AI-generated non-consensual intimate imagery (AIG-NCII) is an emerging social problem due to the advancement of AI tools. While recent incidents in middle and high schools have highlighted the urgency of this issue, there is limited understanding of what concrete supports schools need to effectively address AIG-NCII. To fill this gap, we conducted an interview study with 20 educators in the U.S. and investigated their attitudes, experiences, and practices related to AIG-NCII. Educators expressed concerns about both students’ and their own vulnerability, as AIG-NCII may cause moral decline among students, while educators themselves could become victims. Nevertheless, existing practices in schools are limited, and they lack both training and systematic policies. Challenges such as a lack of resources, unclear legal boundaries, and limited knowledge of AI make implementation difficult. The findings of this paper contribute to interactive educational tool design, curriculum design, and policy-making, especially regarding the need for multi-stakeholder strategies to address issues surrounding AIG-NCII.

In online fan communities, generative AI is providing new ways for fans to engage with and produce fan content related to their favorite celebrities.In this study, we examined how people feel about AIgenerated fan art and AI-generated music covers of their favorite musical artists.Through an online survey (N=200) we explored how parasocial relationships with their favorite musicians and selfdetermination factors of autonomy, competence, and relatedness were associated with attitudes towards AI-generated fan content.Parasocial relationships were positively associated, but when these intrinsic values were taken into consideration, participants' sense of choice in controlling what they see on social media was the only factor that explained favorable sentiments towards AI-generated fan content.

Sleep has been studied as an individual activity, with the interconnected behaviors among family members being rarely considered. In this study, by incorporating the identified themes from the previous phase, we designed and tested two types of family-based sleep management systems: Bedtime Pal and Caring Heart. These systems redistributed sleep-relevant tasks among family members, so that they would have the chance to reflect on the difficulties and values involved in those tasks. We deployed the two prototypes and performed an in-the-wild study with 12 families in their homes. This study empirically revealed the importance of considering social dynamics as a design factor for family sleep management technologies. Design implications for sleep management technologies are discussed.