When Content Complexity Meets Language Demand: AI-Mediated Cognitive Decoupling in Bilingual Professional Education
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Keywords
bilingual higher education, cognitive load theory, affective filter, AI-mediated scaffolding, second-language output, task-based language teaching, professional competence
Abstract
Bilingual professional courses occupy a structurally uncomfortable position in higher education: they demand that learners simultaneously operate at the cognitive frontier of a discipline and produce output in a language that most of them have not yet mastered. Rather than treating this as a sequencing problem to be resolved through curriculum design, this paper argues that the tension is structural and, in its conventional form, irresolvable without a deliberate mechanism that allows the two demands to be addressed independently. Drawing on cognitive load theory, Krashen's affective filter hypothesis, and Swain's output hypothesis, the paper develops the principle of cognitive decoupling, understood as the deliberate design of instructional conditions in which high-order disciplinary reasoning and structured second-language production are no longer required to compete for the same processing resources. It then argues that AI-mediated scaffolding, specifically large-language-model agents capable of differentiated real-time interaction, offers the first technically scalable mechanism through which this principle can be operationalized in resource-constrained teaching environments. Evidence from sustained action research in a bilingual International Business Negotiation course at a provincial Chinese university illustrates how each component of the decoupling framework functions in practice and what boundary conditions constrain its effectiveness. The paper closes with a set of transferable design principles for bilingual professional education in contexts where faculty capacity and student language backgrounds are heterogeneous.
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