A Practical Study on Vocational English Teaching Based on the TPACK-SLA-AI Integrated Framework

Sige Han

doi:10.70088/j6pbth61

Authors

Sige Han Zhejiang Business and Technology Institute, Ningbo, China Author

DOI:

https://doi.org/10.70088/j6pbth61

Keywords:

tpack, second language acquisition, artificial intelligence, vocational english, language teaching

Abstract

Aiming at the problems existing in current applied English teaching in vocational colleges, such as fragmented application of technology and the disconnection between students’ competence and vocational requirements, this study constructs a four-dimensional collaborative TPACK-SLA-AI integration model. The model is grounded in the technological pedagogical content knowledge (TPACK) framework and systematically integrates second language acquisition (SLA) principles with the affordances of artificial intelligence (AI) technology. Adopting a quasi-experimental research design, the study involves 120 Applied English majors from a vocational college and implements a 16-week teaching intervention. Taking the cultivation of translation competence as the entry point, the model explores the in-depth mechanism of technology-enabled vocational English teaching through the coordinated interaction of the technology layer, pedagogy layer, content layer, and SLA support layer. Quantitative and qualitative findings reveal that the effectiveness of technology-enhanced instruction depends on the dynamic collaboration of technology, pedagogy, content, and SLA laws, rather than on technology use in isolation. The integrated framework optimizes learning processes, improves students’ vocationally oriented language performance, and enhances the alignment between classroom outcomes and workplace needs. The proposed model thus provides a feasible path with both theoretical depth and practical validity for the reform of vocational English teaching. Future research can further expand the integrated application of diverse AI tools, conduct long-term longitudinal studies, and deepen the discussion on teachers’ professional development and reflective practice in AI-supported environments.

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