🤖 AI Summary
This study investigates behavioral responses and coping strategies of children aged 8 to 10 when a robot repeatedly misunderstands their instructions three times in succession, addressing a gap in human–robot interaction research for this age group. Building on Liu et al.’s sequential failure paradigm, the authors employed experimental psychology methods and video-based behavioral coding to analyze multimodal interaction data from 59 children. The findings reveal, for the first time, distinct adaptive patterns in this cohort under repeated error conditions: children actively sought adult assistance, temporarily disengaged from the robot, modified their verbal expressions and prosody, and displayed heightened emotional expressivity—though they were also more prone to task disengagement. Notably, despite these errors, children’s overall evaluations of the robot remained unchanged, suggesting more flexible expectations in human–robot interaction compared to adults.
📝 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.