  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.
