儿童与基于大语言模型的聊天机器人互动中的拟人化：驱动因素和结果的系统综述
Anthropomorphism in Children's Interactions with LLM Chatbots: A Systematic Review of Drivers and Outcomes
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中文总结 AI 辅助
本研究通过分析35项实证研究，系统综述儿童与LLM聊天机器人互动中拟人化的驱动因素及结果，发现类人角色构建等驱动互动，出现五种拟人化结果，可为相关聊天机器人设计开发提供参考。
中文摘要 AI 辅助
各领域研究人员通过多种视角和方法调查了儿童对基于大语言模型（LLM）的聊天机器人的使用情况。然而，关于拟人化，即儿童将人类特征赋予这些非人类对象的大语言模型聊天机器人的倾向，先前的研究仍然零散。通过分析2022年至2025年发表的35项实证研究，本系统文献综述确定了儿童与LLM聊天机器人互动中拟人化的驱动因素以及这些互动的后续结果。我们发现，类人角色构建、适应性支架、支持性陪伴和非人类实体设计推动了儿童的拟人化互动。此外，出现了五种拟人化结果，包括儿童表现出矛盾的社会和道德反应、对聊天机器人的双重意识、形成不同的社会关系、探索社会边界以及将人类叙事归因于对话中断。这些发现，包括益处和风险，可为未来专注于儿童福祉并促进满足儿童发展需求的可持续互动的LLM聊天机器人的设计和开发提供参考。
英文摘要
Researchers across domains have investigated children's use of LLM-based chatbots through various perspectives and methodologies. However, prior research remains fragmented regarding anthropomorphism, the tendency for children to assign human characteristics to those large language Model (LLM) chatbots as non-human objects. By analyzing 35 empirical studies published between 2022 and 2025, this systematic literature review identifies the drivers of anthropomorphism in children's LLM chatbot interactions and the subsequent outcomes of these interactions. We found that human-like persona construction, adaptive scaffolding, supportive companionship, and non-human embodied design drive children's anthropomorphic interactions. Additionally, five anthropomorphic outcomes emerged, including children exhibiting paradoxical social and moral responses, dual consciousness about the chatbots, forming varying social ties, exploring social boundaries, and attributing human narratives to conversation breakdowns. The findings, including both benefits and risks, can inform the future design and development of LLM chatbots focused on children's well-being and promoting sustainable interactions that meet children's developmental needs.
