A Multimodal AI Framework for Cross-Cultural Pragmatic Competence Analysis in Global English Communication

Driven by computational linguistics, intercultural communication, and situated cognition, this paper has proposed a research prototype of multimodal, culture-sensitive evaluation of cross-cultural pragmatic competence in English as a global language. The Multimodal Cross-cultural Pragmatic Analysis System (MCPAS) proposed combines verbal, paraverbal, and nonverbal cues through language modeling based on transformers, acoustic-prosodic analysis, and computer-vision characteristics, and applies a culture-calibration layer to condition pragmatic inference on empirically observed cultural orientations and maintain within-group variation. MCPAS is trained and tested on a culturally varied corpus of 645 participants of 27 cultural settings (872 h; 1.9 million utterances). With culture-calibrated inference, in 24-class speech-act classification, the system achieves 94.3% micro-averaged accuracy on a discussed test set, using class-balanced performance (speech-act F1 = 0.913; overall feature-recognition F1 = 0.874). The consistency with the independent expert judgments is high (weighted Cohen’s k = 0.861), and test-retest reliabilities provide high levels of reliability (ICC in general = 0.913). Mixed-methods assessment also investigates the usability of pedagogical methods using expert evaluation, teacher practice, and a small-scale student intervention, indicating that system outputs can be converted into classroom- and professional-based instructional feedback and activities. The results are presented of the corpus and settings under study, external benchmark testing, wider cultural transfer testing, and deployment testing in uncontrolled settings, which is also an important area of future research.