PAL: Designing Conversational Agents as Scalable, Cooperative Patient Simulators for Palliative-Care Training
Journal:
arXiv
Published Date:
Jul 2, 2025
Abstract
Effective communication in serious illness and palliative care is essential
but often under-taught due to limited access to training resources like
standardized patients. We present PAL (Palliative Assisted Learning-bot), a
conversational system that simulates emotionally nuanced patient interactions
and delivers structured feedback grounded in an existing empathy-based
framework. PAL supports text and voice modalities and is designed to scaffold
clinical skill-building through repeated, low-cost practice. Through a
mixed-methods study with 17 U.S. medical trainees and clinicians, we explore
user engagement with PAL, evaluate usability, and examine design tensions
around modalities, emotional realism, and feedback delivery. Participants found
PAL helpful for reflection and skill refinement, though some noted limitations
in emotional authenticity and the adaptability of feedback. We contribute: (1)
empirical evidence that large language models can support palliative
communication training; (2) design insights for modality-aware, emotionally
sensitive simulation tools; and (3) implications for systems that support
emotional labor, cooperative learning, and AI-augmented training in high-stakes
care settings.
