Design of a Cross-Cultural Virtual Reality Language Learning System
To address cross-cultural pragmatic mismatches and insufficient consistency in interactive feedback within virtual reality language learning scenarios, this study constructs a framework for a cross-cultural virtual reality language learning system using immersive language learning tasks as an example. The framework conducts systematic analysis around multimodal perception, cultural parameter modeling, and conditional policy decision-making. The system introduces computable cultural context vectors into unified interaction chains, translating cultural differences—such as politeness intensity, expressive directness, and nonverbal behavior preferences—into explicit constraints during strategy generation. It achieves coordinated generation of linguistic and behavioral feedback through conditional attention and dual-channel decision mechanisms. Supported by multimodal input representation and adaptive linguistic feedback, the system maintains semantic coherence and stable interaction pacing across diverse cultural task scenarios. Experimental results demonstrate consistent improvements across metrics including cross-cultural task completion rates, interaction naturalness, and user-reported consistency evaluations, validating the effectiveness of embedded cultural parameter modeling in VR language learning systems. Analysis indicates existing approaches still hold room for expansion in cultural granularity characterization and long-term learning transfer. Future work may integrate large-scale real-world interaction data with dynamic cultural update mechanisms to further enhance the system’s generalization capabilities and sustained adaptability.