Design and Practice of "AI+" Higher Vocational English Courses Based on Cognitive Load Theory

Abstract

The development and integration of "AI+" English courses within higher vocational education represent a critical direction for advancing vocational education frameworks and enhancing pedagogical efficacy. While artificial intelligence provides vast digital resources, interactive tools, and immersive virtual environments for language learning, the inappropriate selection of these resources or a lack of structured summarization and knowledge internalization can easily lead to severe cognitive overload for students. To address this challenge, this study introduces cognitive load theory—specifically categorizing cognitive load into internal, external, and germane cognitive loads—as a comprehensive analytical framework. This framework is utilized to systematically explore how to optimally balance these three distinct types of cognitive load through meticulous and innovative course design. Through the implementation of three sets of rigorously controlled experiments, the research empirically demonstrates that "AI+" higher vocational English courses, which strategically combine traditional classroom teaching methodologies with advanced AI-assisted technologies, yield significant educational benefits. Specifically, this blended approach can effectively reduce extraneous external load, minimize unnecessary internal load, and substantially enhance the germane cognitive load, thereby facilitating deeper learning. The detailed instructional course design principles and the practical case studies of "AI+" higher vocational English courses presented in this paper offer valuable insights. Ultimately, these findings can serve as a robust foundational reference and practical guide for the future development and optimization of the broader "AI+" series of higher vocational educational programs.