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arXiv 2401.02494cs.HC
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推进生成式人工智能的图形用户界面:通过任务创造性与复杂性绘制人机交互设计空间

Advancing GUI for Generative AI: Charting the Design Space of Human-AI Interactions through Task Creativity and Complexity

Zijian Ding

AI总结:

本文提出一项研究计划,通过考察不同创造性与复杂性任务中的人机交互模式,为生成式AI系统的图形用户界面设计提供指导,并计划扩展至复杂数据分析结果的可解释叙述化。

摘要:

技术进步持续塑造着任务执行中人机交互的动态。为应对生成式人工智能的进展,本文概述了一项详细的研究计划,该计划考察一系列具有不同创造性和复杂性水平的任务中的多种人机交互模式。这一探索旨在为有效集成并增强生成式人工智能系统能力的图形用户界面(GUI)的开发提供信息并做出贡献。研究包含三个部分:通过新闻标题生成探索固定范围任务,通过类比生成深入原子创造性任务,以及通过数据可视化研究复杂任务。未来工作旨在将这一探索扩展至将复杂数据分析结果线性化为更广泛受众可理解的叙述,从而增强人工智能生成内容的可解释性。

英文摘要:

Technological progress has persistently shaped the dynamics of human-machine interactions in task execution. In response to the advancements in Generative AI, this paper outlines a detailed study plan that investigates various human-AI interaction modalities across a range of tasks, characterized by differing levels of creativity and complexity. This exploration aims to inform and contribute to the development of Graphical User Interfaces (GUIs) that effectively integrate with and enhance the capabilities of Generative AI systems. The study comprises three parts: exploring fixed-scope tasks through news headline generation, delving into atomic creative tasks with analogy generation, and investigating complex tasks via data visualization. Future work aims to extend this exploration to linearize complex data analysis results into narratives understandable to a broader audience, thereby enhancing the interpretability of AI-generated content.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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