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）方法层出不穷，用户仍难以获取所需的AI解释。先前研究提出聊天机器人作为动态解决方案，但面向实际人类需求的可对话XAI代理的有效设计尚待探索。本文聚焦于面向AI辅助科学写作任务的可对话XAI。基于人类语言学理论和形成性研究，我们提炼出四项设计原则："多维度"、"可控性"、"混合主动性"及"上下文感知的逐步深入"。我们将这些原则融入交互式原型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图形用户界面的基线系统。本文进一步探讨了用户在与ConvXAI进行科学协同写作时的实际使用模式。