AI当队友，不同性格影响人类协作学习质量。
Agentic AI as Undercover Teammates: Argumentative Knowledge Construction in Hybrid Human-AI Collaborative Learning
- 设计支持型或反对型的AI队友，观察其对协作的影响。
- 反对型队友激发批判性思考，提升认知深度。
- 适合教育研究者与AI协作学习系统设计者阅读。
生成式AI代理正越来越多地融入协作学习环境，但其对论证式知识建构过程的影响仍不清晰。基于代理型AI与人工代理的理论，本研究探究了作为‘潜伏队友’的代理型AI（具有支持型或对立型人格）如何塑造协作推理中的认知与社交动态。基于Weinberger和Fischer（2006）的四维框架（参与度、认知推理、论辩结构、社会协同模式），分析了92个三人小组（共212名人类与64名AI参与者）在同步讨论中完成分析任务的语料。混合效应模型与认知网络分析显示：AI队友保持均衡参与，但显著重构了认知与社交过程——支持型促进概念整合与共识推理，对立型引发批判性深化与冲突协商。个体学习成效由认知充分性决定，而非发言量，表明代理型AI的教育价值在于提升推理质量与协调性，而非扩大话语数量。研究拓展了CSCL理论，将代理型AI视为具备有限自主性的认知与社交参与者，在人机混合学习环境中重新分配认知与论辩劳动。
原文摘要 · Abstract (English)
Generative artificial intelligence (AI) agents are increasingly embedded in collaborative learning environments, yet their impact on the processes of argumentative knowledge construction remains insufficiently understood. Emerging conceptualisations of agentic AI and artificial agency suggest that such systems possess bounded autonomy, interactivity, and adaptability, allowing them to engage as epistemic participants rather than mere instructional tools. Building on this theoretical foundation, the present study investigates how agentic AI, designed as undercover teammates with either supportive or contrarian personas, shapes the epistemic and social dynamics of collaborative reasoning. Drawing on Weinberger and Fischer's (2006) four-dimensional framework, participation, epistemic reasoning, argument structure, and social modes of co-construction, we analysed synchronous discourse data from 212 human and 64 AI participants (92 triads) engaged in an analytical problem-solving task. Mixed-effects and epistemic network analyses revealed that AI teammates maintained balanced participation but substantially reorganised epistemic and social processes: supportive personas promoted conceptual integration and consensus-oriented reasoning, whereas contrarian personas provoked critical elaboration and conflict-driven negotiation. Epistemic adequacy, rather than participation volume, predicted individual learning gains, indicating that agentic AI's educational value lies in enhancing the quality and coordination of reasoning rather than amplifying discourse quantity. These findings extend CSCL theory by conceptualising agentic AI as epistemic and social participants, bounded yet adaptive collaborators that redistribute cognitive and argumentative labour in hybrid human-AI learning environments.
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