Collaboration is widely recognized as a cornerstone of 21st-century education, yet teachers still encounter persistent challenges in fostering productive peer interaction. LLM conversational peer agents introduce new possibilities for mediating in-person group work, raising questions about how persona design, particularly their voice characteristics, shapes learners' perceptions, trust, and interactional dynamics. While prior work has examined agent accent effects in one-to-one settings, little is known about how these effects manifest in groups. We conducted a between-subjects mixed-methods study with 33 teachers examining how a GenAI voice agent with different accents (British, Indian, and African American) influenced collaboration and agent perception. Across surveys, group interaction analyses, and artifacts, we find that accent shaped participants' mental models and the roles the agent assumed in group interaction. The British-accented agent was largely treated as a tool and engaged in detached, utility-based ways, whereas Indian- and African American-accented agents were more readily anthropomorphized and integrated as peers. These role expectations influenced trust, engagement, and reliance over time. This work advances understanding of how GenAI's sociolinguistic design features shape group dynamics in CSCL, with implications for designing culturally inclusive AI partners in group learning.

翻译：协作被广泛认为是21世纪教育的基石，但教师在促进高效同伴互动方面仍面临持续挑战。大语言模型对话同伴智能体为调解面对面小组工作带来了新的可能性，引发了对角色设计——特别是其语音特征——如何影响学习者的感知、信任与互动动态的探讨。尽管先前的研究检验了一对一场景下智能体口音效应，但关于这些效应在小组中如何显现的认知仍十分有限。我们开展了一项包含33名教师的受试者间混合方法研究，考察具有不同口音（英式、印度式及非裔美国人式）的生成式AI语音智能体如何影响协作与智能体感知。通过问卷调查、小组互动分析及人工制品评估，我们发现口音塑造了参与者的心智模型以及智能体在小组互动中所扮演的角色。英式口音智能体主要被视为工具，以疏离、功利的方式被使用；而印度式及非裔美国人式口音智能体则更易被拟人化，并作为同伴融入小组。这些角色期望随时间推移影响了信任、参与度及依赖性。本研究深化了对生成式AI社会语言学设计特征如何塑造CSCL中小组动态的理解，为设计小组学习中的文化包容性AI伙伴提供了启示。