Stories about overcoming personal struggles can effectively illustrate the application of psychological theories in real life, yet they may fail to resonate with individuals' experiences. In this work, we employ large language models (LLMs) to create tailored narratives that acknowledge and address unique challenging thoughts and situations faced by individuals. Our study, involving 346 young adults across two settings, demonstrates that personalized LLM-enhanced stories were perceived to be better than human-written ones in conveying key takeaways, promoting reflection, and reducing belief in negative thoughts. These stories were not only seen as more relatable but also similarly authentic to human-written ones, highlighting the potential of LLMs in helping young adults manage their struggles. The findings of this work provide crucial design considerations for future narrative-based digital mental health interventions, such as the need to maintain relatability without veering into implausibility and refining the wording and tone of AI-enhanced content.

Informed by the previous HCI and CSCW scholarship on residual mobility -- a concept that transcends mere geographical relocation to encompass socio-cultural and communal disruptions -- this study probes the unique religious and spiritual challenges faced by the Hindu migrants from the Indian subcontinent in Canada. Through interviews with 20 participants, we investigate the role of technology in navigating a diverse religious landscape in professional environments, coping with changing religious materiality, and passing down traditions to the next generation. Our work identifies the community's proactive use of social media and videoconferencing for religious festivals and connection with their religious community. The findings raise several implications for CSCW research on supporting residual mobility experiences of the Hindu migrants, including effective organization of religious event information, virtual support for material aspects of religious rituals, and fostering online environments that enable pluralistic spiritual engagement.

Challenges in engagement with digital mental health (DMH) tools are commonly addressed through technical enhancements and algorithmic interventions. This paper shifts the focus towards the role of users' broader social context as a significant factor in engagement. Through an eight-week text messaging program aimed at enhancing psychological wellbeing, we recruited 20 participants to help us identify situational engagement disruptors (SEDs), including personal responsibilities, professional obligations, and unexpected health issues. In follow-up design workshops with 25 participants, we explored potential solutions that address such SEDs: prioritizing self-care through structured goal-setting, alternative framings for disengagement, and utilization of external resources. Our findings challenge conventional perspectives on engagement and offer actionable design implications for future DMH tools.

Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these interventions can be effective, real-world experimental testing can further enhance their design and impact. Adaptive experimentation, utilizing algorithms like Thompson Sampling for (contextual) multi-armed bandit (MAB) problems, can lead to continuous improvement and personalization. However, it remains unclear when these algorithms can simultaneously increase user experience rewards and facilitate appropriate data collection for social-behavioral scientists to analyze with sufficient statistical confidence. Although a growing body of research addresses the practical and statistical aspects of MAB and other adaptive algorithms, further exploration is needed to assess their impact across diverse real-world contexts. This paper presents a software system developed over two years that allows text-messaging intervention components to be adapted using bandit and other algorithms while collecting data for side-by-side comparison with traditional uniform random non-adaptive experiments. We evaluate the system by deploying a text-message-based DMH intervention to 1100 users, recruited through a large mental health non-profit organization, and share the path forward for deploying this system at scale. This system not only enables applications in mental health but could also serve as a model testbed for adaptive experimentation algorithms in other domains.

Traditional interventions for academic procrastination often fail to capture the nuanced, individual-specific factors that underlie them. Large language models (LLMs) hold immense potential for addressing this gap by permitting open-ended inputs, including the ability to customize interventions to individuals' unique needs. However, user expectations and potential limitations of LLMs in this context remain underexplored. To address this, we conducted interviews and focus group discussions with 15 university students and 6 experts, during which a technology probe for generating personalized advice for managing procrastination was presented. Our results highlight the necessity for LLMs to provide structured, deadline-oriented steps and enhanced user support mechanisms. Additionally, our results surface the need for an adaptive approach to questioning based on factors like busyness. These findings offer crucial design implications for the development of LLM-based tools for managing procrastination while cautioning the use of LLMs for therapeutic guidance.

Regular practice of gratitude has the potential to enhance psychological wellbeing and foster stronger social connections among young adults. However, there is a lack of research investigating user needs and expectations regarding gratitude-promoting applications. To address this gap, we employed a user-centered design approach to develop a mobile application that facilitates gratitude practice. Our formative study involved 20 participants who utilized an existing application, providing insights into their preferences for organizing expressions of gratitude and the significance of prompts for reflection and mood labeling after working hours. Building on these findings, we conducted a deployment study with 26 participants using our custom-designed application, which confirmed the positive impact of structured options to guide gratitude practice and highlighted the advantages of passive engagement with the application during busy periods. Our study contributes to the field by identifying key design considerations for promoting gratitude among young adults.

Without a nuanced understanding of users' perspectives and contexts, text messaging tools for supporting psychological wellbeing risk delivering interventions that are mismatched to users' dynamic needs. We investigated the contextual factors that influence young adults' day-to-day experiences when interacting with such tools. Through interviews and focus group discussions with 36 participants, we identified that people's daily schedules and affective states were dominant factors that shape their messaging preferences. We developed two messaging dialogues centered around these factors, which we deployed to 42 participants to test and extend our initial understanding of users' needs. Across both studies, participants provided diverse opinions of how they could be best supported by messages, particularly around when to engage users in more passive versus active ways. They also proposed ways of adjusting message length and content during periods of low mood. Our findings provide design implications and opportunities for context-aware mental health management systems.

Research has revealed the positive effects of reflection on helping students manage their psychological well-being. Hence, we are motivated to investigate the design space of how we can communicate the values of doing reflections. We limit our work to voice reflection because of its inclusivity and effectiveness. We designed a pilot survey on Qualtrics to collect qualitative responses and integrated voice recording features from Phonic.ai. Participants were presented with 4 sample voice recordings related to college students' daily lives and asked to complete a simple voice reflection activity based on the samples they listened to. Then, they were asked to provide feedback on these examples. We deployed the survey on Amazon Mechanical Turk (MTurk) and collected 221 effective responses. By conducting thematic analysis, we found several insightful themes: emotional speech, diverse content, and clear structure are important elements to include, while examples should avoid being overly scripted. The findings suggest ways to design effective examples to engage students in voice reflections and open up the possibilities for further investigations into the design features of voice reflection platforms.

One-way text messaging services have the potential to support psychological wellbeing at scale without conversational partners. However, there is limited understanding of what challenges are faced in mapping interactions typically done face-to-face or via online interactive resources into a text messaging medium. To explore this design space, we developed seven text messages inspired by cognitive behavioral therapy. We then conducted an open-ended survey with 788 undergraduate students and follow-up interviews with students and clinical psychologists to understand how people perceived these messages and the factors they anticipated would drive their engagement. We leveraged those insights to revise our messages, after which we deployed our messages via a technology probe to 11 students for two weeks. Through our mixed-methods approach, we highlight challenges and opportunities for future text messaging services, such as the importance of concrete suggestions and flexible pre-scheduled message timing.

Young adults have high rates of mental health conditions, but most do not want or cannot access formal treatment. We therefore recruited young adults with depression or anxiety symptoms to co-design a digital tool for self-managing their mental health concerns. Through study activities-consisting of an online discussion group and a series of design workshops-participants highlighted the importance of easy-to-use digital tools that allow them to exercise independence in their self-management. They described ways that an automated messaging tool might benefit them by: facilitating experimentation with diverse concepts and experiences; allowing variable depth of engagement based on preferences, availability, and mood; and collecting feedback to personalize the tool. While participants wanted to feel supported by an automated tool, they cautioned against incorporating an overtly human-like motivational tone. We discuss ways to apply these findings to improve the design and dissemination of digital mental health tools for young adults.

