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Reflection on Data Storytelling Tools in the Generative AI Era from the Human-AI Collaboration Perspective

Haotian Li, Yun Wang, Huamin Qu

TL;DR

The paper analyzes how generative AI tools reshape human-AI collaboration in data storytelling by comparing 27 latest academic tools (post Jun 2023) with earlier ones using a fine-grained collaboration framework. It applies Li et al.'s pattern framework to code human/AI roles across analysis, planning, implementation, and communication, and quantifies shifts in collaboration patterns. Findings show generative models expand the AI contribution space and enable new roles such as human-reviewers and AI-creators in communication, while traditional patterns persist. The work outlines design implications, including unified interfaces and usage guidelines, and proposes future directions to guide both academic and commercial tool development in a fast-evolving landscape.

Abstract

Human-AI collaborative tools attract attentions from the data storytelling community to lower the expertise barrier and streamline the workflow. The recent advance in large-scale generative AI techniques, e.g., large language models (LLMs) and text-to-image models, has the potential to enhance data storytelling with their power in visual and narration generation. After two years since these techniques were publicly available, it is important to reflect our progress of applying them and have an outlook for future opportunities. To achieve the goal, we compare the collaboration patterns of the latest tools with those of earlier ones using a dedicated framework for understanding human-AI collaboration in data storytelling. Through comparison, we identify consistently widely studied patterns, e.g., human-creator + AI-assistant, and newly explored or emerging ones, e.g., AI-creator + human-reviewer. The benefits of these AI techniques and implications to human-AI collaboration are also revealed. We further propose future directions to hopefully ignite innovations.

Reflection on Data Storytelling Tools in the Generative AI Era from the Human-AI Collaboration Perspective

TL;DR

The paper analyzes how generative AI tools reshape human-AI collaboration in data storytelling by comparing 27 latest academic tools (post Jun 2023) with earlier ones using a fine-grained collaboration framework. It applies Li et al.'s pattern framework to code human/AI roles across analysis, planning, implementation, and communication, and quantifies shifts in collaboration patterns. Findings show generative models expand the AI contribution space and enable new roles such as human-reviewers and AI-creators in communication, while traditional patterns persist. The work outlines design implications, including unified interfaces and usage guidelines, and proposes future directions to guide both academic and commercial tool development in a fast-evolving landscape.

Abstract

Human-AI collaborative tools attract attentions from the data storytelling community to lower the expertise barrier and streamline the workflow. The recent advance in large-scale generative AI techniques, e.g., large language models (LLMs) and text-to-image models, has the potential to enhance data storytelling with their power in visual and narration generation. After two years since these techniques were publicly available, it is important to reflect our progress of applying them and have an outlook for future opportunities. To achieve the goal, we compare the collaboration patterns of the latest tools with those of earlier ones using a dedicated framework for understanding human-AI collaboration in data storytelling. Through comparison, we identify consistently widely studied patterns, e.g., human-creator + AI-assistant, and newly explored or emerging ones, e.g., AI-creator + human-reviewer. The benefits of these AI techniques and implications to human-AI collaboration are also revealed. We further propose future directions to hopefully ignite innovations.

Paper Structure

This paper contains 10 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Surveys
  3. Method
  4. Tool Collection
  5. Pattern Coding
  6. Findings
  7. Which Patterns Are Still Widely Studied?
  8. What New Patterns Have Been Explored?
  9. Which Patterns Are Gaining More Attention?
  10. Future Opportunities

Figures (4)

  • Figure 1: This figure shows the change of human-AI collaboration pattern frequencies between the latest and earlier tools in four stages. In a subfigure, the color of blocks and numbers in the heatmap encodes the change of pattern frequencies in the stage. A pattern is represented by a human role on the horizontal axis and an AI role on the vertical axis. Green and positive numbers mean that the pattern is more frequent in the latest tools while red and negative numbers indicate that the pattern is less frequent. A number in brown means that the pattern is newly identified in the latest tools. An empty white cell means that the pattern has not emerged in any tools.
  • Figure 2: The number of data storytelling tools between 2010 and 2024
  • Figure 3: The human-AI collaboration pattern frequencies of earlier tools.
  • Figure 4: The human-AI collaboration pattern frequencies of the latest tools.