• Children had more communication breakdowns with AI partners but fewer repair attempts • Children’s perceptions of emotional capacity of their partner affect repair behaviors • Perceived homophily influenced repair behaviors specifically in AI interactions • Children adapted to breakdowns with AI using accommodation and seeking external help • Future child-AI interfaces should enhance emotional responsiveness and connection Children’s interactions with artificial intelligence (AI) are growing, yet communication breakdowns—instances where mutual understanding fails—remain a challenge, especially for young children. While generative AI shows promise in engaging children in open-ended conversations, how children navigate and repair these communication breakdowns remains unclear. This study compares how 78 children, aged four to eight years, managed communication breakdowns and repair strategies while co-creating stories with an AI agent (powered by a large language model) versus a human counterpart. Results reveal that the type of conversational partner—human or AI—significantly influenced children’s repair behaviors. Children experienced more communication breakdowns when interacting with the AI partner but attempted repairs more frequently with the human counterpart. Misunderstandings and mishearings are the most frequent causes, with clarification requests as the primary repair strategy in both cases. However, when interacting with the AI, children were more likely to go along with the conversation flow to compensate for AI errors, even when the dialogue deviated from their intended meaning—a pattern not observed with human partners. Additionally, children’s social perceptions of their partner, especially beliefs about emotional capacity and their psychological closeness to their partner, influenced repair attempts. This study expands research on children’s conversational repairs with AI, shedding light on the role of social dynamics in shaping these interactions.

As Conversational Agents (CAs) develop more sophisticated language abilities, their potential as interactive storytelling companions for children continues to grow.This study examines how children adjust their speech and emotional expression during collaborative storytelling with a human, an AI, or a human through an AI interface.Our analysis of acoustic and emotional dimensions suggests that AI and disembodied interactions show weaker prosodic alignment and lower emotional intensity compared to child-human interactions.These findings point to the need for CAs that nurture more responsive and emotionally attuned interactions.

In this study, we explored communication breakdowns and repair strategies among 71 children aged 4-8 years while co-creating stories with a generative AI agent enabled by Large Language Models and a human partner. Analyzing approximately 1420 minutes of video recordings, our findings reveal that children experienced more communication breakdowns when interacting with the AI partner but attempted repairs more frequently with human counterparts. Notably, children who attributed greater mind perception to non-human entities were more proactive in attempting repairs during interactions with both human and AI partners, with this trend being more pronounced when children interacted with AI. This work-in-progress offers theoretical contributions by illuminating the interplay between perception and communication. It also underscores important design considerations for developing LLM-enabled generative AI agents that are socio-cognitively responsible and aligned with children’s perceptions.