Exploring Caregivers' Acceptance of Conversational AI in Pediatric Cancer Caregiving: A Mixed-Methods Study

Abstract

Caring for a child with cancer involves navigating through complex medical information, often delivered through lengthy handbooks and consultations with healthcare providers. Overnight, parents are expected to become an expert on a domain which they knew nothing about. Conversational UIs, powered by Large Language Models (LLMs) and validated information sources, could play a key role in supporting caregivers. In this paper, we investigate the usability, acceptance, and perceived utility of an LLM-based conversational AI tool for pediatric cancer caregiving, grounded in the Children’s Oncology Group Family Handbook–the leading resource in pediatric oncology care. We employed a mixed-methods approach, interviewing and surveying 12 caregivers as they engaged with a functional prototype. We offer insights into caregiver’s needs and expectations from AI-driven tools, and design guidelines for developing safer, more personalized, and supportive AI interventions for pediatric cancer care.

Publication
Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems