Virtual Reality (VR) systems collect fine-grained behavioral and biometric data, yet privacy policies are rarely read or understood due to their complex language, length, and poor integration into users' interaction workflows. To lower the barrier to informed consent at the point of choice, we explore a Large Language Model (LLM)-powered privacy assistant embedded into a VR app store to support privacy-aware app selection. The assistant is realized in two interaction modes: a text-based chat interface and an embodied virtual avatar providing spoken explanations. We report on an exploratory within-subjects study $(N = 21)$ in which participants browsed VR productivity applications under unassisted and assisted conditions. Our findings suggest that both interaction modes support more deliberate engagement with privacy information and decision-making, with privacy scores primarily functioning as a veto mechanism rather than a primary selection driver. The impact of embodied interaction varied between participants, while textual interaction supported reflective review.

翻译：虚拟现实（VR）系统收集细粒度的行为与生物特征数据，然而隐私政策因其语言复杂、篇幅冗长且与用户交互流程脱节，鲜少被阅读或理解。为降低用户在决策点获取知情同意的门槛，我们探索了一种基于大型语言模型（LLM）的隐私助手，将其嵌入VR应用商店以支持隐私感知的应用选择。该助手通过两种交互模式实现：基于文本的聊天界面，以及提供语音解释的具身虚拟化身。我们报告了一项探索性被试内研究（N = 21），参与者在无辅助与有辅助条件下浏览VR生产力应用。研究结果表明，两种交互模式均能促进用户更审慎地关注隐私信息并参与决策过程，其中隐私评分主要发挥否决机制的作用，而非首要选择驱动因素。具身交互的影响在参与者间存在差异，而文本交互则有助于反思性审查。