Julie A. Kientz

In recent years, interest in eudaimonic player experiences (PX) - concerning reflection, meaning-making, and personal growth - has increased. However, most games user research methods are not well-suited to study eudaimonic PX, as they have been developed to evaluate features of hedonic PX, such as flow, immersion, and playability. To more deeply explore eudaimonic PX, we require methods that can 1) investigate how moment-to-moment PX shapes player reflection and 2) explore how players reengage and reinterpret their experiences longitudinally. In this paper, we present SAVEstate , a method that uses documentation as a means of studying player reflection. SAVEstate consists of two phases: reflection-in-action and reflection-on-action, which allow researchers to probe players’ in-situ reflections and how they reengage with their gameplay, respectively. Using SAVEstate, we were able to observe in-situ meaning-making and connect it to post-game reflection-on-action, and view synchronous sensemaking across multiple participants. We also developed an open-source desktop application for researchers to adapt in their own SAVEstate deployments. We discuss implications for how researchers might use SAVEstate to conduct future research in meaningful PX.

Sexual communication is critical to having healthy relationships and addressing sexual health inequities, but there are barriers to openly sharing clear information with one’s partner(s), especially for transgender and queer youth. Technology-based interventions offer strategies for mediating sexual communication; however, sexual communication literature is overly focused on contraceptive use, and little work has explored how to identify effective strategies through community-based participatory research. We therefore describe a multi-year design partnership with a youth advisory board made up of 18 United States-based transgender and queer youth that yielded two technology-based sexual communication tools. Desire Dialogues facilitates reflection and preparation to discuss one’s sexual boundaries with potential partners, and the Intimate Disclosure Toolkit guides users who wish to disclose personal information with others. By documenting our community- and value-driven design process, we identify participatory challenges, five design principles, and an agenda for future research that can guide the next generation of technology-based sexual communication tools.

Video games have increasingly been used to support mental health through various game-based interventions (GBIs), including exergames, virtual reality therapy, and cognitive behavioral therapy-based games. While these approaches often emphasize novel game mechanics and gamification, such features may undermine intrinsic motivation, which is critical for meaningful internalization of mental health messaging. Narrative, a well-studied mechanism in communication theory for fostering engagement and persuasion, remains underexplored in GBIs for mental health. This systematic review investigates narrative GBIs (NGBIs) using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Seventeen studies met the inclusion criteria. The majority addressed depression and anxiety, and CBT was common as an evidence-based grounding. However, few of the studies investigated the narrative factorially, and the disparate measures used to evaluate success made it difficult to determine the overall efficacy of NGBIs for mental health. We conclude with suggestions for future directions for research and design of NGBIs.

AI technologies, despite having well-documented biases and shortcomings, are becoming increasingly pervasive across various aspects of society. AI biases often reflect and interact with broader societal biases, underscoring the need to support children in understanding these biases so that they can identify when they (or others) are being discriminated against by an AI-based system. To explore this learning through a new methodology, we built an interactive system called CLIP4KIDS. We conducted four classroom sessions with 28 fifth graders in the United States and examined our data using qualitative thematic analysis. Students frequently described AI biases in terms of “assumptions” and “stereotypes” and drew connections between historical injustices and present biases in AI models. This work contributes a novel tool for learning about AI biases, an empirical account of children’s experiences, and a theoretical analysis incorporating Vossoughi and Gutiérrez’s framework of critical pedagogy and sociocultural theory.