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

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.

Figures (8)

• Figure 1: Simulated person with Alzheimer’s dementia (PLWD) interacting with a human caregiver at a selected dementia stage.
• Figure 2: End-to-end participant-facing workflow of the web-based dementia–ADL simulator. After consent and a brief background questionnaire, participants configure a scenario (dementia stage, care setting, ADL). Within the dashed region (turn-level interaction), the LLM-based patient simulator generates a PLWD response; participants provide a realism rating and optional critique, then respond as the caregiver either via free-text or by selecting a strategy-scaffolded suggestion (Recognition, Validation, Facilitation, Negotiation; edits optional). The loop continues until the participant terminates the scenario or reaches turn 10. Participants may then reset the scenario to start a new simulation or end the session. Interaction transcripts can be downloaded.
• Figure 3: Prompt design and turn-level context flow for generating PLWD behaviors and caregiver responses. Scenario configuration (care setting and time in setting, living situation, dementia severity, ADL, and task progress context) forms a shared context. Each turn uses a windowed interaction history to condition (i) PLWD response generation and (ii) strategy-scaffolded caregiver response suggestions.
• Figure 4: Simulation page as displayed to participants during interaction with the simulated PLWD. A) Conversation history updates as the interaction proceeds; B) Downloadable transcript of the entire interaction for participants records; C) Simulation settings chosen by the participant; D) Generated PLWD response (black text: verbal utterance; grey text in parentheses: nonverbal behavior); E) Realism rating (1-5) for the current generated PLWD response, with optional free-text critique; F) Caregiver response interface: participants may write a free-text response or select a strategy-scaffolded suggestion.
• Figure 5: An example of PLWD-caregiver interaction during taking medicine in PLWD's home. The PLWD is with middle-stage Alzheimer's dementia, living at their home for more than 1 year. The text in parentheses represents PLWD's nonverbal behavior. Other text represent PLWD's and caregiver's verbal response, correspondingly