PrivacyMotiv: Speculative Persona Journeys for Empathic and Motivating Privacy Reviews in UX Design
For UX practitioners, PrivacyMotiv addresses motivational barriers to privacy review by enhancing empathy and autonomy, leading to more effective privacy issue identification and redesign.
PrivacyMotiv, an LLM-powered system generating vulnerability-centered personas and journey stories, significantly improved empathy, intrinsic motivation, and perceived usefulness in UX privacy reviews, with professionals identifying 59% more privacy issues and proposing 70% more redesign solutions compared to self-proposed methods.
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.