# Toward a Culturally Adapted Chinese Language Agent: A Wizard-of-Oz Study of Nonverbal Behavior in Chinese-German Intercultural Interaction

arXiv: https://arxiv.org/abs/2609.35150 · HTML: https://mlsanity.com/paper/2609.35150/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 28 Sep 2026.

## Builds on (most cited first, 6 of 6)

- [A High-Fidelity Open Embodied Avatar with Lip Syncing and Expression Capabilities](https://mlsanity.com/paper/1909.08766/) (2019)
- [Towards the generation of synchronized and believable non-verbal facial behaviors of a talking virtual agent](https://mlsanity.com/paper/2311.12804/) (2023)
- [Multilingual Dyadic Interaction Corpus NoXi+J: Toward Understanding Asian-European Non-verbal Cultural Characteristics and their Influences on Engagement](https://mlsanity.com/paper/2409.13726/) (2024)
- [On LLM Wizards: Identifying Large Language Models' Behaviors for Wizard of Oz Experiments](https://mlsanity.com/paper/2407.08067/) (2024)
- [Wizundry: A Cooperative Wizard of Oz Platform for Simulating Future Speech-based Interfaces with Multiple Wizards](https://mlsanity.com/paper/2304.08693/) (2023)
- [Wizard of Oz Experimentation for Language Technology Applications: Challenges and Tools](https://mlsanity.com/paper/2402.14563/) (2024)

## Abstract (arXiv)

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.