博士研究：邀请用户体验设计师与AI/ML从业者测试“基于LLM聊天机器人信任”的设计方法
英文摘要
A PhD researcher from Mainz University of Applied Sciences is recruiting UX designers and AI/ML practitioners to evaluate a structured method for designing interface elements that calibrate user trust in LLM-based chatbots. Participants complete an anonymous 20-30 minute online survey where they apply the method to a worked example, then rate its clarity, usefulness, and applicability and provide open feedback. The study seeks critical feedback to refine the method for the dissertation, focusing on avoiding over-reliance or under-trust. No personal data is collected beyond optional professional background questions, and no compensation is provided.
中文摘要
德国美因茨应用科学大学的一名博士研究员正在招募用户体验设计师和AI/ML从业者，以评估一种结构化设计方法，该方法用于设计界面元素以校准用户对基于LLM的聊天机器人的信任度。参与者需完成一项20-30分钟的匿名在线调查，将该方法应用于一个案例，并对其清晰度、有用性和适用性进行评分并提供开放反馈。研究旨在收集批判性意见以完善学位论文的方法，重点防止用户过度依赖或不充分信任。除可选的专业背景问题外，不收集任何个人数据，且不提供报酬。
关键要点
A PhD study at Mainz University of Applied Sciences is recruiting UX designers and AI/ML practitioners to test a design method for calibrated trust in LLM chatbots.
美因茨应用科学大学的博士研究正在招募UX设计师和AI/ML从业者，测试一种用于LLM聊天机器人校准信任的设计方法。
Participants complete a 20-30 minute anonymous online survey applying the method to a sample case and rating clarity, usefulness, and applicability.
参与者通过一项20-30分钟的匿名在线调查，将方法应用于示例案例，并评估其清晰度、有用性和适用性。
The study seeks critical feedback with no right or wrong answers; no compensation is offered and only optional professional background data is collected.
该研究寻求批判性反馈，没有正确或错误答案；不提供报酬，仅收集可选的职业背景数据。