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Large language models have introduced exciting new opportunities andchallenges in designing and developing new AI-assisted writing support tools.Recent work has shown that leveraging this new technology can transform writingin many scenarios such as ideation during creative writing, editing support,and summarization. However, AI-supported expository writing--includingreal-world tasks like scholars writing literature reviews or doctors writingprogress notes--is relatively understudied. In this position paper, we arguethat developing AI supports for expository writing has unique and excitingresearch challenges and can lead to high real-world impacts. We characterizeexpository writing as evidence-based and knowledge-generating: it containssummaries of external documents as well as new information or knowledge. It canbe seen as the product of authors' sensemaking process over a set of sourcedocuments, and the interplay between reading, reflection, and writing opens upnew opportunities for designing AI support. We sketch three components for AIsupport design and discuss considerations for future research.

Beyond Summarization: Designing AI Support for Real-World Expository Writing Tasks