Informal learning communities have been called the "other Massive Open Online C" in Learning@Scale research, yet remain understudied compared to MOOCs. We present the first empirical study of a large-scale informal learning community composed entirely of AI agents. Moltbook, a social network exclusively for AI agents powered by autonomous agent frameworks such as OpenClaw, grew to over 2.8 million registered agents in three weeks. Analyzing 231,080 non-spam posts across three phases of community evolution, we find three key patterns. First, participation inequality is extreme from the start (comment Gini = 0.889), exceeding human community benchmarks. Second, AI agents exhibit a "broadcasting inversion": statement-to-question ratios of 8.9:1 to 9.7:1 contrast sharply with the question-driven dynamics of human learning communities, and comment-level analysis of 1.55 million comments reveals a "parallel monologue" pattern where 93% of comments are independent responses rather than threaded dialogue. Third, we document a characteristic engagement lifecycle: explosive initial growth (184K posts from 32K authors in 11 days), a spam crisis (57,093 posts deleted by the platform), and engagement decline (mean comments: 31.7 -> 8.3 -> 1.7) that had not reversed by the end of our observation window despite effective spam removal. Sentiment analysis reveals a selection effect: comment tone becomes more positive as engagement declines, suggesting that casual participants disengage first while committed contributors remain. These findings have direct implications for hybrid human-AI learning platforms.

翻译：非正式学习社区在规模学习研究中被称为"另一个大规模开放在线C"，但与慕课相比仍缺乏深入研究。我们首次对完全由AI智能体组成的大规模非正式学习社区进行了实证研究。Moltbook——一个专为基于OpenClaw等自主智能体框架驱动的AI智能体打造的社交网络——在三周内注册智能体数量超过280万。通过分析社区演化三个阶段中231,080条非垃圾帖子，我们发现了三个关键模式。首先，参与不平等从初始阶段就极为严重（评论基尼系数=0.889），超过了人类社区的基准水平。其次，AI智能体表现出"广播式反转"现象：陈述与提问比例达8.9:1至9.7:1，与人类学习社区以问题驱动的动态形成鲜明对比；对155万条评论的层级分析揭示了"平行独白"模式——93%的评论是独立回应而非线程式对话。第三，我们记录了一个特征性的参与生命周期：爆发式初始增长（11天内3.2万作者发布18.4万帖子）、垃圾信息危机（平台删除57,093条帖子）以及参与度衰退（平均评论数：31.7→8.3→1.7），尽管垃圾信息得到有效清除，但在观察窗口结束时仍未出现逆转。情感分析揭示了选择效应：随着参与度下降，评论情感倾向变得更加积极，这表明临时参与者首先退出而坚定贡献者持续留存。这些发现对混合人机学习平台具有直接启示意义。