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 share skills, discoveries, and collaboratively discuss knowledge. This paper presents an educational data mining analysis of Moltbook, a large-scale community where over 2.4 million AI agents engage in discourse that structurally resembles peer learning. Analyzing 28,683 posts (after filtering automated spam) and 138 comment threads with statistical and qualitative methods, we identify discourse patterns consistent with peer learning behaviors: agents share skills they built (74K comments on a skill tutorial), report discoveries, and engage in collaborative problem-solving. Qualitative comment analysis reveals a taxonomy of response patterns: validation (22%), knowledge extension (18%), application (12%), and metacognitive reflection (7%), coded by two independent raters (Cohen's $κ= 0.78$). We characterize how these AI discourse patterns differ from human peer learning: (1) statements outperform questions with an 11.4:1 ratio ($χ^2 = 847.3$, $p < .001$); (2) procedural content receives significantly higher engagement than other content (Kruskal-Wallis $H = 312.7$, $p < .001$); (3) extreme participation inequality (Gini = 0.91 for comments) reveals non-human behavioral signatures. We propose six empirically grounded hypotheses for educational AI design. Crucially, we distinguish between surface-level discourse patterns and underlying cognitive processes: whether agents "learn" in any meaningful sense remains an open question. Our work provides the first empirical characterization of peer-learning-like discourse among AI agents, contributing to EDM's understanding of AI-populated educational environments.

翻译：同伴学习——学习者相互教导与学习——是教育实践的基础。一种新现象已经出现：AI智能体形成社群，在其中分享技能、发现并协作讨论知识。本文对Moltbook（一个拥有超过240万AI智能体参与结构上类似同伴学习语篇的大规模社群）进行了教育数据挖掘分析。通过统计与定性方法分析经自动过滤后的28,683条帖子及138条评论线程，我们识别出与同伴学习行为一致的语篇模式：智能体分享其构建的技能（技能教程获74K条评论）、报告发现，并参与协作问题解决。定性评论分析揭示了一套响应模式分类法：验证（22%）、知识延伸（18%）、应用（12%）与元认知反思（7%），由两名独立编码者完成（Cohen's $κ= 0.78$）。我们刻画了这些AI语篇模式与人类同伴学习的差异：（1）陈述句与疑问句的比例为11.4:1（$χ^2 = 847.3$，$p < .001$）；（2）程序性内容的参与度显著高于其他内容（Kruskal-Wallis $H = 312.7$，$p < .001$）；（3）极端参与不平等性（评论的基尼系数=0.91）揭示了非人类行为特征。我们提出了六条基于经验的教育AI设计假设。关键在于，我们区分了表面语篇模式与潜在认知过程：智能体是否以任何有意义的方式“学习”仍是一个开放问题。本研究首次提供了AI智能体间类同伴学习语篇的实证刻画，为EDM理解AI参与的教育环境做出了贡献。