Adolescents are frequent users of social media, with research suggesting both potential harms and positive impacts from use.Black and Hispanic/Latinx youth in particular are both early adopters and high users of social media platforms.However, adolescents-and youth of color in particular-have relatively little say in the design of such platforms.We propose youth participatory action research (YPAR) as a model for informing co-design sessions with representatives of a social networking platform to develop communitybuilding solutions and improve youth developmental outcomes.In a four-months-long study with Black and Hispanic/Latinx teens aged 14-17 (𝑛 = 14), we examined how their sense of engagement and efficacy were altered by actively leading, participating in and contributing to design exercises facilitated by Instagram, one of the world's largest social media sites.Results of pre-and post-surveys indicated a significant increase in teens' civic engagement as well as leadership efficacy.Our results contribute to the understanding of teenagers' expectations and attitudes toward social media and how participatory methods for achieving equity in design can affect change.Theoretical and practical implications are discussed.

Many researchers are working to address the worldwide mental health crisis by developing therapeutic technologies that increase the accessibility of care, including leveraging large language model (LLM) capabilities in chatbots and socially assistive robots (SARs) used for therapeutic applications. Yet, the effects of these technologies over time remain unexplored. In this study, we use a factorial design to assess the impact of embodiment and time spent engaging in therapeutic exercises on participant disclosures. We assessed transcripts gathered from a two-week study in which 26 university student participants completed daily interactive Cognitive Behavioral Therapy (CBT) exercises in their residences using either an LLM-powered SAR or a disembodied chatbot. We evaluated the levels of active engagement and high intimacy of their disclosures (opinions, judgments, and emotions) during each session and over time. Our findings show significant interactions between time and embodiment for both outcome measures: participant engagement and intimacy increased over time in the physical robot condition, while both measures decreased in the chatbot condition.

With the development of natural language processing (NLP), open-domain chatbots can operate as companions. The success of open-domain chatbots depends on understanding how positive and negative expectancy violations affect both utilitarian and hedonic gratification, which in turn influences user satisfaction and continued use. Therefore, this study examines how violations of expectations influence the hedonic and utilitarian evaluations of open-domain chatbots using the expectancy-violation theory. Participants (n = 204) interacted with an improvising AI chatbot and reported their satisfaction. Results indicated that both hedonic and utilitarian satisfaction were higher when negative expectations were met compared to when expectations were violated. However, no significant difference was found in hedonic gratification between the chatbot’s expected performance and positively unexpected performance. Conversations meeting expectations elicited more utilitarian satisfaction than positively surprising interactions. These findings highlight the dynamics between value types and expectation violations in AI chatbot evaluations.

The use of social robots in service tasks is spreading, showcasing advantages for both consumers and service providers. However, their widespread adoption is hindered by a notable lack of trust. Our study aims to uncover insights into the factors influencing the adoption of social robots in home settings, exploring the factors that lead users to trust and eventually adopt robots. We designed two experimental conditions, presenting the Amazon Astro robot from different perspectives (high-angle and eye-level) and demonstrating its different abilities to 198 people recruited from MTurk. We employed both quantitative (trust, first impressions of warmth and competence as well as usability, familiarity, and attitudes) questionnaires and qualitative (word analysis) assessments, and results showed that participants had higher trust scores when seeing the robot from an eye-level perspective. In addition, usability, familiarity and competence were shown to explain a significant amount of variance in trust. While existing negative attitudes towards robots and the participants’ age were shown to be the strongest predictors for participants’ willingness to purchase a robot, trust was able to significantly affect use intention. We contribute to the broader understanding of the challenges and opportunities in integrating social robots into daily life, shedding light on the dynamics between technological innovation and consumer adoption.

This paper describes a between-subjects Amazon Mechanical Turk study (n = 220) that investigated how a robot’s affective narrative influences its ability to elicit empathy in human observers. We first conducted a pilot study to develop and validate the robot’s affective narratives. Then, in the full study, the robot used one of three different affective narrative strategies (funny, sad, neutral) while becoming less functional at its shopping task over the course of the interaction. As the functionality of the robot degraded, participants were repeatedly asked if they were willing to help the robot. The results showed that conveying a sad narrative significantly influenced the participants’ willingness to help the robot throughout the interaction and determined whether participants felt empathetic toward the robot throughout the interaction. Furthermore, a higher amount of past experience with robots also increased the participants’ willingness to help the robot. This work suggests that affective narratives can be useful in short-term interactions that benefit from emotional connections between humans and robots.