用AI分析用户研究证据，为应急人员设计低负担的数字健康方案。
From Evidence to Design: Developing an AI-Augmented UX Research Point of View for Digital Wellbeing in Emergency and Public Safety Contexts
- 结合AI与用户研究框架，挖掘应急人员的心理行为模式。
- 提出需最小化认知负荷、适应工作场景、保障心理安全的设计原则。
- 适合关注人机协同设计、公共安全系统优化的研究者参考。
本研究探讨如何将用户体验研究（UXR）方法与AI辅助分析相结合，为应急与公共安全人员（EPSP）制定更清晰的数字健康干预设计方向。EPSP在高压力、轮班制环境中易出现认知疲劳和作息不规律，难以持续使用传统健康工具。研究采用UXR观点框架（PoV），通过AI支持的文献分析识别出反复出现的心理、行为与设计模式，并融入行为改变技术与说服性技术原则，实现证据到设计的转化。最终形成一个UXR观点金字塔、九张用户研究行动卡及面向利益相关者的观点叙事。研究发现，有效的健康系统必须降低认知负担、适应实际操作情境并优先保障心理安全。该工作展示了AI在大规模证据解读中的作用，同时强调人类研究者在情境判断与设计决策中的核心责任。
原文摘要 · Abstract (English)
This paper investigates how User Experience Research (UXR) methods can be combined with AI-supported analysis to develop clearer design direction for digital wellbeing interventions targeting Emergency and Public Safety Personnel (EPSP). EPSP work in high-stress, shift-based environments where cognitive fatigue and unpredictable schedules reduce engagement with conventional wellbeing tools. Using the UXR Point-of-View (PoV) framework, this study applied an AI-supported literature analysis process to identify recurring psychological, behavioural, and design patterns. Behaviour Change Techniques and Persuasive Technology principles were integrated throughout interpretation to connect evidence with practical design reasoning. The process resulted in a UXR PoV Pyramid, nine UXR Play Cards, and stakeholder focused PoV narratives. Findings show that effective wellbeing systems for EPSP must minimise cognitive effort, adapt to operational context, and prioritise psychological safety. The work demonstrates how AI can assist large-scale evidence interpretation while human researchers maintain responsibility for contextual judgement and design direction.
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