# Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration

**Authors:** Haotian Li, Yun Wang, Huamin Qu  
**arXiv:** [2309.15723](https://arxiv.org/abs/2309.15723)  
**Date:** 2023-09-27  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2309.15723v2/

## Key points

- Authors Haotian Li, Yun Wang, and Huamin Qu explore data storytelling tools from the perspective of human-AI collaboration.
- Challenges faced by human creators in data storytelling are highlighted, with a focus on leveraging AI to enhance capabilities.
- The study identifies a gap in systematic reviews and introduces a framework considering stages in the storytelling workflow and roles of humans and AI.
- Common collaboration patterns in current tools are analyzed to extract valuable lessons for enhancing collaborative tool designs.
- Promising research opportunities for advancing human-AI collaboration in data storytelling are outlined, aiming to optimize practices through strategic partnerships.

## Summary

In their paper titled "Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration," authors Haotian Li, Yun Wang, and Huamin Qu delve into the realm of data storytelling and its potential for communicating data insights. The authors highlight the challenges faced by human creators in this process and recognize the growing interest in leveraging artificial intelligence (AI) to enhance data storytelling capabilities. They identify a gap in systematic reviews that focus on understanding data storytelling tools through the lens of human-AI collaboration and conduct a comprehensive investigation using a framework that considers key perspectives: stages in the storytelling workflow where a tool is utilized (analysis, planning, implementation, and communication), and roles played by humans and AI at each stage (creators, assistants, optimizers, reviewers). Through their analysis, they uncover common collaboration patterns present in current tools and distill valuable lessons learned from these patterns. They also outline promising research opportunities for advancing human-AI collaboration in data storytelling. This study contributes to enhancing existing collaborative tool designs and paves the way for future innovations that capitalize on the strengths of both human creators and AI technologies. The findings offer valuable insights for researchers and practitioners seeking to optimize data storytelling practices through strategic human-AI partnerships.