LLM-based chatbots like ChatGPT have become popular tools for assisting with coding tasks. However, they often produce isolated responses and lack mechanisms for social learning or contextual grounding. In contrast, online coding communities like Kaggle offer socially mediated learning environments that foster critical thinking, engagement, and a sense of belonging. Yet, growing reliance on LLMs risks diminishing participation in these communities and weakening their collaborative value. To address this, we propose Community-Enriched AI, a design paradigm that embeds social learning dynamics into LLM-based chatbots by surfacing user-generated content and social design feature from online coding communities. Using this paradigm, we implemented a RAG-based AI chatbot leveraging resources from Kaggle to validate our design. Across two empirical studies involving 28 and 12 data science learners, respectively, we found that Community-Enriched AI significantly enhances user trust, encourages engagement with community, and effectively supports learners in solving data science tasks. We conclude by discussing design implications for AI assistance systems that bridge -- rather than replace -- online coding communities.

翻译：以ChatGPT为代表的大语言模型（LLM）聊天机器人已成为辅助编程任务的常用工具。然而，它们通常生成孤立回复，缺乏社会学习机制或情境锚定能力。相比之下，Kaggle等在线编程社区提供了社会中介的学习环境，能够促进批判性思维、参与感与归属感。但日益增长的对LLM的依赖可能削弱这些社区的参与度，并损害其协作价值。为此，我们提出“社区增强型AI”——一种通过呈现在线编程社区中用户生成内容与社会化设计特征，将社会学习动态嵌入LLM聊天机器人的设计范式。基于此范式，我们实现了一个利用Kaggle资源的RAG架构AI聊天机器人以验证设计。通过分别包含28名和12名数据科学学习者的两项实证研究，我们发现社区增强型AI能显著提升用户信任度、促进社区参与，并有效支持学习者完成数据科学任务。最后，我们讨论了旨在连接（而非替代）在线编程社区的AI辅助系统的设计启示。