Generative AI (GenAI) is evolving from standalone tools to interconnected ecosystems that integrate chatbots, cloud platforms, and third-party services. While this ecosystem model enables personalization and extended services, it also introduces complex information flows and amplifies privacy risks. Existing solutions focus on system-level protections, offering little support for users to make meaningful privacy choices. To address this gap, we conducted two vignette-based survey studies with 486 participants and a follow-up interview study with 16 participants. We also explored users’ needs and preferences for privacy choice design across both GenAI personalization and data-sharing. Our results reveal paradoxical patterns: participants sometimes trusted third-party ecosystems more for personalization but perceived greater control in first-party ecosystems when data was shared externally. We discuss design implications for privacy choice interfaces that enhance transparency, control, and trust in GenAI ecosystems.

Children born in the digital era are facing increasing privacy risks and the need to control privacy in various contexts, suggesting an urgent need to enhance their privacy literacy. While previous research focuses on developing children’s privacy literacy by delivering privacy knowledge, it remains unclear how children process the knowledge and apply it in various privacy situations. Furthermore, children’s desire for privacy controls remains understudied. To fill the gap, we conducted two five-day co-design workshops with 11 children (ages 6-11). We uncovered children’s sophisticated expectations of everyday privacy management, such as staying aware of their privacy situations, strong authentication methods, and minimal privacy exposure. We further discovered that children translated their privacy knowledge to privacy practices through an iterative reflection and action process. We discussed key considerations to support children’s privacy literacy development by leveraging this process and offered implications for children-friendly privacy design.

While 96% of U.S. teens use the internet daily, most families face challenges in discussing privacy concerns, with parents feeling unprepared and teens being hesitant to communicate. This study explored how guided family discussions, grounded in perspective-taking theory, promoted mutual understanding and enhanced digital privacy literacy. Through a qualitative study involving 13 parent-child pairs, we identified three key communication challenges: abstract discussions about privacy, reliance on absolute statements, and a decline in teen engagement. These challenges stemmed from limited privacy literacy and a lack of adaptive communication. Our perspective-taking facilitation approach addressed these issues by transforming traditional parent-led conversations into collaborative exchanges through reflective practices and helping families view privacy as a context-dependent concept. We propose design implications for educational technology to scale the support of family privacy discussions, including tools that support perspective-taking and interfaces that highlight non-binary privacy choices.

As families face increasingly complex safety challenges in digital and physical environments, generative AI (GenAI) presents new opportunities to support household safety through multiple specialized AI agents. Through a two-phase qualitative study consisting of individual interviews and collaborative sessions with 13 parent-child dyads, we explored families' conceptualizations of GenAI and their envisioned use of AI agents in daily family life. Our findings reveal that families preferred to distribute safety-related support across multiple AI agents, each embodying a familiar caregiving role: a household manager coordinating routine tasks and mitigating risks such as digital fraud and home accidents; a private tutor providing personalized educational support, including safety education; and a family therapist offering emotional support to address sensitive safety issues such as cyberbullying and digital harassment. Families emphasized the need for agent-specific privacy boundaries, recognized generational differences in trust toward AI agents, and stressed the importance of maintaining open family communication alongside the assistance of AI agents. Based on these findings, we propose a multi-agent system design featuring four privacy-preserving principles: memory segregation, conversational consent, selective data sharing, and progressive memory management to help balance safety, privacy, and autonomy within family contexts.

Adolescent peer relationships, essential for their development, are increasingly mediated by digital technologies. As this trend continues, wearable devices, especially smartwatches tailored for adolescents, is reshaping their socialization. In China, smartwatches like XTC have gained wide popularity, introducing unique features such as “Bump-to-Connect” and exclusive social platforms. Nonetheless, how these devices influence adolescents’ peer experience remains unknown. Addressing this, we interviewed 18 Chinese adolescents (age: 11—16), discovering a smartwatch-mediated social ecosystem. Our findings highlight the ice-breaking role of smartwatches in friendship initiation and their use for secret messaging with local peers. Within the online smartwatch community, peer status is determined by likes and visibility, leading to diverse pursuit activities (i.e., chu guanxi, jiazu, kuolie) and negative social dynamics. We discuss the core affordances of smartwatches and Chinese cultural factors that influence adolescent social behavior, and offer implications for designing future wearables that responsibly and safely support adolescent socialization.

Children face increasing privacy risks and the need to navigate complex choices, while privacy education is not sufficient due to limited education scope and family involvement. We advocate for informal learning spaces (ILS) as a pioneering channel for family-based privacy education, given their established role in holistic technology and digital literacy education, which specifically targets family groups. In this paper, we conducted an interview study with eight families to understand revealing current approaches to privacy education and engagement with ILS for family-based learning. Our findings highlight ILS’s transformative potential in family privacy education, considering existing practices and challenges. We discuss the design opportunities for family-based privacy education in ILS, covering goals, content, engagement, and experience design. These insights contribute to future research on family-based privacy education in ILS.

Observation is common for children to connect with nature, increasing their knowledge and awareness of biodiversity. However, it is challenging for them to make and document their observations due to a lack of observation and drawing skills. Therefore, we designed an interactive drawing tool, Bio Sketchbook, which scaffolds children in systematic observation, observational drawing, and knowledge acquisition. It can recognize plant species and generate contour drawings from children’s photographs, guiding them to observe and draw multi-dimensional plant features with a digital magnifier and in-context biological information. Our in-situ user study with 19 children revealed that Bio Sketchbook provided an engaging experience and effectively supported children in recording and retaining biodiversity information and in balancing observations with screen time. Additionally, Bio Sketchbook intervened in children’s interaction with plants by prompting observational behaviors, encouraging them to directly touch and establish rapport with plants, and arousing their interest and knowledge of plants.