Intercultural communicative competence in translation teaching mediated by artificial intelligence: Cultural schema theory, mediated discourse analysis and intercultural pragmatics

This study explores how translation teaching mediated by artificial intelligence (AI) affects learners' intercultural communicative competence (ICC) and constructs a theoretical framework based on cultural schema theory, mediated discourse analysis (MDA) and intercultural pragmatics. The study uses quantitative analysis methods, combined with hypotheses and variable definitions, to measure the relationship between the use of AI translation tools, cultural schema recognition ability, MDA ability and cross-cultural pragmatic ability. The data sources cover translation software user behavior logs, cross-cultural corpora, automatic translation evaluation and other indicators collected in the experiment. The path relationship between variables is analyzed through the structural equation model (SEM). The results show that the use of AI translation tools significantly improves learners' ICC ( p < 0.05), cultural schemas play a mediating role in cross-cultural understanding ( p < 0.01), MDA ability affects the choice of translation strategies (p < 0.01), and cross-cultural pragmatic ability enhances contextual adaptability (p < 0.01). The study further showed that AI translation optimizes translation quality and promotes the development of ICC. The data-driven intelligent translation teaching model can provide theoretical support and practical guidance for translation education and ICC training. • Links AI-assisted translation to ICC via schema, MDA, and pragmatics. • Uses multi-source data and SEM to test direct and mediated AI effects on ICC. • Finds significant ICC gains, with schema as a key mediator and improved pragmatics. • Offers a practical AI-supported translation model for ICC curricula.