non-use and alternatives to XAI for managing ambiguity in AI interactions.Additionally, we provide design implications for XAI design at personal and societal levels.These include understanding users through a computational rationality lens, adaptive design that coevolves with users, and advancing the "society-in-the-loop" vision with everyday XAI.

Suhas Bn, Yash Mahajan, Dominik O. Mattioli, Andrew M. Sherrill, Rosa I. Arriaga, Christopher Wiese, Saeed Abdullah. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

The advancement of AI systems for mental health support is hindered by limited access to therapeutic conversation data, particularly for trauma treatment. We present Thousand Voices of Trauma, a synthetic benchmark dataset of 3,000 therapy conversations based on Prolonged Exposure therapy protocols for Post-traumatic Stress Disorder (PTSD). The dataset comprises 500 unique cases, each explored through six conversational perspectives that mirror the progression of therapy from initial anxiety to peak distress to emotional processing. We incorporated diverse demographic profiles (ages 18-80, M=49.3, 49.4\% male, 44.4\% female, 6.2\% non-binary), 20 trauma types, and 10 trauma-related behaviors using deterministic and probabilistic generation methods. Analysis reveals realistic distributions of trauma types (witnessing violence 10.6\%, bullying 10.2\%) and symptoms (nightmares 23.4\%, substance abuse 20.8\%). Clinical experts validated the dataset's therapeutic fidelity, highlighting its emotional depth while suggesting refinements for greater authenticity. We also developed an emotional trajectory benchmark with standardized metrics for evaluating model responses. This privacy-preserving dataset addresses critical gaps in trauma-focused mental health data, offering a valuable resource for advancing both patient-facing applications and clinician training tools.

Role-play exercises are widely utilized for training across a variety of domains; however, they have many shortcomings, including low availability, resource intensity, and lack of diversity. Large language model-driven virtual agents offer a potential avenue to mitigate these limitations and offer lower-risk role-play. The implications, however, of shifting this human-human collaboration to human-agent collaboration are still largely unexplored. In this work we focus on the context of psychotherapy, as psychotherapists-in-training extensively engage in role-play exercises with peers and/or supervisors to practice the interpersonal and therapeutic skills required for effective treatment. We provide a case study of a realistic ''virtual patient'' system for mental health training, evaluated by trained psychotherapists in comparison to their previous experiences with both real role-play partners and real patients. Our qualitative, reflexive analysis generated three themes and thirteen subthemes regarding key interpersonal skills of psychotherapy, the utility of the system compared to traditional role-play techniques, and factors which impacted psychotherapist-perceived ''humanness'' of the virtual patient. Although psychotherapists were optimistic about the system's potential to bolster therapeutic skills, this utility was impacted by the extent to which the virtual patient was perceived as human-like. We leverage the Computers Are Social Actors framework to discuss human-virtual-patient collaboration for practicing rapport, and discuss challenges of prototyping novel human-AI systems for clinical contexts which require a high degree of unpredictability. We pull from the ''SEEK'' three-factor theory of anthropomorphism to stress the importance of adequately representing a variety of cultural communities within mental health AI systems, in alignment with decolonial computing.

The mental health crisis in the United States spotlights the need for more scalable training for mental health workers. While present-day AI systems have sparked hope for addressing this problem, we must not be too quick to incorporate or solely focus on technological advancements. We must ask empirical questions about how to ethically collaborate with and integrate autonomous AI into the clinical workplace. For these Human-Autonomy Teams (HATs), poised to make the leap into the mental health domain, special consideration around the construct of trust is in order. A reflexive look toward the multidisciplinary nature of such HAT projects illuminates the need for a deeper dive into varied stakeholder considerations of ethics and trust. In this paper, we investigate the impact of domain---and the ranges of expertise within domains---on ethics- and trust-related considerations for HATs in mental health. We outline our engagement of 23 participants in two speculative activities: design fiction and factorial survey vignettes. Grounded by a video storyboard prototype, AI- and Psychotherapy-domain experts and novices alike imagined TEAMMAIT, a prospective AI system for psychotherapy training. From our inductive analysis emerged 10 themes surrounding ethics, trust, and collaboration. Three can be seen as substantial barriers to trust and collaboration, where participants imagined they would not work with an AI teammate that didn't meet these ethical standards. Another five of the themes can be seen as interrelated, context-dependent, and variable factors of trust that impact collaboration with an AI teammate. The final two themes represent more explicit engagement with the prospective role of an AI teammate in psychotherapy training practices. We conclude by evaluating our findings through the lens of Mayer et al.'s Integrative Model of Organizational Trust to discuss the risks of HATs and adapt models of ability-, benevolence-, and integrity-based trust. These updates motivate implications for the design and integration of HATs in mental health work.

