分析生成式AI时代人机协作的数据叙事工具演进
Reflection on Data Storytelling Tools in the Generative AI Era from the Human-AI Collaboration Perspective
- 构建框架对比新旧工具的人机协作模式
- 发现主流模式从人工主导转向AI生成+人工审核
- 适合关注AI辅助创作与协同设计的研究者
人机协作工具受到数据叙事领域关注，旨在降低专业门槛并优化工作流程。近年来，大规模生成式AI技术（如大语言模型LLMs和文本转图像模型）的进展，使其在视觉生成与叙述创作方面展现出巨大潜力。自这些技术公开发布两年以来，有必要反思其应用进展，并展望未来机遇。为此，我们基于专门设计的框架，比较了最新工具与早期工具在数据叙事中的人机协作模式。通过对比，识别出持续受关注的主流模式，如‘人工创作者 + AI助手’，以及新兴探索的模式，如‘AI创作者 + 人工评审者’。同时揭示了这些技术带来的优势及其对人机协作的影响。最后提出若干未来方向，以期激发创新。
原文摘要 · Abstract (English)
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.
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