委托还是自行操作？理解混合人机界面中的用户行为
Delegating or Doing? Understanding User Behavior in Hybrid Human-Agent Interfaces
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中文总结 AI 辅助
该研究通过被试间实验对比三种交互模式，发现混合人机界面可降低交互成本，委托行为更多反映用户个体特征而非任务需求。
中文摘要 AI 辅助
大型语言模型（LLMs）正越来越多地嵌入各类应用中，使用户可通过直接操作或将任务委托给对话智能体两种方式完成任务。然而，当两种方式都可用时，用户如何在这两种模式间进行权衡，目前人们对此知之甚少。我们构建了一个基于网络的内容管理系统，通过模型上下文协议（MCP）为其添加LLM智能体，使用户可通过图形界面、对话智能体或两者结合的方式执行CRUD任务。我们开展了一项被试间研究（N=73），对比三种交互模式：仅传统模式、AI优先模式与混合模式。在16种场景中，我们分析了任务完成时间、交互日志及委托行为。AI辅助交互显著减少了点击次数、页面导航次数与滚动次数，表明交互成本降低。令人惊讶的是，这些减少并未转化为更快的任务完成速度，不同条件下的任务时长无显著差异。我们还发现CRUD操作类型与委托行为间无显著关联，表明用户并未系统性地避免委托更高风险的操作。相反，委托行为在不同参与者间的差异远大于不同任务间的差异，个体差异约占助手使用方差的一半（ICC=.50）。我们的研究结果表明，人机界面的主要益处可能在于降低交互成本而非提升速度，且委托行为更多反映用户个体特征而非任务需求。
英文摘要
Large Language Models (LLMs) are increasingly embedded into applications, allowing users to complete tasks either through direct manipulation or by delegating actions to conversational agents. However, little is known about how users balance these modalities when both are available. We present a web-based content management system augmented with an LLM agent through the Model Context Protocol (MCP), enabling users to perform CRUD tasks through a graphical interface, a conversational agent, or both. We conducted a between-subjects study (N=73) comparing three interaction modes: Traditional-Only, AI-First, and Hybrid. Across sixteen scenarios, we analyzed task completion time, interaction logs, and delegation behavior. AI-assisted interaction significantly reduced clicks, page navigations, and scrolling indicating lower interaction effort. Surprisingly, these reductions did not translate into faster task completion, as task duration did not differ significantly across conditions. We also found no significant relationship between CRUD operation type and delegation, suggesting that users did not systematically avoid delegating higher-risk actions. Instead, delegation varied far more between participants than between tasks, with individual differences accounting for roughly half the variance in assistant use (ICC = .50). Our findings suggest that the primary benefit of human--agent interfaces may be reducing interaction effort rather than improving speed, and that delegation reflects who the user is more than what the task demands.
