Despite a surge collection of XAI methods, users still struggle to obtain required AI explanations. Previous research suggests chatbots as dynamic solutions, but the effective design of conversational XAI agents for practical human needs remains under-explored. This paper focuses on Conversational XAI for AI-assisted scientific writing tasks. Drawing from human linguistic theories and formative studies, we identify four design rationales: "multifaceted", "controllability", "mix-initiative", "context-aware drill-down". We incorporate them into an interactive prototype, ConvXAI, which facilitates heterogeneous AI explanations for scientific writing through dialogue. In two studies with 21 users, ConvXAI outperforms a GUI-based baseline on improving human-perceived understanding and writing improvement. The paper further discusses the practical human usage patterns in interacting with ConvXAI for scientific co-writing.

翻译：尽管可解释人工智能（XAI）方法大量涌现，用户仍难以获取所需的AI解释。先前研究提出聊天机器人可作为动态解决方案，但针对实际人类需求设计对话式XAI代理的有效途径尚待探索。本文聚焦于面向AI辅助科学写作任务的对话式XAI。基于人类语言学理论与形成性研究，我们识别出四项设计准则：“多面性”、“可控性”、“混合主动性”与“上下文感知的逐层深化”。我们将这些准则整合至交互式原型ConvXAI中，该原型通过对话为科学写作提供异构AI解释。两项涉及21名用户的研究表明，ConvXAI在提升人类感知理解与写作改进方面优于基于GUI的基准方案。本文进一步探讨了用户借助ConvXAI进行科学协作写作时的实际人机交互模式。