对比聊天机器人与看板在工业决策中的效果，发现聊天更省力但复杂任务仍需看板。
Neither Replacement nor Panacea: Comparing LLM-Based Conversational and Graphical Decision Support in Industrial Tasks
- 用对话界面（CUI）替代看板，降低认知负荷，提升简单任务速度。
- 复杂任务下，聊天界面优势消失，准确率与看板无显著差异。
- 数据素养不影响效果，复杂决策仍需可视化支持，适合有经验的工业决策者。
制造场景中管理者依赖数字界面解读运营数据做决策，但数据量和复杂性增加使关键洞察难以高效识别。尽管看板仍是主流，基于大语言模型（LLM）的对话代理（CAs）通过对话用户界面（CUI）可能提供更直接的数据访问。本研究在2×3混合因子实验中，让134名工业决策者使用一种界面完成三项复杂度递增的任务，评估感知心理负荷（MWL）、决策准确率、完成时间及预期依赖度，并检验数据素养的调节作用。结果表明，CUI总体上降低了感知心理负荷，在低复杂度任务中加快了完成速度，但这些优势随任务复杂度上升而减弱。两种界面在决策准确率上无一致优势，且CUI不被视为后续决策的唯一依据。数据素养未显著调节界面效果。研究显示，对话交互对工业决策支持具有条件性而非普适性优势。基于LLM的对话代理可减少信息获取负担，而复杂决策仍需持续、可检查的可视化表示。
原文摘要 · Abstract (English)
Managers in manufacturing settings rely on digital interfaces to interpret operational data for decision-making, but growing data volume and complexity can make relevant insights difficult to identify efficiently. While dashboards remain dominant in industrial contexts, Large Language Model (LLM)-based conversational agents (CAs), accessed through conversational user interfaces (CUIs), may provide more direct access to such data. However, their effectiveness may depend on the information-processing demands of the task. This study compares an LLM-based CA delivered through a CUI with a dashboard in a manufacturing decision-support scenario. In a mixed factorial experiment with a 2x3 design, 134 industrial decision-makers were assigned to one interface condition and completed three tasks of increasing complexity. We examined perceived Mental Workload (MWL), decision accuracy, completion time, and intended reliance, and tested self-reported data literacy as a moderator. Results showed that the CUI reduced perceived MWL overall and supported faster completion in less demanding tasks, but both advantages diminished as task complexity increased. Neither interface produced a consistent overall advantage in decision accuracy, and the CUI was not preferred as a sole basis for subsequent decisions. Furthermore, data literacy did not reliably moderate interface effects. These findings indicate that conversational interaction offers conditional rather than universal benefits for industrial decision support. LLM-based CAs may reduce information-access effort, whereas complex decisions continue to benefit from persistent, inspectable visual representations.
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