发表机构
Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI); National Kaohsiung University of Science and Technology（德国人工智能研究中心; 国立高雄科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对现有中文学习智能体缺乏跨文化规范敏感度支持的问题，设计绿野仙踪实验与实时多模态数据采集系统，构建分层标注语料库与生态互动场景，为文化适配的中文学习对话智能体奠定基础。
AI 中文摘要
成功的跨文化交流不仅需要语法能力，还要求对根植于文化的社会规范保持敏感，违反这些规范会引发细微但有意义的非语言反应。对于学习普通话的德国学习者而言，获得这种敏感性至关重要，但现有语言学习智能体对此支持不足。我们提出了一种绿野仙踪（WoZ，即 Wizard-of-Oz，指由人暗中操控智能体行为的实验范式）研究设计及配套实时系统，用于收集中文母语者对德国学习者违反社会规范行为做出反应的多模态行为数据。该系统具备由Live Link面部捕捉和MediaPipe上半身追踪驱动的照片级真实感MetaHuman虚拟化身、用于实时行为选择的巫师控制台，以及跨智能体与学习者数据流的同步多模态日志记录。基于心理学理论的分层标注框架涵盖不可观察的社会情绪反应、规范解读、言语及可观察行为，以及未来监督目标，使该语料库能够支持未来自动化文化解读与行为生成模型的训练。由文化与教学专家共同开发的四个具有生态效度的互动场景，为面向中文学习的文化适配对话智能体提供了方法与技术基础。
英文摘要
Successful intercultural communication requires more than grammatical competence. It demands sensitivity to culturally embedded social norms whose violation triggers subtle but meaningful nonverbal responses. For German learners of Mandarin Chinese, acquiring this sensitivity is critical yet poorly supported by existing language-learning agents. We present a Wizard-of-Oz (WoZ) study design and supporting real-time system for collecting multimodal behavioral data from native Chinese speakers reacting to social norm violations by German learners. The system features a photorealistic MetaHuman avatar driven by Live Link face capture and MediaPipe upper-body tracking, a wizard console for real-time behavior selection, and synchronized multimodal logging across agent and learner streams. A layered annotation framework, based on psychological theory and covering non-observable socioemotional reactions, norm interpretation, verbal, and observable behavior thereof, and future supervision targets enables the corpus to support training of future automated cultural interpretation and behavior generation models. Four ecologically valid interaction scenarios, developed with cultural and pedagogical experts, provide the methodological and technical foundation for a culturally adapted conversational agent for Chinese language learning.
CommentsAccepted to ICMI Companion '26. 7 pages, 4 figure
