研究对话式搜索中用户的认知模型与界面透明度的关系
Understanding Mental Models of Generative Conversational Search and The Effect of Interface Transparency
- 通过对比四种透明度不同的对话界面，观察用户认知模型
- 多数用户模型过于抽象，难以解释具体搜索行为
- 透明度提升有助于建立正确信任，适合人机交互设计者参考
对话式搜索的体验与采纳程度取决于用户认知模型的准确性和完整性——即用户对系统行为的理解和预测框架。理解这些模型有助于发现设计改进点。透明度是一种可提升系统可解释性并促进认知模型对齐的干预手段。尽管过去研究关注传统搜索引擎的认知模型，但生成式对话式搜索的认知模型仍缺乏探索，而这类系统使用率正在迅速上升。为此，我们对16名参与者进行了研究，让他们在四种不同透明度水平的对话界面中完成4项搜索任务。分析显示，大多数用户的认知模型过于抽象，无法支持其解释单个搜索实例。结果表明：1）认知模型可能成为用户合理信任对话式搜索的障碍；2）混合网页-对话式搜索是未来搜索界面设计的一个有前景的新方向。
原文摘要 · Abstract (English)
The experience and adoption of conversational search is tied to the accuracy and completeness of users' mental models -- their internal frameworks for understanding and predicting system behaviour. Thus, understanding these models can reveal areas for design interventions. Transparency is one such intervention which can improve system interpretability and enable mental model alignment. While past research has explored mental models of search engines, those of generative conversational search remain underexplored, even while the popularity of these systems soars. To address this, we conducted a study with 16 participants, who performed 4 search tasks using 4 conversational interfaces of varying transparency levels. Our analysis revealed that most user mental models were too abstract to support users in explaining individual search instances. These results suggest that 1) mental models may pose a barrier to appropriate trust in conversational search, and 2) hybrid web-conversational search is a promising novel direction for future search interface design.
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