As older adults (OAs) increasingly favor aging in place, remote family members (RFMs) need meaningful awareness of their loved ones without intrusive monitoring. Large language models can provide rich interpretations of non-intrusive sensing data, yet recent work has largely emphasized technical accuracy over how families use or benefit from this information. We conducted formative interviews with 12 RFMs and 2 OAs, followed by a three-month deployment case study with one OA-RFM dyad. Our system provided daily AI-generated summaries of multimodal sensing data, which we iteratively refined with both parties using a research-through-design approach. Although the summaries integrated well into the dyad's daily routine, sustaining over 90% engagement, their lived experience revealed evolving tensions around agency, reassurance, privacy, trust, and communication, shaped by sensor limitations and existing communication misalignments. Our findings inform the design of future AI-mediated sharing systems that account for real-world sensing constraints and complex family care dynamics.
Tamara Zubatiy, Niharika Mathur, Reina Chan, Srijan Pokharel, Rosemarie Santa González, Agata Rozga, Xiang Zhi Tan, Elizabeth D. Mynatt
原文摘要
Conversational agents (CAs) for older adults have moved through at least two distinct eras, split by the emergence of large language models (LLMs) at the end of 2022. The first era was defined by commercial smart speakers such as the original Google Home and Amazon Alexa, built on rigid, rule-based dialogue. Interactions were single-turn, user-initiated and "successful" if and only if a command matched one of a finite set of pre-templated intents. The current era is marked by LLM-powered systems capable of open-ended, multi-turn conversation, with capacity for personalization and foundations for more proactive interaction. This shift has not only transformed what CAs can do for users, but also changed what researchers can measure when it comes to understanding "success" of user interactions with CAs. In this case study, we reflect on over 8 years of research spanning more than 100 participants across multiple field sites to trace how our own working definitions of "success" have evolved. We reflect on two completed bodies of work with pre-LLM CAs and an in-progress study with a post-LLM CA. We discuss how our definition and measurement of success have evolved during this transition and also ask open questions to the community about how to optimally measure "success" in the post-LLM CA era. This research is deeply applied and subject to the messiness of real-world deployments. Our intended contributions are deliberately practical, a combination of methodological considerations and reflection questions to support researchers engaging with this new wave of technologies.