研究人与AI互动中共享理解的感知维度，揭示关键影响因素。
On the Same Page: Dimensions of Perceived Shared Understanding in Human-AI Interaction
- 通过用户调研识别出8个共享理解感知维度
- 发现用户对AI的流畅性与情境认知尤为敏感
- 适合关注AI交互体验与人机信任的研究者
共享理解在人际沟通与协作中起关键作用。随着AI日益融入人类场景，未来个人与工作场景中的人机交互（HAII）将普遍出现，而对共享理解的感知尤为重要。现有文献已探讨人际互动中感知共享理解（PSU）的过程与影响，但在人机交互中的建构仍缺乏深入研究。为更好理解HAII中的PSU，我们开展在线调查，收集用户在认为大语言模型与自身对情境理解一致或不同时的反思。通过归纳主题分析，识别出八个构成HAII中感知共享理解的维度：流畅性、操作一致性、连贯性、结果满意度、情境认知、缺乏类人能力、计算局限性及怀疑感。
原文摘要 · Abstract (English)
Shared understanding plays a key role in the effective communication in and performance of human-human interactions. With the increasingly common integration of AI into human contexts, the future of personal and workplace interactions will likely see human-AI interaction (HAII) in which the perception of shared understanding is important. Existing literature has addressed the processes and effects of PSU in human-human interactions, but the construal remains underexplored in HAII. To better understand PSU in HAII, we conducted an online survey to collect user reflections on interactions with a large language model when it sunderstanding of a situation was thought to be similar to or different from the participant's. Through inductive thematic analysis, we identified eight dimensions comprising PSU in human-AI interactions: Fluency, aligned operation, fluidity, outcome satisfaction, contextual awareness, lack of humanlike abilities, computational limits, and suspicion.
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