Niharika Mathur

Designing Conversational AI systems to support older adults with MCI necessitates robustness in handling complex and personalized interactions. In this study, we investigate how older adults with MCI experience, navigate and attempt to repair breakdowns while interacting with a voice-based AI system in their homes for medication management through the lens of interactional ”seams”. Through a 20-week in-home deployment with 7 participant dyads, we analyzed 844 interactions to identify interactional and conversational breakdowns and user repair strategies. Drawing on post-deployment interviews, we reflect on the nature of these breakdowns and older adults’ experiences of mitigating them. We further illustrate how older adults draw on their situated knowledge and environment to make sense of and recover from these disruptions, highlighting the cognitive effort required in doing so. Our findings emphasize the collaborative nature of interaction in human-AI contexts, and point to the need for voice assistants to better align with users’ expectations for memory, their routines and external resources in their environment. We conclude by discussing opportunities for voice assistants to integrate contextual knowledge from older adults’ sociotechnical environment and to facilitate more meaningful and user-centered interactions.

Older adults aging in place often have informal support systems to help them maintain independence and quality of life. As they age, many older adults deal with the onset of Mild Cognitive Impairment (MCI), which introduces a new set of functional and cognitive changes that affect their ability to manage daily routines . The approach to arranging and coordinating support for everyday activities for older adults with MCI varies across informal care networks, but typically involves a primary care partner and a network of family, friends and others. In this paper, we present a thematic analysis of in-depth interviews with older adults with MCI and their primary care partners to gain a holistic picture of their day-to-day lived experience . Our analysis uses a multi-dimensional lens of people (“ who ”), activities (“ what ”), and tools (“ how ”) to reveal insights about the nature of informal care coordination in MCI. Our results characterize informal care for MCI as a set of complex orchestration tasks by a primary care partner that support the practical, cognitive and emotional needs of the diagnosed individual and mediate the involvement of the broader care network. We uncover that coordination is not solely a matter of logistical organization, but also a deeply relational process shaped by negotiation with technological tools and evolving roles. Through this work, we reframe care coordination for MCI as a distinct and underexplored design space, one that demands systems capable of scaffolding autonomy, adapting to shifting capacities, responding to socio-emotional needs, and fostering collaborative caregiving.

As AI systems become increasingly conversational, a gap emerges wherein explanations are studied as static artifacts, yet in practice, are experienced as dialogue. In this provocation, we argue that the conversational layer around an explanation is not incidental to its effectiveness, but a critical constituent. Drawing on three illustrative scenarios, we invite the CUI community to study explanations as interactive, conversational exchanges shaped by timing, tone, persona and conversational history, and introduce our vision for Human-Centered Conversational XAI (HC2XAI).

Designing explainable and personalized AI systems to provide support to older adults aging in place requires an understanding of their motivations and expectations for the explanations. This poster presents our ongoing work in exploring explanation preferences within AI systems for older adults aging in place with their carepartners. We do so by leveraging the speculative and iterative benefits of the Research through Design (RtD) approach in HCI, and explore variations in explanation requirements for different users by understanding their needs, goals and motivations for the different sources of information within the home. We illustrate an example for employing a Research through Design inquiry for the design of AI applications, adopting speculative methods to probe into future possibilities of Explainable AI (XAI) using a human-centered design framework. Through a Speed Dating study and a Co-Design activity, we investigate different explanation types and scenarios and argue for a shift in the algorithmic focus of Explainable AI research toward user-centered requirements, positioning explanation as a collaborative process between AI systems and users.

While commercial conversational agents (CA) (i.e. Google assistant, Siri, Alexa) are widely used, these systems have limitations in error-handling, flexibility, personalization and overall dialogue management that are amplified in care coordination settings. In this paper, we synthesize and articulate these limitations through quantitative and qualitative analysis of 56 older adults interacting with a commercial CA deployed in their home for a 10 week period. We look at the CA as a compensatory technology in an older adult's care network. We argue that the CA limitations are rooted in the rigid cue-and-response style of task-oriented interactions common in CAs. We then propose a redesign for CA conversation flow to favor flexibility and personalization that is nonetheless viable within the limitations of current AI and machine learning technologies. We explore design tradeoffs to better support the usability needs of older adults compared to current design optimizations driven by efficiency and privacy goals.