UXer-AI Collaboration Process for Enhancing Trust

2025-10-13

AI Summary

AI adoption in UX design remains limited due to designers’ unfamiliarity with AI technologies, insufficient organizational support, and—most critically—a pronounced trust gap between designers and AI systems. Method: Through participatory workshops, task-based experiments, and in-depth interviews, we identified key collaboration barriers and developed TW-AI, a RAG-based prototype featuring a novel “Source” mechanism for verifiable provenance. Contribution/Results: TW-AI establishes a transparent, controllable, and scalable UXer–AI collaboration framework. Empirical evaluation demonstrates significant improvements in designers’ trust in AI, perceived task control, and decision-making efficiency; it reduces verification time, enhances capability in handling complex tasks, and improves cross-role communication. This work provides both a methodological foundation and empirical evidence for the trustworthy integration of AI into professional UX practice.

Technology Category

• Humans and AI: User Experience and Usability
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

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.

Problem

• Addressing trust issues in UX designer-AI collaboration processes
• Overcoming limited AI application due to complexity and unpredictability
• Developing verification mechanisms to enhance AI reliability in UX

Innovation

• UXer-AI collaboration process enhances trust and efficiency
• TW-AI model incorporates verification and decision-making processes
• RAG technology improves AI tool reliability and communication