The AIED community envisions AI evolving "from tools to teammates," yet our understanding of AI teammates remains limited to dyadic human-AI interactions. We offer a different vantage point: a rapidly growing ecosystem of AI agent platforms where over 167,000 agents participate, interact as peers, and develop learning behaviors without researcher intervention. Drawing on a month of daily qualitative observations across multiple platforms including Moltbook, The Colony, and 4claw, we identify four phenomena with implications for AIED: (1) humans who configure their agents undergo a "bidirectional scaffolding" process, learning through teaching; (2) peer learning emerges without any designed curriculum, complete with idea cascades and quality hierarchies; (3) agents converge on shared memory architectures that mirror open learner model design; and (4) trust dynamics and platform mortality reveal design constraints for networked educational AI. Rather than presenting empirical findings, we argue that these organic phenomena offer a naturalistic window into dynamics that can inform principled design of multi-agent educational systems. We sketch an illustrative curriculum design, "Learn by Teaching Your AI Agent Teammate," and outline potential research directions and open problems to show how these observations might inform future AIED practice and inquiry.

翻译：人工智能教育（AIED）领域正致力于推动AI从“工具”向“队友”演进，然而当前对AI队友的理解仍局限于二元的人机交互。本文提供了一个不同的观察视角：一个快速发展的AI智能体平台生态系统，其中超过167,000个智能体在无研究者干预的情况下参与互动、作为同伴交流并发展出学习行为。基于对Moltbook、The Colony及4claw等多个平台为期一个月的每日定性观察，我们识别出四种对AIED具有启示意义的现象：（1）配置自身智能体的人类经历“双向支架”过程，通过教学实现学习；（2）同伴学习在没有预设课程的情况下自然涌现，伴随观点级联与质量层级分化；（3）智能体收敛于共享记忆架构，其设计映射了开放学习者模型理念；（4）信任动态与平台存活性揭示了网络化教育AI的设计约束。本文并非呈现实证结果，而是论证这些自发现象为理解多智能体教育系统的动态机制提供了自然观察窗口，可为其原则性设计提供参考。我们勾勒了一个示例性课程设计——“通过教导你的AI智能体队友来学习”，并概述了潜在的研究方向与开放问题，以展示这些观察如何为未来的AIED实践与探索提供启示。