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：基于大型语言模型（LLM）的聊天机器人（如ChatGPT）已成为辅助编程任务的流行工具。然而，其回应往往孤立生成，缺乏社会学习机制或情境基础。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、参与感和归属感。但日益增长的对LLM的依赖可能削弱这些社区的参与度并削弱其协作价值。为此，我们提出“社区增强型AI”这一设计范式，通过呈现在线编程社区中用户生成的内容与社会化设计特征，将社会学习动态嵌入基于LLM的聊天机器人中。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。通过分别涉及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显著提升了用户信任度，促进了社区参与，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（而非取代）在线编程社区的AI辅助系统的设计启示。