An Interactive LLM-Based Simulator for Dementia-Related Activities of Daily Living

• URL: http://arxiv.org/abs/2603.29856v1
• Date: Fri, 06 Mar 2026 03:15:17 GMT
• Title: An Interactive LLM-Based Simulator for Dementia-Related Activities of Daily Living
• Abstract summary: We introduce a web-based simulator that generates multi-turn, severity- and care-setting-conditioned patient behaviors during ADL assistance.<n>Simulated behavior was judged moderately to highly plausible, with a typical session length of six turns.<n>Experts wrote custom replies for 54.5 percent of turns; Recognition and refinements were the most-used strategies.
• Score: 1.7290060384409396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Effective dementia caregiving requires training and adaptive communication, but assistive AI and robotics are constrained by a lack of context-rich, privacy-sensitive data on how people living with Alzheimer's disease and related dementias (ADRD) behave during activities of daily living (ADLs). We introduce a web-based simulator that uses a large language model (gpt-5-mini) to generate multi-turn, severity- and care-setting-conditioned patient behaviors during ADL assistance, pairing utterances with lightweight behavioral cues (in parentheses). Users set dementia severity, care setting (and time in setting), and ADL; after each patient turn they rate realism (1-5) with optional critique, then respond as the caregiver via free text or by selecting/editing one of four strategy-scaffolded suggestions (Recognition, Negotiation, Facilitation, Validation). We ran an online formative expert-in-the-loop study (14 dementia-care experts, 18 sessions, 112 rated turns). Simulated behavior was judged moderately to highly plausible, with a typical session length of six turns. Experts wrote custom replies for 54.5 percent of turns; Recognition and Facilitation were the most-used suggested strategies. Thematic analysis of critiques produced a six-category failure-mode taxonomy, revealing recurring breakdowns in ADL grounding and care-setting consistency and guiding prompt/workflow refinements. The simulator and logged interactions enable an evidence-driven refinement loop toward validated patient-caregiver co-simulation and support data collection, caregiver training, and assistive AI and robot policy development.

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