Calling for Backup: How Children Navigate Successive Robot Communication Failures
Maria Teresa Parreira, Isabel Neto, Filipa Rocha, Wendy Ju
TL;DR
This study investigates how children aged 8–10 respond to successive robot errors, including performance and social errors, by reproducing an adult-based paradigm with a wizarded robot. Using a controlled protocol, children interact with Simon while their perceptions and behaviors are video-coded; analyses compare child patterns to adults and reveal developmental differences. Key findings show that children maintain overall robot perception despite errors, but exhibit distinct coping strategies such as verbal reprompting, politeness shifts, emotional progression from confusion to frustration, and increased disengagement coupled with help-seeking. The work contributes design implications for error-tolerant, child-friendly human-robot interaction, emphasizing external agency transfer, multi-layered repair, and non-intrusive disengagement dynamics. These insights support building more robust, developmentally appropriate robotic systems for education and assistive contexts serving young users.
Abstract
How do children respond to repeated robot errors? While prior research has examined adult reactions to successive robot errors, children's responses remain largely unexplored. In this study, we explore children's reactions to robot social errors and performance errors. For the latter, this study reproduces the successive robot failure paradigm of Liu et al. with child participants (N=59, ages 8-10) to examine how young users respond to repeated robot conversational errors. Participants interacted with a robot that failed to understand their prompts three times in succession, with their behavioral responses video-recorded and analyzed. We found both similarities and differences compared to adult responses from the original study. Like adults, children adjusted their prompts, modified their verbal tone, and exhibited increasingly emotional non-verbal responses throughout successive errors. However, children demonstrated more disengagement behaviors, including temporarily ignoring the robot or actively seeking an adult. Errors did not affect participants' perception of the robot, suggesting more flexible conversational expectations in children. These findings inform the design of more effective and developmentally appropriate human-robot interaction systems for young users.
