AI 中文总结
研究通过对153名频繁使用聊天机器人的用户调查，发现人机交互中信任与验证无关联，三种用户端实践与满意度正相关，揭示了两种监督区别及差距，重新构建用户端监督为常规认知治理并得出对话式人工智能的四个设计方向。
AI 中文摘要
长期以来，关于人机交互的研究将系统输出验证视为一种依赖信任的行为，认为更高校准的信任应减少这种行为。我们通过对153名频繁使用聊天机器人的用户进行混合方法调查，在日常人机对话交互中测试了这一假设。与典型预测相反，我们发现信任与验证之间没有可检测到的关联，且该结果在敏感性分析中具有稳健性。另外三种用户端实践——自动化操作前的细化、纠正和批准——得到广泛认可，并与满意度呈正相关。数据揭示了评估性监督（与信任脱钩，与满意度弱相关）和干预性监督（与信任弱相关，与满意度强相关）之间的实质性区别。中到较大的满意度控制差距表明，有效的任务结果不会产生自主感。定性研究结果确定了工具性心理模型、特定故障模式的怀疑以及对认知基础设施的需求。我们将用户端监督重新构建为与信任兼容的常规认知治理，并得出了对话式人工智能中支架式监督的四个设计方向。
英文摘要
Research on human-AI interaction has long framed verification of system outputs as a trust-contingent behavior that better-calibrated trust should reduce. We test this assumption in everyday human-chatbot interaction through a mixed-methods survey of 153 frequent chatbot users. Contrary to the canonical prediction, we find no detectable association between trust and verification, with the result robust across sensitivity analyses. Three further user-side practices - refinement, correction, and approval before automated actions - are widely endorsed and positively associated with satisfaction. The data reveal a substantive distinction between evaluative oversight (trust-decoupled, weakly tied to satisfaction) and interventionist oversight (weakly trust-correlated, strongly tied to satisfaction). A medium-to-large satisfaction-control gap shows that effective task outcomes do not produce a felt sense of agency. Qualitative findings identify instrumental mental models, failure-mode-specific doubt, and demand for epistemic infrastructure. We reframe user-side oversight as routine epistemic governance compatible with trust, and derive four design directions for scaffolded oversight in conversational AI.
