Author(s)

Xinyu Gao

Corresponding Author

Xinyu Gao

Abstract

In second language instruction, vocabulary knowledge is widely recognized as a multidimensional construct that encompasses lexical frequency, register variation, derivational morphology, grammatical function, and contextual collocation. However, traditional vocabulary teaching methods often focus narrowly on semantic memorization and form recognition, lacking an integrative approach across dimensions. To address this gap, the present study proposes a comprehensive instructional framework that integrates AI-based feedback systems, corpus-informed tools, and cross-cultural task design. Anchored in three core cognitive mechanisms—form–meaning integration, contextualization, and usage activation—the framework aims to facilitate personalized support and dynamic lexical transfer. Findings suggest that the model effectively enhances learners ’ accuracy in collocation use, sensitivity to register, and command of derivational structures. Notably, it demonstrates clear advantages in fostering polysemy comprehension and contextual adaptability. This study contributes a new perspective for unifying theoretical models of vocabulary knowledge with technological mediation in language pedagogy, while also laying the groundwork for future empirical exploration into its cross-cultural applicability and long-term instructional outcomes.

Keywords

Multidimensional Vocabulary Knowledge, Corpus-Assisted Instruction, Ai Feedback Tools, Task-Based Language Teaching, Contextual Adaptability, Multilingual Pedagogical Framework