  Data storytelling is powerful for communicating data insights, but it
requires diverse skills and considerable effort from human creators. Recent
research has widely explored the potential for artificial intelligence (AI) to
support and augment humans in data storytelling. However, there lacks a
systematic review to understand data storytelling tools from the perspective of
human-AI collaboration, which hinders researchers from reflecting on the
existing collaborative tool designs that promote humans' and AI's advantages
and mitigate their shortcomings. This paper investigated existing tools with a
framework from two perspectives: the stages in the storytelling workflow where
a tool serves, including analysis, planning, implementation, and communication,
and the roles of humans and AI in each stage, such as creators, assistants,
optimizers, and reviewers. Through our analysis, we recognize the common
collaboration patterns in existing tools, summarize lessons learned from these
patterns, and further illustrate research opportunities for human-AI
collaboration in data storytelling.
