This study examines how different robot dialogue systems (rule-based vs. large language model-based) and content types (emotional support vs. safety education) affect pediatric patients' responses during hospital-based robot-mediated interventions.
Approximately 60 pediatric patients aged 2-9 years will be randomly assigned to interact with a social robot (LIKU) using either rule-based or LLM-based dialogue. Each child will participate in two activity sessions (emotional content and safety content) in randomized order.
Primary outcomes include child engagement, emotional responses, robot perception, and activity preferences, assessed through standardized questionnaires (UEQ, Godspeed), child interviews, and behavioral observations. Additionally, 5 experts will evaluate content appropriateness and safety.
This pilot study aims to provide foundational data for developing personalized pediatric robot programs in hospital settings, optimizing both dialogue approaches and content design based on individual child characteristics.