While mental health apps can help to promote adolescents’ mental health, prevent mental health problems, and reduce symptoms, maintaining sufficient user engagement with these apps remains challenging. This is often caused by a mismatch between the needs and preferences of adolescents and what the apps offer. Therefore, we need a better understanding of (i) adolescents’ needs and preferences and (ii) potential differences based on user characteristics. To this end, we qualitatively and quantitatively analyzed a dataset describing the user experience of 1312 Dutch adolescents (12–25 years) from the general population after they interacted for several weeks with a gamified mHealth app (the Grow It! app) that aims to promote momentary emotional awareness, reflection, and adaptive coping. A total of 4833 free-text survey responses spanning five user experience survey questions were analyzed using an inductive and iterative coding process, while accounting for intercoder reliability. We used (i) a thematic analysis to identify adolescents’ needs and preferences related to the app, and (ii) an exploratory quantitative analysis of the subthemes to investigate potential differences in which needs and preferences were mentioned by adolescents based on demographics. Through our thematic analysis, we identified three overarching themes related to the app’s design: usability , psychological impact , and meaningful interactive features . Furthermore, we identified two overarching themes that related to the adolescents’ motivation to use the app: intrinsic (de)motivators , and social–environmental factors impacting usage . Each of these themes consisted of four subthemes. Our exploratory statistical analysis shed light on several differences in how frequently these subthemes were mentioned based on age, sex, and educational level. By synthesizing our insights, we identify five design implications that can help tailor future mHealth apps to adolescents’ needs and preferences. These include concrete suggestions to personalize self-monitoring, include actionable insights, align content with personal needs, implement meaningful interactive features (e.g., competitions, gamification, and social communication), and make apps appealing to the entire target group.

The proliferation and pervasive use of artificial intelligence (AI) pose significant challenges to our democracies. In particular, AI leverages microtargeting political campaigns by constructing detailed user profiles and inferring people’s individual susceptibilities from their data. This capability enables highly targeted political messaging that can substantially influence voting decisions, potentially undermining citizens’ ability to make deliberative decisions in elections. While existing research has proposed interventions to mitigate the negative impacts of political micro-targeting, many of these interventions may become less effective as AI continually yields more powerful and subliminal forms of micro-targeting. Assuming that technologies can play a role in mitigating these negative impacts of AI, we conducted an explorative study to identify the design principles of technological tools that could facilitate citizens’ deliberative decision-making in elections in the continuously levelaged AI-based political campaigns. Using Indonesia’s 2024 elections as a case study, we interviewed twenty citizens and four political actors to gain critical insights into how such technologies might be developed. Initially, we anticipated that privacy-enhancing AI, used to counterattack profiling AI used by political actors, might suffice to facilitate election deliberation. However, our findings reveal a more complex reality: addressing societal issues through technology is inherently challenging; no single solution can serve as a silver bullet. Instead, facilitating election deliberation requires integrating privacy with other conditions, including self-reflection, education, access to diverse information, critical thinking, and openness to others. These design principles might serve as concrete, actionable design principles to guide the development of technologies to enhance election deliberation.

AI is developing rapidly, as are concerns about the environmental impact of its training and deployment. Studies about the environmental sustainability of AI have begun to emerge in the past five years, stressing the need for critical reflection on the discursive underbelly of this emerging scholarship. For example, how do authors frame the problem of the environmental impact of AI? Are there any ethical reflections accompanying their reporting, and if so, which ethical theories and principles guide normative considerations about the environmental impact of AI? In this study, we conduct a scoping literature review on (1) how authors refer to and frame the problem of the environmental impact of AI systems, (2) who is ascribed responsibility for mitigating said impact, (3) what mitigative measures are proposed, and (4) what normative commitments justify such prescriptive normative statements. Our findings indicate that most literature on the topic is concentrated in computer science, engineering, and natural sciences, and the humanities are mostly absent. This results in a dominance of technofix attitudes towards the problem, and a narrow and limited engagement with ethical principles and theories. As such, we argue for more interdisciplinary work on the environmental sustainability of AI, leading to more diverse solutions and more explicit and pluralistic ethical starting points, grounded, for example, in relational and more-than-human ethics. The findings of this review highlight gaps in the literature and opportunities for developers, social scientists, and AI ethicists for more effective and diverse responses to AI’s environmental impact.

Living with a chronic condition involves ongoing work: tracking symptoms, managing medication, coordinating care, and maintaining routines to prevent recurrence of acute episodes. In psychiatric care, this workload is equally complex yet rarely examined in HCI and design research. Digital phenotyping, a nascent AI/ML-based approach to monitoring behaviour, passively collects sensor and device data and is promoted as an unobtrusive and scalable tool. Yet, this promise of passivity obscures the skilled labour that takes place-from data sense-making to fitting new tools into existing care practices. In this position paper, situated in the context of outpatient bipolar disorder treatment in the Netherlands, we seek to problematise the current approach and apply Burden of Treatment Theory to unpack this hidden workload. We argue that digital phenotyping design lacks sensitivity to the care ecologies of self-management it intervenes into and call for HCI researchers to further engage with this problem space.

Large Language Models (LLMs) are expected to significantly impact various socio-technical systems, offering transformative possibilities for improved interaction between humans and technology. However, their integration poses complex challenges due to the intricate interplay between societal structures, human behaviour, and technological innovation. This research explores these multifaceted challenges, emphasising the need for a human-centered approach in integrating LLMs to ensure that technological advancements are aligned with ethical standards and societal needs. Utilizing a structured methodology comprising a workshop, literature analysis, and expert collaborations, the study uses a multi-dimensional human-centered AI framework to guide the responsible integration of LLMs. Key insights include the importance of inclusive data, considering unintended consequences, maintaining privacy, and respecting intellectual property rights. The paper identifies and advocates for principles like human-in-the-loop, continuous longitudinal studies, proactive awareness campaigns, and regular audits to develop LLMs that are ethically sound, adaptable, and effectively integrated into various socio-technical systems, thus addressing user needs and broader societal impacts. The paper also underlines the importance of collaboration among academia, industry, and policymakers to develop LLMs that are ethically aligned, socially beneficial, and adaptable to future societal needs. The findings offer valuable insights into the strategic integration of LLMs, advocating for a broader research perspective beyond industrial motivations to fully understand and leverage LLMs in socio-technical landscapes.