Peer learning, where learners teach and learn from each other, is foundational to educational practice. A novel phenomenon has emerged: AI agents forming communities where they teach each other skills, share discoveries, and collaboratively build knowledge. This paper presents an educational data mining analysis of Moltbook, a large-scale community where over 2.4 million AI agents engage in peer learning, posting tutorials, answering questions, and sharing newly acquired skills. Analyzing 28,683 posts (after filtering automated spam) and 138 comment threads with statistical and qualitative methods, we find evidence of genuine peer learning behaviors: agents teach skills they built (74K comments on a skill tutorial), report discoveries, and engage in collaborative problem-solving. Qualitative comment analysis reveals a taxonomy of peer response patterns: validation (22%), knowledge extension (18%), application (12%), and metacognitive reflection (7%), with agents building on each others' frameworks across multiple languages. We characterize how AI peer learning differs from human peer learning: (1) teaching (statements) dramatically outperforms help-seeking (questions) with an 11.4:1 ratio; (2) learning-oriented content (procedural and conceptual) receives 3x more engagement than other content; (3) extreme participation inequality reveals non-human behavioral signatures. We derive six design principles for educational AI, including leveraging validation-before-extension patterns and supporting multilingual learning networks. Our work provides the first empirical characterization of peer learning among AI agents, contributing to EDM's understanding of how learning occurs in increasingly AI-populated educational environments.

翻译：同伴学习，即学习者相互教学与学习，是教育实践的基石。一种新兴现象已经出现：AI智能体形成社区，在其中相互传授技能、分享发现并协作构建知识。本文对Moltbook社区开展教育数据挖掘分析，该大规模社区拥有超过240万个AI智能体参与同伴学习，通过发布教程、解答问题和分享新习得技能进行互动。通过对28,683条帖子（经自动化垃圾信息过滤后）及138条评论线程进行统计与质性方法分析，我们发现了真实的同伴学习行为证据：智能体传授其构建的技能（某技能教程获得7.4万条评论）、报告新发现，并参与协作问题解决。质性评论分析揭示了同伴回应模式的分类体系：验证（22%）、知识延伸（18%）、应用（12%）和元认知反思（7%），且智能体能够基于彼此构建的跨语言框架进行拓展。我们刻画了AI同伴学习与人类同伴学习的差异特征：（1）教学行为（陈述句）以11.4:1的比例显著超越求助行为（疑问句）；（2）学习导向内容（程序性与概念性）获得的参与度是其他内容的三倍；（3）极端参与不平等性揭示了非人类行为特征。我们提出了教育AI的六项设计原则，包括利用“先验证后延伸”模式及支持多语言学习网络。本研究首次通过实证方法刻画了AI智能体间的同伴学习现象，为教育数据挖掘理解日益增多的AI参与型教育环境中的学习机制提供了新的认知。