Anthropomorphisation -- the phenomenon whereby non-human entities are ascribed human-like qualities -- has become increasingly salient with the rise of large language model (LLM)-based conversational agents (CAs). Unlike earlier chatbots, LLM-based CAs routinely generate interactional and linguistic cues, such as first-person self-reference, epistemic and affective expressions that empirical work shows can increase engagement. On the other hand, anthropomorphisation raises ethical concerns, including deception, overreliance, and exploitative relationship framing, while some authors argue that anthropomorphic interaction may support autonomy, well-being, and inclusion. Despite increasing interest in the phenomenon, literature remains fragmented across domains and varies substantially in how it defines, operationalizes, and normatively evaluates anthropomorphisation. This scoping review maps ethically oriented work on anthropomorphising LLM-based CAs across five databases and three preprint repositories. We synthesize (1) conceptual foundations, (2) ethical challenges and opportunities, and (3) methodological approaches. We find convergence on attribution-based definitions but substantial divergence in operationalization, a predominantly risk-forward normative framing, and limited empirical work that links observed interaction effects to actionable governance guidance. We conclude with a research agenda and design/governance recommendations for ethically deploying anthropomorphic cues in LLM-based conversational agents.

翻译：拟人化——即赋予非人类实体以人类特质的现象——随着基于大语言模型的对话代理的兴起而日益凸显。与早期聊天机器人不同，基于大语言模型的对话代理能够常规性地生成互动性和语言性线索（如第一人称自我指涉、认知与情感表达），实证研究表明这些线索能提升用户参与度。另一方面，拟人化引发了包括欺骗、过度依赖和剥削性关系框架在内的伦理担忧，而部分学者则认为拟人化交互可能有助于促进自主性、福祉和包容性。尽管学界对该现象的关注度持续提升，相关文献仍呈现出跨领域碎片化特征，且在如何界定、操作化和规范性评估拟人化方面存在显著差异。本范围综述通过对五个数据库和三个预印本平台的系统检索，绘制了关于拟人化大语言模型对话代理的伦理导向研究图谱。我们综合梳理了：(1)概念基础，(2)伦理挑战与机遇，(3)方法论路径。研究发现：学界在基于属性定义上达成共识，但在操作化层面存在显著分歧；规范性框架以风险导向为主流；将观察到的交互效应转化为可操作的治理指导的实证研究有限。最后，我们提出未来研究议程，并为伦理化部署大语言模型对话代理中的拟人化线索提供设计与治理建议。