The aftermath of industry-wide mass layoffs has led to an increasingly discontent and disillusioned tech workforce. Our empirical study with 29 laid off tech workers presents critical reflections on tech work and the tech industry in the aftermath of mass layoffs. Through weekly creative reflection activities over 5 weeks as well as focus groups, we find that tech workers experience alienation and unfulfillment with their work. Tech workers expressed conflicted emotions in assessing their attachment to tech work as a site of labor, oscillating between discomfort with the current status of the tech industry and lack of agency in choosing alternatives. We argue that tech workers are embroiled in cruelly optimistic relationships with tech work, and trace the implications of this on conflicting sociotechnical imaginaries shaping tech work, affective attachments in the tech industry, and tech worker resistance and organizing.
Client-Service Representatives (CSRs) are vital to organizations. Frequent interactions with disgruntled clients, however, disrupt their mental well-being. To help CSRs regulate their emotions while interacting with uncivil clients, we designed Care-Pilot, an LLM-powered assistant, and evaluated its efficacy, perception, and use. Our comparative analyses between 665 human and Care-Pilot-generated support messages highlight Care-Pilot’s ability to adapt to and demonstrate empathy in various incivility incidents. Additionally, 143 CSRs assessed Care-Pilot’s empathy as more sincere and actionable than human messages. Finally, we interviewed 20 CSRs who interacted with Care-Pilot in a simulation exercise. They reported that Care-Pilot helped them avoid negative thinking, recenter thoughts, and humanize clients; showing potential for bridging gaps in coworker support. Yet, they also noted deployment challenges and emphasized the indispensability of shared experiences. We discuss future designs and societal implications of AI-mediated emotional labor, underscoring empathy as a critical function for AI assistants for worker mental health.
Prosocial behaviors, such as helping others, are well-known to enhance human well-being. While there is a growing trend of humans helping AI agents, it remains unclear whether the well-being benefits of helping others extend to interactions with non-human entities. To address this, we conducted an experiment (N = 295) to explore how helping AI agents impacts human well-being, especially when the agents fulfill human basic psychological needs—relatedness, competence, and autonomy—during the interaction. Our findings showed that helping AI agents reduced participants' feelings of loneliness. When AI met participants’ needs for competence and autonomy during the helping process, there was a further decrease in loneliness and an increase in positive affect. However, when AI did not meet participants' need for relatedness, participants experienced an increase in positive affect. We discuss the implications of these findings for understanding how AI can support human well-being.
Parental emotion coaching approaches that advocate for noticing and validating child emotions can greatly impact children's regulatory abilities. However, in daily life, parents often struggle to apply emotion coaching strategies that they access through parenting programmes or online help, suggesting a need for in situ support. This paper explores a potential new avenue for providing such support. We undertook conceptual work to develop a set of emotion-focused reflective questions that could increase parents’ attention to child emotions and delivered these as daily ecological momentary assessments (EMAs). We investigated the perceived impact of the approach through a 2-week online trial (n=33) and then co-designed child-facing component with parents through a 4-week asynchronous remote community study (n=15). Our paper contributes (1) conceptual insights on designing a potential novel intervention approach, (2) empirical insights on its acceptability and perceived impacts for parents, and (3) design implications for applying the approach to wider psychological constructs.
This paper characterizes the mental health technology “kits” of individuals managing depression: the specific technologies on their digital devices and physical items in their environments that people turn to as part of their mental health management. We interviewed 28 individuals living across the United States who use bundles of connected tools for both individual and collaborative mental health activities. We contribute to the HCI community by conceptualizing these tool assemblages that people managing depression have constructed over time. We detail categories of tools, describe kit characteristics (intentional, adaptable, available), and present participant ideas for future mental health support technologies. We then discuss what a mental health technology kit perspective means for researchers and designers and describe design principles (building within current toolkits; creating new tools from current self-management strategies; and identifying gaps in people’s current kits) to support depression self-management across an evolving set of tools.
Prosociality has been well-documented to positively impact mental, social, and physical well-being. However, existing studies of interventions for promoting prosociality have limitations such as small sample sizes or unclear benchmarks. To address this gap, we
conducted a global-scale well-being intervention deployment study, BIGJOY, with more than 18,000 participants from 172 countries and regions. The week-long BIGJOY intervention consists of seven daily micro-acts (i.e., brief actions that require minimal effort), each adapted from validated positive psychology interventions. The analyses of large-scale intervention data reveal unique insights into the impact of well-being micro-acts across diverse populations, patterns of responses, effectiveness of specific micro-acts and their nuanced impacts across different populations, linkages between improvements in prosociality and in well-being, as well as the potential for machine learning to predict changes in prosociality. This study offers valuable insights into a set of design guidelines for future well-being and prosociality interventions. We envision our
work as a stepping stone towards future large-scale prosociality interventions that foster a more unified and compassionate world.
In Affective computing, recognizing users' emotions accurately is the basis of affective human–computer interaction. Understanding users' interoception contributes to a better understanding of individually different emotional abilities, which is essential for achieving inter-individually accurate emotion estimation. However, existing interoception measurement methods, such as the heart rate discrimination task, have several limitations, including their dependence on a well-controlled laboratory environment and precision apparatus, making monitoring users' interoception challenging. This study aims to determine other forms of data that can explain users' interoceptive or similar states in their real-world lives and propose a novel hypothetical concept "cyberoception," a new sense (1) which has properties similar to interoception in terms of the correlation with other emotion-related abilities, and (2) which can be measured only by the sensors embedded inside commodity smartphone devices in users' daily lives. Results from a 10-day-long in-lab/in-the-wild hybrid experiment reveal a specific cyberoception type "Turn On." (users' subjective sensory perception about the frequency of turning-on behavior on their smartphones)