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Systematic Review Identifies Drivers and Outcomes of Anthropomorphism in Children's LLM Chatbot Interactions
Key points
- Review analyzed 35 studies on children's LLM chatbot use.
- Identified four drivers of anthropomorphism in children's interactions.
- Found five outcomes of anthropomorphic interactions.
- Findings inform child-focused LLM chatbot design.
Understanding Children's LLM Interactions
Researchers conducted a systematic literature review to examine how children interact with large language model (LLM) chatbots, specifically focusing on the phenomenon of anthropomorphism. Anthropomorphism refers to children's tendency to assign human characteristics to non-human LLM chatbots. The review synthesized findings from 35 empirical studies published between 2022 and 2025.
Drivers of Anthropomorphism
The review identified four key factors that drive anthropomorphic interactions in children. These drivers include human-like persona construction, adaptive scaffolding, supportive companionship, and non-human embodied design. These elements contribute to children perceiving LLM chatbots with human-like qualities.
Outcomes of Interactions
Five distinct outcomes emerged from children's anthropomorphic interactions with LLM chatbots. Children exhibited paradoxical social and moral responses, displayed a dual consciousness regarding the chatbots, formed varying social ties, explored social boundaries, and attributed human narratives to conversation breakdowns. These outcomes highlight the complex nature of children's engagement.
Implications for Design
The findings, encompassing both benefits and risks, offer guidance for the future design and development of LLM chatbots intended for children. The research aims to inform practices that prioritize children's well-being and foster sustainable interactions. Understanding these drivers and outcomes is crucial for creating LLM applications that meet children's developmental needs effectively.
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Primary sources
arXiv 2607.18250Reporting from
A systematic literature review analyzed 35 empirical studies on children's interactions with large language model (LLM) chatbots, focusing on anthropomorphism. The review identified specific factors that drive children to attribute human characteristics to chatbots and detailed the subsequent outcomes of these interactions. These findings provide insights for the future design and development of LLM chatbots for children, aiming to promote well-being and sustainable interactions.