SoHyun Park

Eunkyung Jo, Yuin Jeong, SoHyun Park et al.

Recent large language models (LLMs) offer the potential to support public health monitoring by facilitating health disclosure through open-ended conversations but rarely preserve the knowledge gained about individuals across repeated interactions. Augmenting LLMs with long-term memory (LTM) presents an opportunity to improve engagement and self-disclosure, but we lack an understanding of how LTM impacts people's interaction with LLM-driven chatbots in public health interventions. We examine the case of CareCall -- an LLM-driven voice chatbot with LTM -- through the analysis of 1,252 call logs and interviews with nine users. We found that LTM enhanced health disclosure and fostered positive perceptions of the chatbot by offering familiarity. However, we also observed challenges in promoting self-disclosure through LTM, particularly around addressing chronic health conditions and privacy concerns. We discuss considerations for LTM integration in LLM-driven chatbots for public health monitoring, including carefully deciding what topics need to be remembered in light of public health goals.

Jungeun Lee, Kyungah Lee, Inseok Hwang et al.

Social narratives are known to help autistic children understand and navigate social situations through stories. To ensure effectiveness, however, the materials need to be customized to reflect each child's unique behavioral context, requiring considerable time and effort for parents to practice at home. We present AutiHero, a generative AI-based social narrative system for behavioral guidance, which supports parents to create personalized stories for their autistic children and read them together. AutiHero generates text and visual illustrations that reflect their children's interests, target behaviors, and everyday contexts. In a two-week deployment study with 16 autistic child-parent dyads, parents created 218 stories and read an average of 4.25 stories per day, demonstrating a high level of engagement. AutiHero also provided an effective, low-demanding means to guide children's social behaviors, encouraging positive change. We discuss the implications of generative AI-infused tools to empower parents in guiding their children's behaviors, fostering their social learning.

Migyeong Yang, Kyungah Lee, Jinyoung Han et al.

Journaling can potentially serve as an effective method for autistic adolescents to improve narrative skills. However, its text-centric nature and high executive functioning demands present barriers to practice. We present Autiverse, an AI-guided multimodal journaling app for tablets that scaffolds storytelling through conversational prompts and visual supports. Autiverse elicits key details through a stepwise dialogue with peer-like, customizable AI and composes them into an editable four-panel comic strip. Through a two-week deployment study with 10 autistic adolescent-parent dyads, we examine how Autiverse supports autistic adolescents to organize their daily experience and emotion. Autiverse helped them construct coherent narratives, while enabling parents to learn additional details of their child's events and emotions. The customized AI peer created a comfortable space for sharing, fostering enjoyment and a strong sense of agency. We discuss the implications of designing technologies that complement autistic adolescents' strengths while ensuring their autonomy and safety in sharing experiences.