UX professionals routinely conduct design reviews, yet privacy concerns are often overlooked, not only due to limited tools, but more fundamentally from low intrinsic motivation, driven by limited privacy knowledge, weak empathy for unexpectedly affected users, and low autonomy in identifying harms. We present PrivacyMotiv, an LLM-powered system that generates vulnerability-centered personas, persona journey stories, and traceable design diagnoses grounded in lo-fi user flows to support privacy-oriented UX design review. In a within-subjects study with professional UX practitioners (N=16), PrivacyMotiv significantly improved empathy, intrinsic motivation, and perceived usefulness, with participants identifying 59% more privacy issues and proposing 70% more redesign solutions compared to self-proposed methods. This work contributes empirical insight into motivational barriers in privacy-aware UX and a structured, narrative-driven approach for integrating privacy review into early-stage UX practice.

翻译：UX专业人员常规进行设计审查，但隐私问题常被忽视，这不仅源于有限工具的支持，更根本地受制于低内在动机——这种动机缺失由隐私知识匮乏、对意外受影响用户共情不足以及识别危害的自主性有限共同导致。我们提出PrivacyMotiv——一种基于LLM的系统，能生成以漏洞为中心的角色画像、角色旅程故事，以及基于低保真用户流程的可追溯设计诊断，以支持面向隐私的UX设计审查。在与专业UX从业者（N=16）开展的组内实验中，PrivacyMotiv显著提升了共情能力、内在动机和感知有用性，参与者比使用自主方法时多识别出59%的隐私问题，并提出70%更多的重新设计方案。本研究为隐私感知型UX中的动机障碍提供了实证洞见，并提出一种结构化的叙事驱动方法，用于将隐私审查整合到早期UX实践中。