Reflection on Data Storytelling Tools in the Generative AI Era from the Human-AI Collaboration Perspective
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ID: 283235
2025
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Abstract
Human-AI collaborative tools attract attentions from the data storytelling
community to lower the barrier of expertise 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 persistent collaboration patterns, e.g., human-creator +
AI-assistant, and emerging ones, e.g., AI-creator + human-reviewer. The
benefits of these AI techniques and other implications to human-AI
collaboration are also revealed. We further propose future directions to
hopefully ignite innovations.
| Reference Key |
qu2025reflection
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|---|---|
| Authors | Haotian Li; Yun Wang; Huamin Qu |
| Journal | arXiv |
| Year | 2025 |
| DOI |
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