Informal caregivers perform an important role in taking care of family members with chronic disease. Informal caregivers' mental health can be negatively impacted by life-changing events (e.g., patients' diagnosis, care transitioning, etc.). This leads the caregiver to suffer from interpersonal and intrapersonal conflicts, causing a sense of disorientation and escalating malaise. In this study, we investigated informal caregivers' experiences of facing conflicts and life-changing events by qualitatively analyzing the data from online health communities. We categorized conflicts using a psychodynamic framework. We further looked at the interplay of life-changing events and conflicts and how this leads to caregivers' sense-making and decisions to mediate conflicts. We also found that online health communities provide support by helping caregivers interpret and navigate conflicts and raising awareness of the temporal resolution of life-changing events. We conclude this study by discussing designing online health communities to better support such practice.

Many people do not get enough physical activity. Establishing routines to incorporate physical activity into people’s daily lives is known to be effective, but many people struggle to establish and maintain routines when facing disruptions. In this paper, we build on prior self-experimentation work to assist people in establishing or improving physical activity routines using a framework we call “reflective iteration.” This framework encourages individuals to articulate, reflect upon, and iterate on high-level “strategies” that inform their day-to-day physical activity plans. We designed and deployed a mobile application, Planneregy, that implements this framework. Sixteen U.S. college students used the Planneregy app for 42 days to reflectively iterate on their weekly physical exercise routines. Based on an analysis of usage data and interviews, we found that the reflective iteration approach has the potential to help people find and maintain effective physical activity routines, even in the face of life changes and temporary disruptions.

Today, clinicians have limited visibility into the quality of homework exercises that occur outside of the clinical context; however, understanding patient performance in these exercises is essential for guiding patient-centered care. To address this, we present the Clinician Homework Review (CHR), a unique measure and interface that displays similarity ratings calculated using sensor-captured patient-generated data (sPGD; i.e. heart rate, phone usage, ambient noise, and physical activity) for therapeutic exercises outside of the clinical setting within the post-traumatic stress disorder (PTSD) treatment context. Through concept testing sessions with 10 clinicians, we examine how sPGD can be leveraged to measure and investigate what contributes to patient performance in a therapeutic exercise. We also share in-depth information regarding clinician interpretation and planned use of data displayed by CHR in clinical sessions with patients. We frame our results in the context of situated objectivity and propose the notion of "perceived reference weight," which describes the significance attributed to contextualized data. In doing so, we support clinical decision-making in PTSD therapy.

To resist government and corporate use of facial recognition to surveil users through their personal images, researchers have created privacy-enhancing image filters that use adversarial machine learning. These "subversive AI'' (SAI) image filters aim to defend users from facial recognition by distorting personal images in ways that are barely noticeable to humans but confusing to computer vision algorithms. SAI filters are limited, however, by the lack of rigorous user evaluation that assess their acceptability. We addressed this limitation by creating and validating a scale to measure user acceptance --- the SAIA-8. In a three-step process, we apply a mixed-methods approach that closely adhered to best practices for scale creation and validation in measurement theory. Initially, to understand the factors that influence user acceptance of SAI filter outputs, we interviewed 15 participants. Interviewees disliked extant SAI filter outputs because of a perceived lack of usefulness and conflicts with their desired self-presentation. Using insights and statements from the interviews, we generated 106 potential items for the scale. Employing an iterative refinement and validation process with 245 participants from Prolific, we arrived at the SAIA-8 scale: a set of eight items that capture user acceptability of privacy-enhancing perturbations to personal images, and that can aid in benchmarking and prioritizing user acceptability when developing and evaluating new SAI filters.

We introduce a new type of test, called a Turing Experiment (TE), for evaluating to what extent a given language model, such as GPT models, can simulate different aspects of human behavior. A TE can also reveal consistent distortions in a language model’s simulation of a specific human behavior. Unlike the Turing Test, which involves simulating a single arbitrary individual, a TE requires simulating a representative sample of participants in human subject research. We carry out TEs that attempt to replicate well-established findings from prior studies. We design a methodology for simulating TEs and illustrate its use to compare how well different language models are able to reproduce classic economic, psycholinguistic, and social psychology experiments: Ultimatum Game, Garden Path Sentences, Milgram Shock Experiment, and Wisdom of Crowds. In the first three TEs, the existing findings were replicated using recent models, while the last TE reveals a “hyper-accuracy distortion” present in some language models (including ChatGPT and GPT-4), which could affect downstream applications in education and the arts.

As enrollment increases, teaching assistants (TAs) need help prioritizing responding to students' posts in on-line forums. In a graduate-level Artificial Intelligence forum, three instructors rank the urgency of posts which is compared to the ratings of 13 course TAs; correlation with the instructors' scores have an r=0.55, with a TA inter-rater reliability of 35%. However, when TAs used a codebook containing seven dimensions created by the instructors to define urgency levels, correlation increased to r=0.73 and reliability to 53%. The instructor rankings are also compared to cognitive presence ratings from the Community of Inquiry framework. Cognitive presence correlates with urgency with r=-0.68. These results suggest that recommendation agents that prioritize posts based on cognitive presence or the urgency codebook may be beneficial for TAs.

The U.S. National Institute of Health (NIH) 3D Print Exchange is a public, open-source repository for 3D printable medical device designs with contributions from clinicians, expert-amateur makers, and people from industry and academia. In response to the COVID-19 pandemic, the NIH formed a collection to foster submissions of low-cost, locally manufacturable personal protective equipment (PPE) . We evaluated the 623 submissions in this collection to understand: what makers contributed, how they were made, who made them, and key characteristics of their designs. We found an immediate design convergence to manufacturing-focused remixes of a few initial designs affiliated with NIH partners and major for-profit groups. The NIH worked to review safe, effective designs but was overloaded by manufacturing-focused design adaptations. Our work contributes insights into: the outcomes of distributed, community-based medical making; the features that the community accepted as “safe” making; and how platforms can support regulated maker activities in high-risk domains.

Past research has demonstrated that accounts of trusted others can provide additional context into real world behavior relevant to clinical decision-making and patient engagement. Our research investigates the Social Sensing System, a concept which leverages trusted other feedback for veterans in therapy for PTSD. In our two phase study, we work with 10 clinicians to develop text-message queries and realistic scenarios to present to patients and trusted others. We then present the results in the form of a storyboard to 10 veterans with PTSD and 10 trusted others and gather feedback via semi-structured interview and survey. We find that while trusted other feedback may provide a unique and useful perspective, key design features and considerations of underlying relationships must be considered. We present our findings and utilize the mechanisms and conditions framework to assess the power dynamics of systems such as social sensing in the mental health realm.

Meditation has become a popular option to manage stress. Though studies examine technologies to assist in meditation, few explore how technology supports development of such skills for independent practice. From a two-phase mixed-methods study, we contribute learner-centered insights from 36 participants in a virtual reality environment designed to teach meditation skills to novices. In Phase I, we gathered affective and behavioral learner needs from 21 meditation novices, experts, and instructors to synthesize insights for learning. We then designed ZenVR: an interactive system to deliver an eight-lesson meditation curriculum to support learners’ progress. In Phase II, we conducted a 6-week longitudinal lab-based evaluation with 15 novice meditation learners. We found statistically significant improvements in mindfulness and self-reported meditation ability. Their insights from a self-managed practice, two weeks after the study ended, offered opportunities to understand how technology can be designed to offer progressive support without creating dependence in technology-mediated meditation practice.

