  Peer review is a cornerstone of science. Research communities conduct peer
reviews to assess contributions and to improve the overall quality of science
work. Every year, new community members are recruited as peer reviewers for the
first time. How could technology help novices adhere to their community's
practices and standards for peer reviewing? To better understand peer review
practices and challenges, we conducted a formative study with 10 novices and 10
experts. We found that many experts adopt a workflow of annotating,
note-taking, and synthesizing notes into well-justified reviews that align with
community standards. Novices lack timely guidance on how to read and assess
submissions and how to structure paper reviews. To support the peer review
process, we developed ReviewFlow -- an AI-driven workflow that scaffolds
novices with contextual reflections to critique and annotate submissions,
in-situ knowledge support to assess novelty, and notes-to-outline synthesis to
help align peer reviews with community expectations. In a within-subjects
experiment, 16 inexperienced reviewers wrote reviews in two conditions: using
ReviewFlow and using a baseline environment with minimal guidance. With
ReviewFlow, participants produced more comprehensive reviews, identifying more
pros and cons. While participants appreciated the streamlined process support
from ReviewFlow, they also expressed concerns about using AI as part of the
scientific review process. We discuss the implications of using AI to scaffold
the peer review process on scientific work and beyond.
