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Translation and Foreign Language Learning

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ArticleMulti-perspective Studies of Foreign Languages Learning http://dx.doi.org/10.26855/tfll.2026.01.024

Empirical Analysis of an AI-powered Teaching Model for Industry-education Integration in Academic English

Pei Liu

School of Foreign Languages, Qilu University of Technology, Shandong Academy of Social Sciences, Jinan 250353, Shandong, China.

*Corresponding author: Pei Liu

Published: January 31, 2026

Abstract

This study proposes and empirically examines an AI-powered teaching model that integrates industry-education collaboration with Academic English instruction. A quasi-experimental design was employed, in which one group of students received the new model while a second group followed conventional teaching methods. The findings indicate that the model significantly enhances students’ overall Academic English proficiency as well as their practical application skills. Key strengths include personalized learning pathways and the alignment of curriculum content with industry requirements; limitations involve insufficient teacher training in AI tools and relatively narrow industry partnerships. The paper concludes by discussing implications for instructional practice.

Keyword

AI; Academic English; Industry-Education Integration; Teaching Model; Empiri-cal Research

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Copyright

© 2026 by the author(s).
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) license, which permits non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited and is not modified or adapted.
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How to cite this paper

Empirical Analysis of an AI-powered Teaching Model for Industry-education Integration in Academic English

How to cite this paper: Pei Liu. (2026). Empirical Analysis of an AI-powered Teaching Model for Industry-education Integration in Academic English. Translation and Foreign Language Learning2(1), 146-154.

DOI: http://dx.doi.org/10.26855/tfll.2026.01.024