Embedding a post recommendation system in online course forums improves students' ability to find relevant course content. Yet, there is limited research quantifying how these interventions impact forum interactions and students' class performance. We randomly divide an on-line class in a Masters degree-seeking program into control and experimental sections. Midway through the class, for the experimental section, we introduce an information retrieval system that suggests relevant posts to students while they compose their new posts. The average number of initial posts in the discussion forum dropped by 55% for the experimental group compared to 21% in the control group. In post-hoc analysis, we classify students as having an active or passive (i.e., mostly lurking) forum participation style. Posts per student per assignment by passive participants in the experimental group decreased by 15%, while passive participants' posts in the control group increased by 200%. The number of answers given by instructors in the experimental group decreased twice as much as the control group after intervention, though the difference was not statistically significant. The reduction of posts was not associated with a decrease in academic performance (grades) for the experimental group. This line of research convinced the popular forum Piazza to implement a post recommendation system similar to that used in these experiments.

We present an ethnographic study of a maker community that conducted safety-driven medical making to deliver over 80,000 devices for use at medical facilities in response to the COVID-19 pandemic. To achieve this, the community had to balance their clinical value of safety with the maker value of broadened participation in design and production. We analyse their struggles and achievement through the artifacts they produced and the labors of key facilitators between diverse community members. Based on this analysis we provide insights into how medical maker communities, which are necessarily risk-averse and safety-oriented, can still support makers' grassroots efforts to care for their communities. Based on these findings, we recommend that design tools enable adaptation to a wider set of domains, rather than exclusively presenting information relevant to manufacturing. Further, we call for future work on the portability of designs across different types of printers which could enable broader participation in future maker efforts at this scale.

Making medical devices in healthcare settings engages practitioners in organizational innovation. Nurses improvise physical workarounds at the bedside in response to patient needs. Yet nurse-led problem-solving is rarely centralized in an emerging innovation ecosystem through medical making. We interviewed medical makers in six healthcare makerspaces to understand factors for nurse inclusion in the medical making ecosystem. Findings from 16 multi-stakeholder interviews with 6 facilitators and 10 nurses in the USA, reveal insights into nurse-led problem-solving with and without the use of physical prototyping (making) in formal innovation spaces with maker technologies. We report how a nurse's capacity for making is practice-driven to address in-patient discomfort, repair their own practice, and update standardized workflows. Most nurses iterate on low-tech solutions facing barriers to formal collaboration when they attempt to scale up. Their technical capabilities extend from innovation-centered resources (e.g., lab spaces, technologies), often with complete reliance on facilitators who have limited authority in the medical system. We contribute to themes around practice-based innovation, participation in technology design, and articulation work for collaborative innovation. From nurse makers' experiences, we discuss how nurse participation can be supported in healthcare technology design.

Veterans are a unique marginalized group facing multiple vulnerabilities. Current assessments of veteran needs and support largely come from first-person accounts guided by researchers' prompts. Social media platforms not only enable veterans to connect with each other, but also to self-disclose experiences and seek support. This paper addresses the gap in our understanding of veteran needs and their own support dynamics by examining self-initiated and ecologically-valid self-expressions. In particular, we adopt the Veteran Critical Theory (VCT) to conduct a computational study on the Reddit community of veterans. Using topic modeling, we find veteran-friendly gestures with good intentions might not be appreciated in the subreddit. By employing transfer learning methodologies, we find this community has more informational and emotional support behaviors than general online communities and a higher prevalence of informational support than emotional support. Lastly, an examination of support dynamics reveals some contrasts to previous scholarship in military culture and social media. We discover that positive language and author platform tenure have negative relations with posts receiving replies and replies getting votes, and that replies reflecting personal disclosures tend to get more votes. Through the lens of VCT, we discuss how online communities can help uncover veterans' needs and provide more effective social support.

Medical making intersects opposing value systems of a medical “do no harm” ethos and makers’ drive to innovate. Since March 2020, online maker communities have formed to design, manufacture, and distribute personal protective equipment (PPE) and other medical devices needed to fight the COVID-19 pandemic. We present a participant observation study of 14 maker communities, which have developed differing driving principles for efforts with varied access to interdisciplinary expertise on online platforms that mutually shape collective action. Over time, these communities unintentionally align towards action-oriented or regulated practices because they often lack higher level insight and agency in choosing communication platforms. In response, we recommend: regulatory bodies to build coalitions with makers, online platforms to give communities more control over the presentation of information, and repositories to balance needs to distribute information while limiting the spread of misinformation.

