Conjoint analysis is a widely used preference measurement method in marketing research, political science, healthcare, and human-computer interaction. Despite broad adoption, researchers without access to commercial platforms face significant barriers, as existing tools are either expensive or lack end-to-end survey infrastructure. This paper presents an open-source, self-hosted web application for designing, deploying, and analysing conjoint surveys. Beyond conventional tabular stimuli, the platform uses generative AI to produce integrated stimuli formats: textual scenario descriptions generated by a large language model, and visual stimuli by a text-to-image model. A researcher-defined base prompt is parameterised with the conjoint profile, and optional LLM-facing level annotations enrich the generation. A structured setup wizard, AI-assisted attribute suggestion, and live data analysis lower the technical barriers for researchers new to conjoint methodology. A full export bundle including all stimuli, their generating prompts, and response data facilitates transparency and reproducibility. The platform is demonstrated through a proof-of-concept study on care robot preferences for ambient assisted living (AAL, N=55) using AI-generated visual stimuli. The paper discusses the role of AI assistance in conjoint design, arguing that theoretical grounding must remain the researcher's responsibility, and outlining how genAI-generated stimuli can broaden the methodological repertoire for HCI and related fields.

翻译：联合分析是市场营销研究、政治学、医疗健康和人与计算机交互领域广泛应用的一种偏好测量方法。尽管已被广泛采用，但无法访问商业平台的研究人员面临重大障碍，因为现有工具要么价格昂贵，要么缺乏端到端的调查基础设施。本文介绍了一个用于设计、部署和分析联合调查的开源、自托管Web应用程序。除了传统的表格化刺激材料外，该平台还利用生成式AI生成综合刺激形式：由大语言模型生成的文本场景描述，以及由文本到图像模型生成的视觉刺激材料。研究者定义的基础提示词通过联合分析档案进行参数化，并且可选的面向大语言模型的层级注释丰富了生成内容。结构化的设置向导、AI辅助属性建议以及实时数据分析降低了联合分析新手研究者的技术门槛。一个包含所有刺激材料、其生成提示词以及响应数据的完整导出包促进了透明度和可复现性。通过一项关于环境辅助生活护理机器人偏好的概念验证研究（N=55），使用AI生成的视觉刺激材料对该平台进行了演示。本文讨论了AI辅助在联合分析设计中的作用，指出理论依据必须仍是研究者的责任，并概述了生成式AI生成的刺激材料如何拓展人机交互及相关领域的方法论工具库。