User studies are crucial for meeting user needs. In user studies, real experimental scenarios and participants are constructed and recruited. However, emerging and unfamiliar studies face limitations, including safety concerns and iterative efficiency. To address these challenges, this study utilises a Generative Artificial Intelligence (GenAI) to create GenAI-generated scenarios for user experience (UX). By recruiting real users to evaluate this experience, we can collect feedback that enables rapid iteration in the early design phase. The air taxi is particularly representative of these challenges and has been chosen as the case study for this research. The key contribution was designing an Air Taxi Journey (ATJ) using Large Language Models (LLMs) and AI image and video generators. Based on the GPT-4-generated scripts, key visuals were created for the air taxi, and the ATJ was evaluated by 72 participants. Furthermore, the LLMs demonstrated the ability to identify and suggest environments that significantly improve participants' willingness toward air taxis. Education level and gender significantly influenced participants' the difference in willingness and their satisfaction with the ATJ. Satisfaction with the ATJ serves as a mediator, significantly influencing participants' willingness to take air taxis. Our study confirms the capability of GenAI to support user studies, providing a feasible approach and valuable insights for designing air taxi UX in the early design phase.