Integrating Artificial Intelligence into Military English for Specific Purposes Education: A Case Study at a Military University in Vietnam

This study investigates how Artificial Intelligence (AI) can enhance two mission-critical linguistic competencies in military English for Specific Purposes (ESP): (1) technical vocabulary accuracy and (2) spoken command performance under time-pressured operational conditions. A mixed-method case study was adopted to address the limited digital access and strict security requirements at the ADAFA in Vietnam. The quantitative component involved administering a 20-item questionnaire to 60 cadets, while qualitative insights were collected through semi-structured interviews with five ESP instructors. The instruments were adapted from validated AI-in-education scales and designed to capture four dimensions relevant to defense education: usefulness, motivation, linguistic performance, and technological readiness. Findings reveal that intranet-based AI tools significantly improve cadets’ accuracy in domain-specific terminology and increase confidence in delivering English command phrases. AI-generated feedback reduces speaking anxiety, while adaptive modules provide targeted practice aligned with operational tasks such as radar coordination and artillery command. However, implementation remains constrained by teachers’ limited AI literacy, security restrictions, and insufficient infrastructural support. To address these issues, the study proposes a three-layer model combining pedagogical adaptation, secure technological mediation, and institutional capacity building. The results provide a contextualized framework for integrating AI into ESP instruction in high-security military environments.

Artificial Intelligence, English for Specific Purposes, Military English

1. Akhter, E. (2022). The role of large language models (LLMs) in personalized English language instruction. International Journal of Scientific Interdisciplinary Research, 1(1), 97–128. https://doi.org/10.63125/86jf4136 [Google Scholar] [Crossref]

2. Braun, V., & Clarke, V. (2023). Thematic analysis. In H. Cooper, M. N. Coutanche, L. M. McMullen, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA handbook of research methods in psychology: Research designs: Quantitative, qualitative, neuropsychological, and biological (2nd ed., pp. 65–81). [Google Scholar] [Crossref]

3. American Psychological Association. https://doi.org/10.1037/0000319-004 [Google Scholar] [Crossref]

4. Chen, H. (2024). Innovative approaches in English language teaching: Integrating communicative methods and technology for enhanced proficiency. Communications in Humanities Research, 32, 214– 220. https://doi.org/10.54254/2753-7064/32/20240075 [Google Scholar] [Crossref]

5. Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE Publications. [Google Scholar] [Crossref]

6. Dudley-Evans, T., & St John, M. J. (1998). Developments in English for specific purposes. Cambridge University Press. [Google Scholar] [Crossref]

7. Edmett, A., Ichaporia, N., Crompton, H., & Crichton, R. (2023). Artificial intelligence and English language teaching: Preparing for the future. British Council. [Google Scholar] [Crossref]

8. He, L., Zhang, X., & Huang, Y. (2025). Integration of Artificial Intelligence in English for Specific Purposes Education. Atlantis Press. https://doi.org/10.2991/978-2-38476-400-6_68 [Google Scholar] [Crossref]

9. Mizumoto, A. (2023). Data-Driven Learning Meets Generative AI: Introducing the Framework of Metacognitive Resource Use. Applied Corpus Linguistics, 3(3), 100074. https://doi.org/10.1016/j.acorp.2023.100074 [Google Scholar] [Crossref]

10. Rashid, A. B., Kausik, A. K., Sunny, A. A. H., & Bappy, M. H. (2023). Artificial intelligence in the military: An overview of the capabilities, applications, and challenges. International Journal of Intelligent Systems, 2023(1), Article 8676366. https://doi.org/10.1155/2023/8676366 [Google Scholar] [Crossref]

11. Xu, H. (2024). Innovative research on college English teaching model based on artificial intelligence. Education Reform and Development, 6(9), 273–277. https://doi.org/10.26689/erd.v6i9.8313 [Google Scholar] [Crossref]

12. Xuan Mai, L., & Thanh Thao, L. (2022). English language teaching pedagogical reforms in Vietnam: External factors in light of teachers’ backgrounds. Cogent Education, 9(1). https://doi.org/10.1080/2331186X.2022.2087457 [Google Scholar] [Crossref]

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