Efficacy of the traditional individualistic viewpoint towards mental health is questionable, particularly in the context of the Global South countries. Past works in psychology have rarely incorporated people’s background and surroundings in dealing with mental health. In contrast, we argue that many mental health problems of people living in the Global South countries are rooted in underdevelopment. Analyzing 1,000 call data of a telephone crisis helpline in Bangladesh, we found that 91% of the cases where callers shared their problems were impacted by failure to meet one of the 17 sustainable developed goals proposed by United Nations. Challenges towards ensuring gender equality, good health and well-being, quality education, and decent work and economic growth among a few others were causing mental health problems among callers. Our findings demonstrate the need to revise support behavior models followed by helplines in the Global South and advocate for a shift in viewing mental health problems from an individualistic to a social approach.

Adopting new psychological strategies to improve mental wellness can be challenging since people are often unable to anticipate how new habits are applicable to their circumstances. Narrative-based interventions have the potential to alleviate this burden by illustrating psychological principles in an applied context. In this work, we explore how stories can be delivered via the ubiquitous and scalable medium of text messaging. Through formative work consisting of interviews and focus group discussions with 15 participants, we identified desirable elements of stories about mental health, including authenticity and relatability. We then deployed story-based text messages to 42 participants to explore challenges regarding both the stories' content (e.g., specific versus generalized) and format (e.g., story length). We observed that our stories helped participants reflect on and identify flaws in their thinking patterns. Our findings highlight design implications and opportunities for mental wellness interventions that utilize stories in text messaging services.

Women in the global south often seek justice to their online harassment through unveiling the harassers and the screenshots of their sent harassment texts and visual contents before the relevant authorities. Nevertheless, such evidence is often challenged for their authenticity. Our survey (n=91) and interview (n=43) with Bangladeshi online gender harassment victims revealed the depth of the problem, and we set design goals to collect evidence from Facebook Messenger with ensured authenticity. Building on the ‘shame-based model’ of gender justice [12], we designed ‘Unmochon’, a tool that captures authentic evidence and shares with victims’ intended group. Our user-study (n=48) revealed that diminishing authenticity problem may still leave the victim and online gender justice entangled with mob-sentiment, hegemonic legal consciousness, and several privacy aspects. Our findings open up a new discussion on how HCI-design should address online gender justice in such a complex social setting.

How can educational platforms be instrumented to accelerate the use of research to improve students' experiences? We show how modular components of any educational interface - e.g. explanations, homework problems, even emails - can be implemented using the novel MOOClet software architecture. Researchers and instructors can use these augmented MOOClet components for: (1) Iterative Cycles of Randomized Experiments that test alternative versions of course content; (2) Data-Driven Improvement using adaptive experiments that rapidly use data to give better versions of content to future students, on the order of days rather than months. A MOOClet supports both manual and automated improvement using reinforcement learning; (3) Personalization by delivering alternative versions as a function of data about a student's characteristics or subgroup, using both expert-authored rules and data mining algorithms. We provide an open-source web service for implementing MOOClets (www.mooclet.org) that has been used with thousands of students. The MOOClet framework provides an ecosystem that transforms online course components into collaborative micro-laboratories, where instructors, experimental researchers, and data mining/machine learning researchers can engage in perpetual cycles of experimentation, improvement, and personalization.

This work is meant to provide insights towards state-of-the-art Application Programming Interfaces (APIs) commercially available today for recognizing emotions from video footprint of facial expressions. We analyze and compare performance of four such integrable commercial APIs using standard Extended Cohn-Kanade Dataset (CK+) containing over 10, 000 images with facial emotions and some randomly collected real life images. We also discuss issues to understand their limitations and adaptation as well as enhancement strategies. For performance analysis, we introduce the performance metric moving average to find the primary expression in the displayed emotion of a video sequence. Finally, using the popular valence-arousal dimensions, we show how to (lexicographically) order six basic emotions anger, happiness, sadness, surprise, fear and disgust. By providing neutral performance comparison, we hope to fill the existing notable gap between researchers, solution providers and application developers working on emotion based user modeling.