Unprecedented maker efforts arose in response to COVID-19 medical supply gaps worldwide. Makers in the U.S., participated in peer-production activities to manufacture personal protective equipment (PPE). Whereas, medical makers, who innovate exclusively for points of care, pivoted towards safer, reliable PPE. What were their efforts to pivot medical maker infrastructure towards reliable production of safe equipment at higher volumes? We interviewed 13 medical makers as links between institutions, maker communities, and wider regional industry networks. These medical makers organized stopgap manufacturing in institutional spaces to resolve acute shortages (March–May) and chronic shortages (May–July). They act as intermediaries in efforts to prototype and produce devices under regulatory, material, and human constraints of a pandemic. We re-frame their making efforts as repair work to offer an alternate critical view of optimism around making for crisis. We contribute an understanding of these efforts to inform infrastructure design for making with purpose and safety leading to opportunities for community production of safe devices at scale.

Airflow energy harvesting has attracted much attention in the community of energy harvesting, but the efforts to make it accessible for individual users have been limited. In this paper, we address this issue and demonstrate a comprehensive toolkit, Exergy, which can help and support novice users to simulate, design, and manufacture an airflow energy harvester for vehicles.

Post-traumatic stress disorder (PTSD) disproportionately affects United States veterans, yet they may be reluctant to seek or engage in care. We interview 21 participants, including veterans with PTSD, clinicians who treat veterans and friends and family that support veterans through mental health ordeals. We investigate the military identity these veterans share. We explore how this may add to their reluctance in care-seeking behaviors. We also explore the roles of human and non-human intermediaries in ecologies of care and the potential for enhancing patient empowerment in current clinical treatment contexts. We discuss how military culture can be utilized in clinical care, how multiple perspectives can be leveraged to create a more holistic view of the patient, and finally, how veterans can be empowered during treatment. We conclude with recommendations for the design of sociotechnical systems that prioritize the above in support of the mental well-being of veterans with PTSD.

Adolescents with chronic conditions must work with family caregivers to manage their illness experiences. To explore how technology can support collaborative documentation of these experiences, we designed and distributed a paper diary probe kit in a two-week field deployment with 12 adolescent-parent dyads (24 participants). Three insights emerged from the study that highlight how technology can support shared illness management: 1) provide scaffolds to recognize physical and emotional experiences in the context of daily activities; 2) help families reconstruct patient experiences; and 3) adapt to individual preferences for capturing, representing and sharing experiences. We discuss opportunities for HCI research that follow from these findings and conclude by reflecting on the benefits and limitations of using diary probes with adolescent patients and their parental caregivers.

As enrollment numbers in online courses increase, students, instructors, and teaching assistants have difficulty finding needed information in online forums because of the number of posts, resulting in duplicate posts that exacerbate the problem. We introduce PARQR, a recommendation tool that suggests relevant contributions as participants compose their posts. We investigate the use of PARQR in five online degree-seeking courses. We survey 74 students and interview five teaching assistants to understand their experience with online forums and PARQR. We compare the differences between using and not using PARQR for an online course assignment. PARQR users found the tool to be useful for navigating online forums, and PARQR was effective in reducing the number of posts (0.291 vs. 0.506 posts per active student) and duplicate posts (17.8% vs. 25.6%) in an online course. These results suggest that PARQR makes on-line forums more efficient for users to find needed information.

We present an initial qualitative exploration of Twitter data on autism spectrum disorders (ASDs) and acquired immunodeficiency syndrome (AIDS) from India and the United States, conducted with the goal of learning more about the nature of existing online awareness around ASDs in India. We collected tweets from both locations during and around World Autism Awareness Day and World AIDS Day. We then qualitatively analyzed these tweets, finding differences in how each country talks about stigmatized health issues and how Indian users talk about ASDs. We use these findings to discuss potential avenues for research on supporting and extending existing levels of autism awareness in India.

We introduce PARQR, a tool for online education forums that reduces duplicate posts by 40% in a degree seeking online masters program at a top university. Instead of performing a standard keyword search, PARQR monitors questions as students compose them and continuously suggests relevant posts. In testing, PARQR correctly recommends a relevant post, if one exists, 73.5% of the time. We discuss PARQR's design, initial experimental results comparing different semesters with and without PARQR, and interviews we conducted with teaching instructors regarding their experience with PARQR.

