SearcharxivSearch

arXiv subjects

Kelly Beaton

Publications and source records attributed to Kelly Beaton.

2 recordsLinked to original sources

Listening before Asking: Lived-Experience Advisors as Methodological Partners in Dementia Caregiving Studies

Research with dementia caregivers poses persistent methodological and ethical challenges, particularly when interview-based studies are designed without sufficient grounding in lived caregiving realities. Questions framed through clinical or deficit-oriented assumptions risk alienating participants, undermining rapport, and producing shallow or ethically fraught data. While human-computer interaction (HCI) research increasingly adopts participatory approaches in technology design, participation rarely extends to the design of research methods themselves. This paper examines the role of lived-experience advisors as methodological partners in caregiver interview research. We report on a qualitative study in which two advisors with extensive dementia caregiving experience were engaged prior to fieldwork as methodological partners, extending participatory principles beyond technology design into the design of research methods themselves. Drawing on transcripts of advisor consultations and subsequent interviews with ten caregivers and one person living with dementia, we identify two key methodological contributions of advisor involvement. First, advisors enabled anticipatory validity by surfacing caregiving challenges, ethical sensitivities, and interpretive concerns that later appeared in caregiver interviews, allowing the researcher to enter the field with grounded awareness under constrained recruitment and fieldwork conditions. Second, advisors provided cultural, emotional, and systemic context that improved interpretive sensitivity and helped avoid misreadings. We argue that lived experience functions as methodological infrastructure, extending participatory principles into the design and conduct of research itself, and constituting a generalizable methodological pattern for HCI research with caregivers and other vulnerable or marginalized populations.

cs.HC

From Checking to Sensemaking: A Caregiver-in-the-Loop Framework for AI-Assisted Task Verification in Dementia Care

Informal caregivers play a central role in enabling people living with dementia (PLwD) to remain at home, yet they face persistent challenges verifying whether daily tasks have been completed. Existing digital reminder systems prompt actions but rarely confirm outcomes, leaving caregivers to double-check tasks manually. This study explores how generative artificial intelligence (AI) might support caregiver-led task verification without displacing human judgment. We combined qualitative interviews with ten caregivers and one PLwD with a speculative simulation probe using a generative large language model to generate follow-up questions and flag responses for verification. Using template analysis, we identified three interrelated patterns of reasoning: detecting anomalies, constructing trustworthy evidence, and calibrating trust and control. These insights informed the Caregiver-in-the-Loop Task Verification (CLTV) framework, which models verification as a collaborative cycle of anomaly detection, evidence triangulation, AI-assisted summarization, and accountability circulation centered on caregiver oversight. CLTV advances human-AI collaboration theory by situating interpretability, trust, and control within the relational and emotional realities of dementia care and by offering design principles for transparent, adjustable, and context-aware AI support. We contribute a care-centered extension of human-AI collaboration theory, demonstrating how interpretability and trust can be operationalized through caregiver oversight.

cs.HC