Elder-Sim: A Psychometrically Validated Platform for Personality-Stable Elderly Digital Twins
Abstract
Abstract Background Large language models (LLMs) enable patient-facing conversational agents, creating a plausible pathway toward patient digital twins that can capture older adults’ lived experiences, beliefs, and behavioral responses across time. A central barrier to clinical-grade digital twins is personality drift—inconsistent trait expression across repeated, longitudinal interactions—which can undermine the reliability of generated trajectories and intervention-response simulation in geriatric care contexts. Objective To develop ELDER-SIM as a multi-role elderly-care conversational platform oriented toward building personality-stable elderly digital twin agents, and to propose a psychometric validation framework for quantifying and improving personality consistency in LLM-based agents across repeated interactions. Methods ELDER-SIM was implemented using n8n workflow orchestration with local LLM inference (Ollama/vLLM), integrating (1) Big Five (OCEAN) trait specifications, (2) a three-layer Cognitive Conceptualization Diagram (CCD) grounded in Beck’s cognitive behavioral therapy framework, and (3) a MySQL-based long-term memory module for persistent states. We conducted a systematic ablation study across four conditions—Baseline (prompt-only), +Memory, +CCD, and +LoRA (domain-specific fine-tuning using 19,717 instruction pairs derived from CHARLS)—and evaluated personality consistency using Cronbach’s α (internal consistency), intraclass correlation coefficient (ICC)…
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