Jiayue Melissa Shi

Dong Whi Yoo, Jiayue Melissa Shi, Violeta J. Rodriguez et al.

Recent advancements in LLMs enable chatbots to interact with individuals on a range of queries, including sensitive mental health contexts. Despite uncertainties about their effectiveness and reliability, the development of LLMs in these areas is growing, potentially leading to harms. To better identify and mitigate these harms, it is critical to understand how the values of people with lived experiences relate to the harms. In this study, we developed a technology probe, a GPT-4o based chatbot called Zenny, enabling participants to engage with depression self-management scenarios informed by previous research. We used Zenny to interview 17 individuals with lived experiences of depression. Our thematic analysis revealed key values: informational support, emotional support, personalization, privacy, and crisis management. This work explores the relationship between lived experience values, potential harms, and design recommendations for mental health AI chatbots, aiming to enhance self-management support while minimizing risks.

Jiayue Melissa Shi, Dong Whi Yoo, Keran Wang et al.

Family caregivers of individuals with Alzheimer's Disease and Related Dementia (AD/ADRD) face significant emotional and logistical challenges that place them at heightened risk for stress, anxiety, and depression. Although recent advances in generative AI -- particularly large language models (LLMs) -- offer new opportunities to support mental health, little is known about how caregivers perceive and engage with such technologies. To address this gap, we developed Carey, a GPT-4o-based chatbot designed to provide informational and emotional support to AD/ADRD caregivers. Using Carey as a technology probe, we conducted semi-structured interviews with 16 family caregivers following scenario-driven interactions grounded in common caregiving stressors. Through inductive coding and reflexive thematic analysis, we surface a systemic understanding of caregiver needs and expectations across six themes -- on-demand information access, emotional support, safe space for disclosure, crisis management, personalization, and data privacy. For each of these themes, we also identified the nuanced tensions in the caregivers' desires and concerns. We present a mapping of caregiver needs, AI chatbot's strengths, gaps, and design recommendations. Our findings offer theoretical and practical insights to inform the design of proactive, trustworthy, and caregiver-centered AI systems that better support the evolving mental health needs of AD/ADRD caregivers.

Jiayue Melissa Shi, Keran Wang, Dong Whi Yoo et al.

Alzheimer's Disease and Related Dementias (AD/ADRD) are progressive neurodegenerative conditions that impair memory, thought processes, and functioning. Family caregivers of individuals with AD/ADRD face significant mental health challenges due to long-term caregiving responsibilities. Yet, current support systems often overlook the evolving nature of their mental wellbeing needs. Our study examines caregivers' mental wellbeing concerns, focusing on the practices they adopt to manage the burden of caregiving and the technologies they use for support. Through semi-structured interviews with 25 family caregivers of individuals with AD/ADRD, we identified the key causes and effects of mental health challenges, and developed a temporal mapping of how caregivers' mental wellbeing evolves across three distinct stages of the caregiving journey. Additionally, our participants shared insights into improvements for existing mental health technologies, emphasizing the need for accessible, scalable, and personalized solutions that adapt to caregivers' changing needs over time. These findings offer a foundation for designing dynamic, stage-sensitive interventions that holistically support caregivers' mental wellbeing, benefiting both caregivers and care recipients.