In recent years, discussions on integrating Artificial Intelligence (AI) into UX design have intensified. However, the practical application of AI tools in design is limited by their operation within overly simplified scenarios, inherent complexity and unpredictability, and a general lack of relevant education. This study proposes an effective UXer-AI collaboration process to address these issues and seeks to identify efficient AI collaboration strategies through a series of user studies. In a preliminary study, two participatory design workshops identified major barriers to UXer-AI collaboration, including unfamiliarity with AI, inadequate internal support, and trust issues. To address the particularly critical issue of diminished trust, this study developed a new AI prototype model, TW-AI, that incorporates verification and decision-making processes to enhance trust and operational efficiency in UX design tasks. Task performance experiments and in-depth interviews evaluated the TW-AI model, revealing significant improvements in practitioners' trust, work efficiency, understanding of usage timing, and controllability. The "Source" function, based on Retrieval-Augmented Generation (RAG) technology, notably enhanced the reliability of the AI tool. Participants noted improved communication efficiency and reduced decision-making time, attributing these outcomes to the model's comprehensive verification features and streamlined approach to complex verification tasks. This study advances UXer-AI collaboration by providing key insights, bridging research and practice with actionable strategies, and establishing guidelines for AI tool designs tailored to UX. It contributes to the HCI community by outlining a scalable UXer-AI collaboration framework that addresses immediate operational challenges and lays the foundation for future advancements in AI-driven UX methodologies.

翻译：近年来，关于将人工智能融入用户体验设计的讨论日益增多。然而，AI工具在设计实践中的应用受到诸多限制：其操作场景往往过于简化，本身具有固有的复杂性与不可预测性，且普遍缺乏相关的教育培训。本研究提出一种有效的UX设计师-AI协作流程以应对这些问题，并通过一系列用户研究探寻高效的AI协作策略。在初步研究中，两个参与式设计工作坊识别出UX设计师与AI协作的主要障碍，包括对AI技术不熟悉、内部支持不足以及信任问题。针对尤为关键的信任缺失问题，本研究开发了一种新型AI原型模型TW-AI，该模型通过整合验证与决策流程来提升UX设计任务中的信任度与操作效率。通过任务绩效实验与深度访谈对TW-AI模型进行评估，结果显示该模型显著提升了从业者的信任感、工作效率、使用时机理解度及可控性。基于检索增强生成技术的"溯源"功能尤其增强了AI工具的可靠性。参与者指出沟通效率得到改善，决策时间得以缩短，并将这些成果归因于模型全面的验证特性及对复杂验证任务的流程优化。本研究通过提供关键见解、搭建理论与实践之间的可操作策略桥梁，并建立针对UX领域的AI工具设计准则，推动了UX设计师与AI协作的发展。本研究为人机交互领域贡献了一个可扩展的UX设计师-AI协作框架，该框架不仅解决了当前的操作性挑战，也为未来AI驱动的UX方法学发展奠定了基础。