The use of Generative AI Conversational User Interfaces (CUI) as a new way to access and analyze data is growing in all sectors, and the industrial one is no exception. There, large amounts of data produced by IoT devices are flowing through user interfaces and may require them a new adaptation to the new analyses needs of decision-makers. LLM-based CUIs are promising a new way to directly interact with those data through the directness of natural language and without the learning costs that every GUI design has. Moreover, the capabilities of LLMs and their agency open up the possibility to automate some tasks and help with the reasoning during decision-making activities. But are this promises well founded? We try to scope this general question with a mixed-approach study comparing a state-of-the-art dashboard with a conversational agent. A total of 20 participants used both interfaces to complete four simulated industrial decision tasks of varying complexity. We combined measures of mental workload, completion time, and decision accuracy with a post-study questionnaire and semi-structured interviews analyzed through thematic analysis. The findings suggest that the conversational agent can reduce interactional effort by supporting more direct access to information, while the dashboard remains valuable for overview and verification. However, these benefits may vary across tasks and require validation through larger-scale studies.

翻译：生成式人工智能对话式用户界面（CUI）作为访问和分析数据的新兴方式，正广泛应用于各行业，工业领域也不例外。工业物联网设备产生的海量数据通过用户界面流动，可能要求界面为决策者新的分析需求作出适应性调整。基于大语言模型的对话式用户界面承诺通过自然语言的直接性提供与数据交互的新途径，且无需图形用户界面（GUI）设计所需的学习成本。此外，大语言模型的能力及其自主性为任务自动化与决策推理辅助开辟了可能性。但这些承诺是否成立？我们通过一项混合方法研究，将最先进的仪表盘与对话代理进行对比，以界定这一宏观问题。共20名参与者使用两种界面完成了四种不同复杂度的模拟工业决策任务。我们综合测量了脑力负荷、完成时间与决策准确性，并通过主题分析法对问卷及半结构化访谈数据进行分析。结果表明，对话代理可通过支持更直接的信息获取降低交互成本，而仪表盘在全局概览与验证方面仍具价值。但上述优势可能因任务类型而异，需通过更大规模研究加以验证。