Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration
It provides a framework for researchers to reflect on tool designs in data storytelling, but it is incremental as it reviews existing work without introducing new methods or data.
This paper conducted a systematic review of data storytelling tools from a human-AI collaboration perspective, analyzing them based on workflow stages and roles to identify common patterns and research opportunities.
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.