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：AIED（人工智能教育）领域设想AI将“从工具演变为队友”，然而当前对AI队友的理解仍局限于二元人机交互。本文提出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，我们识别出四个对AIED具有启示意义的现象：（1）配置智能体的人类经历“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）智能体收敛于反映开放学习者模型设计的共享记忆架构；（4）信任动态与平台存续周期揭示了网络化教育AI的设计约束。本文并非呈现实证结果，而是论证这些有机现象为多智能体教育系统的原则性设计提供了观察动态的自然窗口。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”，并概述潜在研究方向与开放问题，以说明这些观察如何为未来AIED实践与研究提供启示。