发表机构
Aarhus University; TU Wien; VRVIS GmbH（奥胡斯大学; 维也纳工业大学; VRVIS有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究通过采访14国16位专家，探讨生成式AI时代可视化仪表板的未来，明确其持续应用场景、交互与创作者职责变化，指出相关风险并提出七大研究实践方向。
AI 中文摘要
生成式AI有望让仪表板的创建变得更简单，这也引发了关于仪表板及其创建者、使用者未来走向的疑问。我们采访了来自14个国家的16位专家，了解他们的实践做法与预期。几乎所有专家都认为，仪表板会在重复性问题处理、监控和报告场景中持续存在。他们预计未来会出现自适应视图，且语言、图形控件与手势相结合的交互方式会成为主流，同时强调交互是人类探索与理解过程的一部分。参与者认为，仪表板创作者的职责将转向需求明确、生成内容梳理与输出评估，设计知识与沟通能力依然重要。创建门槛降低也引发了诸多担忧，包括验证工作量增加、用户理解不足、维护问题，以及个性化削弱共识理解等。我们探讨了研究与实践层面的七大机遇，涉及验证、终端用户教育、仪表板泛滥与腐化、组织指引、自适应、新型可视化，以及AI生成内容的问责制。我们的研究结果将仪表板的演进与维持其使用所需的人力及组织工作关联起来。
英文摘要
Generative AI promises easier dashboard creation, raising questions about the future of dashboards and the people who create and use them. We interviewed 16 experts based in 14 countries about their practices and expectations. Almost all expected dashboards to persist for recurring questions, monitoring, and reporting. They anticipated adaptive views and combinations of language, graphical controls, and gestures, while emphasizing interaction as part of human exploration and understanding. Participants expected authors' responsibilities to shift toward specifying requirements, curating generated work, and evaluating outputs, with design knowledge and communication remaining important. Easier creation also raised concerns about validation effort, users' understanding, maintenance, and personalization weakening shared understanding. We discuss seven opportunities for research and practice concerning validation, end-user education, dashboard proliferation and rot, organizational guidance, adaptation, novel visualizations, and accountability for AI-generated content. Our findings connect dashboard evolution with the human and organizational work needed to sustain their use.
