<![CDATA[The rapid advancement of digital intelligence presents unprecedented opportunities and challenges for international Chinese language education, particularly in cultural instruction where learners often struggle with abstract concepts. Therefore, the primary objective of this study explores how Generative Artificial Intelligence (Gen AI) can be utilized to enhance the multimodal discourse competence of international Chinese learners and thereby identify innovative pathways for Chinese cultural teaching. Grounded in social semiotics and multimodal theory, this research employs an empirical inquiry through a task-driven case studies. Specifically, it examines the cognitive mechanisms underlying the cross-modal transformation between learners’ “linguistic prompt construction”and “visual semantic generation” during human-machine interactions. Empirical findings from systematically structured, multi-level multimodal cultural teaching cases indicate that the cross-modal transformation facilitated by Gen AI effectively reduce learners’ cognitive load during second language acquisition. By providing dynamic visual feedback, Gen AI assists learners in constructing rich and meaningful mental representations of Chinese culture. In conclusion, to mitigate potential algorithmic cultural biases and optimize learning outcomes, this study recommends incorporating a Gen AI interaction literacy module into the curriculum to cultivate learners’ critical competencies in human machine collaborative communication. This pedagogical paradigm has the potential to advance the digital-intelligent transformation of Chinese language education and ultimately enhance the effectiveness of global Chinese-language communication.]]>
