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 utilizes a large
language model (LLM) to create generative AI virtual scenarios for user
experience. 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 a
virtual ATJ using OpenAI's GPT-4 model and AI image and video generators. Based
on the LLM-generated scripts, key visuals were created for the air taxi, and
the ATJ was evaluated by 72 participants. Furthermore, the LLM demonstrated the
ability to identify and suggest environments that significantly improve
participants' attitudes toward air taxis. Education level and gender
significantly influenced participants' attitudes and their satisfaction with
the ATJ. Our study confirms the capability of generative AI to support user
studies, providing a feasible approach and valuable insights for designing air
taxi user experiences in the early design phase.

利用大型语言模型（LLM）创建产生式人工智能虚拟场景，通过招募真实用户评估体验，收集反馈以便在早期设计阶段进行快速迭代，本研究以空中出租车为案例研究，使用 OpenAI 的 GPT-4 模型和人工智能图像、视频生成器设计了虚拟空中出租车，经过 72 名参与者的评估，LLM 展示了识别和建议显著改善参与者对空中出租车态度的环境的能力，研究还确认了产生式人工智能支持用户研究的能力，为早期设计阶段的空中出租车用户体验设计提供了可行的方法和有价值的见解。