人工智能辅助因果路径图,实现以人为本的设计

ArXiv Pub Date : 2024-03-12 DOI:10.1145/3613904.3642179
Ruican Zhong, Donghoon Shin, Rosemary Meza, P. Klasnja, Lucas Colusso, Gary Hsieh
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引用次数: 0

摘要

本文探讨了将因果路径图(CPD)融入以人为本的设计(HCD)的问题,研究了这些图表如何加强设计流程的早期阶段。我们为在线协作白板平台 Miro 开发了一个专用的 CPD 插件,以简化图表创建过程并提供实时的人工智能指导。通过对设计人员(20 人)进行用户研究,我们发现 CPD 的分支及其对因果联系的强调有助于设计过程中的发散和聚合过程。CPD 还能促进利益相关者之间的交流。此外,我们还发现,我们的插件大大减轻了设计师的认知工作量,提高了他们在头脑风暴过程中的创造力,突出了人工智能辅助工具在支持创造性工作和循证设计方面的意义。
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AI-Assisted Causal Pathway Diagram for Human-Centered Design
This paper explores the integration of causal pathway diagrams (CPD) into human-centered design (HCD), investigating how these diagrams can enhance the early stages of the design process. A dedicated CPD plugin for the online collaborative whiteboard platform Miro was developed to streamline diagram creation and offer real-time AI-driven guidance. Through a user study with designers (N=20), we found that CPD's branching and its emphasis on causal connections supported both divergent and convergent processes during design. CPD can also facilitate communication among stakeholders. Additionally, we found our plugin significantly reduces designers' cognitive workload and increases their creativity during brainstorming, highlighting the implications of AI-assisted tools in supporting creative work and evidence-based designs.
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