超越摘要：面向真实世界说明文写作任务的AI支持设计
Beyond Summarization: Designing AI Support for Real-World Expository Writing Tasks
AI总结：
本文指出AI支持的说明文写作研究不足，将其特征化为基于证据和知识生成，并提出AI支持设计的三个组成部分及未来研究方向。
摘要：
大型语言模型在设计和开发新型AI辅助写作支持工具方面引入了令人兴奋的新机遇与挑战。近期研究表明，利用这一新技术能够变革诸多场景下的写作，例如创意写作过程中的构思、编辑支持以及摘要。然而，AI支持的说明文写作——包括学者撰写文献综述或医生撰写病程记录等真实世界任务——相对缺乏研究。在本立场论文中，我们论证指出，为说明文写作开发AI支持具有独特且激动人心的研究挑战，并可能带来极高的现实影响。我们将说明文写作的特征概括为基于证据和知识生成：它既包含对外部文档的摘要，也包含新信息或新知识。它可以被视为作者对一组源文档进行意义建构过程的产物，而阅读、反思与写作之间的相互作用为设计AI支持开辟了新机遇。我们勾勒了AI支持设计的三个组成部分，并讨论了未来研究的考量。
英文摘要：
Large language models have introduced exciting new opportunities and challenges in designing and developing new AI-assisted writing support tools. Recent work has shown that leveraging this new technology can transform writing in many scenarios such as ideation during creative writing, editing support, and summarization. However, AI-supported expository writing--including real-world tasks like scholars writing literature reviews or doctors writing progress notes--is relatively understudied. In this position paper, we argue that developing AI supports for expository writing has unique and exciting research challenges and can lead to high real-world impacts. We characterize expository writing as evidence-based and knowledge-generating: it contains summaries of external documents as well as new information or knowledge. It can be seen as the product of authors' sensemaking process over a set of source documents, and the interplay between reading, reflection, and writing opens up new opportunities for designing AI support. We sketch three components for AI support design and discuss considerations for future research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