Maker culture is on the rise in healthcare with the adoption of consumer-grade fabrication technologies. However, little is known about the activities and resources involved in prototyping medical devices to improve patient care. In this paper, we refer to such activity asmedical making to report findings based on a qualitative study of stakeholder engagement in physical prototyping (making) experiences. We examine perspectives from diverse stakeholders including clinicians, engineers, administrators, and medical researchers. Through 18 semi-structured interviews with medical-makers in the US and Canada, we analyze making activity in medical settings. We find that medical makers share strategies to address risks, adopt labor roles, and acquire resources within traditional medical practice. Our findings outline how medical-makers mitigate risks for patient safety, collaborate with local and global stakeholder networks, and overcome constraints of co-location and material practices. We recommend a clinician-aided software system, partially-open repositories, and a collaborative skill-sharing social network to extend their strategies in support of medical making.

Since 2014, Venezuela has experienced severe economic crisis, including scarcity of basic necessities such as food and medicine. This has resulted in over-priced goods, scams, and other forms of economic abuse. We present an investigation of Venezuelans' efforts to form an alternative, Solidarity Economy (SE) through Facebook Groups. In these groups, individuals can barter for items at fair prices. We highlight group practices and design features of Facebook Groups which support solidarity or anti-solidarity behaviors. We conclude by leveraging design principles for online communities presented by Kollock to present strategies to design more effective SEs in environments of low trust.

Most smartphones today have a rich set of sensors that could be used to infer input (e.g., accelerometer, gyroscope, microphone); however, the primary mode of interaction is still limited to the front-facing touchscreen and several physical buttons on the case. To investigate the potential opportunities for interactions supported by built-in sensors, we present the implementation and evaluation of BeyondTouch, a family of interactions to extend and enrich the input experience of a smartphone. Using only existing sensing capabilities on a commodity smartphone, we offer the user a wide variety of additional inputs on the case and the surface adjacent to the smartphone. Although most of these interactions are implemented with machine learning methods, compact and robust rule-based detection methods can also be applied for recognizing some interactions by analyzing physical characteristics of tapping events on the phone. This article is an extended version of Zhang et al. [2015], which solely covered gestures implemented by machine learning methods. We extended our previous work by adding gestures implemented with rule-based methods, which works well with different users across devices without collecting any training data. We outline the implementation of both machine learning and rule-based methods for these interaction techniques and demonstrate empirical evidence of their effectiveness and usability. We also discuss the practicality of BeyondTouch for a variety of application scenarios and compare the two different implementation methods.

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.

While most smartphones today have a rich set of sensors that could be used to infer input (.e.g., accelerometer, gyroscope, microphone), the primary mode of interaction is still limited to the front-facing touchscreen and several physical buttons on the case. To investigate the potential opportunities for interactions supported by built-in sensors, we present the implementation and evaluation of BeyondTouch, a family of interactions to extend and enrich the input experience of a smartphone. Using only existing sensing capabilities on a commodity smartphone, we offer the user a wide variety of additional tapping and sliding inputs on the case of and the surface adjacent to the smartphone. We outline the implementation of these interaction techniques and demonstrate empirical evidence of their effectiveness and usability. We also discuss the practicality of BeyondTouch for a variety of application scenarios.

Humans learn categories of complex objects quickly and from a few examples. Random projection has been suggested as a means to learn and categorize efficiently. We investigate how random projection affects categorization by humans and by very simple neural networks on the same stimuli and categorization tasks, and how this relates to the robustness of categories. We find that (1) drastic reduction in stimulus complexity via random projection does not degrade performance in categorization tasks by either humans or simple neural networks, (2) human accuracy and neural network accuracy are remarkably correlated, even at the level of individual stimuli, and (3) the performance of both is strongly indicated by a natural notion of category robustness.

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.

The goal of this study was to extend and replicate an SMS health intervention for pediatric asthma patients. This intervention was designed using the Health Belief Model (HBM). Thirty patients were randomly assigned to one of three conditions. In the Knowledge condition patients were queried about their asthma knowledge every other day. In the Knowledge and Symptoms condition patients received a daily text message. They were queried about their symptoms and knowledge of asthma on alternate days. The Control group received no texts. Our main finding is that daily text messages lead to improved health outcomes.

