Agentic AI as Undercover Teammates: Argumentative Knowledge Construction in Hybrid Human-AI Collaborative Learning

February 09, 2026

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📝 Original Info

Title: Agentic AI as Undercover Teammates: Argumentative Knowledge Construction in Hybrid Human-AI Collaborative Learning
ArXiv ID: 2512.08933
Date: 2025-10-19
Authors: Lixiang Yan, Yueqiao Jin, Linxuan Zhao, Roberto Martinez-Maldonado, Xinyu Li, Xiu Guan, Wenxin Guo, Xibin Han, Dragan Gašević

📝 Abstract

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-dim...

📄 Full Content

Generative artificial intelligence (AI) technologies are increasingly embedded in collaborative learning contexts, reshaping how students engage in discourse, reasoning, and collaborative problem solving. Within these hybrid human-AI teams, generative agents can act as more than informational tools; they can participate as quasi-teammates capable of generating, challenging, and extending ideas in real time (Shneiderman, 2020;Cukurova, 2025;Yan, Greiff, Teuber and Gašević, 2024). Decades of computer-

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