Research to date in AI and dementia has primarily focused on medical and care-giver applications. This study complements prior work by collecting qualitative data from individuals with Mild Cognitive Impairment (MCI), and mild-to-moderate dementia, uncovering their perspectives, envisioned use cases, and anticipated challenges with generative AI applications. Findings uncover ways people with MCI and dementia want to use AI to engage in resilience building activities related to fostering wellness, building social connections, and finding purpose. However, each of these use cases came with tradeoffs in embracing healthy thinking. Based on these findings, we discuss directions for future work in AI for resilience building in dementia. We intend for this work to lay the groundwork for future research in AI design and development to empower people living with dementia to engage in resilience building activities, which may have lasting positive impacts on their mental health and well-being.

A growing organizational trend is to utilize ad-hoc team formation which allows for teams to intentionally form based on the member skills required to accomplish a specific task. Due to the unfamiliar nature of these teams, teammates are often limited by their understanding of one another (e.g., teammate preferences, tendencies, attitudes) which limits the team's functioning and efficiency. This study conceptualizes and investigates the use of a teammate information-sharing recommender system which selectively shares interpersonal recommendations between unfamiliar teammates (e.g., "Your voice may be overshadowed by this teammate when making decisions...") to promote teammate understanding. Through a mixed-methods approach involving 105 participants working on actual unfamiliar teams, this study explores how presentation elements such as anonymity and explanations influence system perceptions and how anonymity influences team outcomes. Results indicate that anonymizing recommendations was associated with worse team measures, particularly team satisfaction and team cohesion. Qualitative results shed light on why team members perceived privacy concerns and team benefits associated with using the system. We contribute to CSCW through a better understanding of how to support unfamiliar teams, the conceptualization and empirical investigation of a novel teammate information-sharing recommender system, and foundational design recommendations associated with such a system.