PrivacyMotiv: Speculative Persona Journeys for Empathic and Motivating Privacy Reviews in UX Design

• URL: http://arxiv.org/abs/2510.03559v1
• Date: Fri, 03 Oct 2025 23:14:22 GMT
• Title: PrivacyMotiv: Speculative Persona Journeys for Empathic and Motivating Privacy Reviews in UX Design
• Authors: Zeya Chen, Jianing Wen, Ruth Schmidt, Yaxing Yao, Toby Jia-Jun Li, Tianshi Li,
• Abstract summary: PrivacyMotiv generates speculative personas with UX user journeys centered on individuals vulnerable to privacy risks.<n>In a study with professional UX practitioners, we show significant improvements in empathy, intrinsic motivation, and perceived usefulness.<n>This work contributes a promising privacy review approach which addresses the motivational barriers in privacy-aware UX.
• Score: 21.44559961372506
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: UX professionals routinely conduct design reviews, yet privacy concerns are often overlooked -- not only due to limited tools, but more critically because of low intrinsic motivation. Limited privacy knowledge, weak empathy for unexpectedly affected users, and low confidence in identifying harms make it difficult to address risks. We present PrivacyMotiv, an LLM-powered system that supports privacy-oriented design diagnosis by generating speculative personas with UX user journeys centered on individuals vulnerable to privacy risks. Drawing on narrative strategies, the system constructs relatable and attention-drawing scenarios that show how ordinary design choices may cause unintended harms, expanding the scope of privacy reflection in UX. In a within-subjects study with professional UX practitioners (N=16), we compared participants' self-proposed methods with PrivacyMotiv across two privacy review tasks. Results show significant improvements in empathy, intrinsic motivation, and perceived usefulness. This work contributes a promising privacy review approach which addresses the motivational barriers in privacy-aware UX.

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