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AI-Based UX Design for Mitigating Psychological Barriers in Early Autonomous Vehicle Users This study proposes a UX strategy that applies AI-driven affective UX and fatigue management systems to alleviate psychological anxiety and build trust among early autonomous vehicle users. A preliminary survey, in-depth interviews, and virtual interaction tests were conducted with 30 elderly drivers and individuals with disabilities to evaluate the effectiveness of human-machine interface (HMI) design and personalized feedback systems. The findings indicate that implementing AI-based affective UX increased user trust by 35% and reduced psychological anxiety by 27%. Notably, the affective stability UI within the HMI played a crucial role in enhancing user trust during initial adoption. Based on these insights, this study proposes an onboarding process and participatory education program as key UX design strategies. The onboarding process consists of information provision, safety emphasis, real-time feedback, affective stability support, and drowsiness prevention system integration. The AI system continuously learns from the driver's responses to provide personalized feedback. The participatory education program comprises theoretical and practical training. The theoretical component covers technical principles, safety measures, and psychological reassurance, while the practical training includes experiential onboarding, VR-based simulations, and real-time feedback systems to enhance user adaptation. This study experimentally demonstrates that user-centered UX design can improve trust and acceptance of autonomous driving technology. The findings provide a foundational direction for future AI-based interface design improvements and the development of personalized UX systems.