The social world that most of us navigate effortlessly can prove to be a perplexing and disconcerting place for individuals with autism. Interactive tools to teach social skills that are personalized to the individual's needs show promise, but it is challenging to author them. We describe the design, development, and preliminary evaluation of an approach to using human computation that enables the creation of models of complex and interesting social scenarios, possible obstacles that may arise in those scenarios, and potential solutions to those obstacles. Our preliminary evaluation of the models confirms that these models have the potential to help an author create a social skills instructional module.

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.

Individuals with Autism Spectrum Disorder (ASD) often engage in stereotypical behaviors. In some individuals these behaviors occur with very high frequency and can be disruptive and at times self-injurious. We propose a system that can tacitly collect contextual data related to the individual's physiological state and their external environment, and map it to occurrences of stereotypies. A user study was conducted with children with ASD, parents, and caregivers to explore and validate this concept. A prototype of the system, developed through participatory design, was used in the study as a probe to elicit the information needs of these stakeholders, and provide a better understanding of the nuances involved in supporting those needs. Here we present the findings of this study, and four design recommendations; promoting ecological integration, addressing privacy concerns, supporting inference, and enabling customization.

Parents of young children often want to keep a variety of records on their children's early years, for the purposes of preservation of memories or at the request of their pediatrician. However, time constraints, motivation, and forgetfulness may hinder their ability to keep consistent records. We developed a system, Baby Steps, which is designed to improve the record-keeping process. In this paper, we present the results of a 3-month deployment study of this technology with 8 families and their pediatricians. The study showed that when compared to a control condition, experimental design features of Baby Steps encouraged parents to more frequently collect and review records, provided higher confidence in reporting, and improved parent-pediatrician communication.

Mobile technologies, due to their ubiquitous nature, play an important role in supporting health care. However, it is not easy to design useful integrated mobile services without a systematic understanding of users, and this is especially true for children. Therefore, we propose a new theoretical perspective for generating design concepts in the early stage of the design process. Our ecological model is based on Ecological Systems Theory which approaches development in terms of the child's relationships and environmental context. It leverages the fact that mobile technologies are deeply involved with users' circumstances. We argue that the ecological model can provide a heuristic to help researchers understand users' needs in context and generate concepts logically and creatively. Here we explore pediatric asthma management as a case study for this model. Finally, five promising mobile technology concepts are provided as examples for further development of mobile technologies related to pediatric asthma management.

Social learning in robotics has largely focused on imitation learning. Here we take a broader view and are interested in the multifaceted ways that a social partner can influence the learning process. We implement four social learning mechanisms on a robot: stimulus enhancement, emulation, mimicking, and imitation, and illustrate the computational benefits of each. In particular, we illustrate that some strategies are about directing the attention of the learner to objects and others are about actions. Taken together these strategies form a rich repertoire allowing social learners to use a social partner to greatly impact their learning process. We demonstrate these results in simulation and with physical robot `playmates'.

From birth through age five, children undergo rapid development and learn skills that will influence them their entire lives. Regular visits to the pediatrician and detailed record-keeping can ensure that children are progressing and can identify early warning signs of developmental delay or disability. However, new parents are often overwhelmed with new responsibilities, and we believe there is an opportunity for computing technology to assist in this process. In this paper, we present a qualitative study aimed at uncovering some specific needs for record-keeping and analysis for new parents and their network of caregivers. Through interviews and focus groups, we have confirmed assumptions about the rationales parents have and the functions required for using technology for record-keeping. We also identify new themes, potential prototypes, and design guidelines for this domain.

We study the phenomenon of cognitive learning from an algorithmic standpoint. How does the brain effectively learn concepts from a small number of examples despite the fact that each example contains a huge amount of information? We provide a novel analysis for a model of robust concept learning (closely related to "margin classifiers"), and show that a relatively small number of examples are sufficient to learn rich concept classes (including threshold functions, Boolean formulae and polynomial surfaces). As a result, we obtain simple intuitive proofs for the generalization bounds of Support Vector Machines. In addition, the new algorithm has several advantages-they are faster conceptually simpler and highly resistant to noise. For example, a robust half-space can be PAC-learned in linear time using only a constant number of training examples, regardless of the number of attributes. A general (algorithmic) consequence of the model, that "more robust concepts are easier to learn", is supported by a multitude of psychological studies.