The Interaction Layer: An Exploration for Co-Designing User-LLM Interactions in Parental Wellbeing Support Systems

Parenting brings emotional and physical challenges, from balancing work, childcare, and finances to coping with exhaustion and limited personal time. Yet, one in three parents never seek support. AI systems potentially offer stigma-free, accessible, and affordable solutions. Yet, user adoption often fails due to issues with explainability and reliability. To see if these issues could be solved using a co-design approach, we developed and tested NurtureBot, a wellbeing support assistant for new parents. 32 parents co-designed the system through Asynchronous Remote Communities method, identifying the key challenge as achieving a "successful chat". Aspart of co-design, parents role-played as NurturBot, rewriting its dialogues to improve user understanding, control, and outcomes. The refined prototype evaluated by 32 initial and 46 new parents, showed improved user experience and usability, with final CUQ score of 91.3/100, demonstrating successful interaction patterns. Our process revealed useful interaction design lessons for effective AI parenting support.

Beware of the Ostrich Policy: End-Users' Perceptions Towards Data Transparency and Control

End users' awareness about the data they share, the purpose of sharing that data, and their control over it, is key to establishing trust and eradicating privacy concerns. We experimented on personal data management by prototyping a Point-of-Interest recommender system in which data collected on the user can be viewed, edited, deleted, and shared via elements in the User Interface. Based on our qualitative findings, in this paper we discuss "The ostrich policy" adopted by end users who do not want to manage their personal data. We sound a waking whistle to design and model for personal data management by understanding end users' perceptions towards data transparency and control.