VietTESOL International Convention 2026

The growing integration of artificial intelligence (AI) into mobile-assisted language learning (MALL) has opened new possibilities for personalized and flexible vocabulary development beyond the traditional classroom. This study investigates the effectiveness of AI-supported out-of-class mobile learning as a supplementary strategy for improving technical English vocabulary among A2-level cadets at the Naval Technical College, Vietnam. Given that cadets have limited access to mobile phones during formal instruction, the study positions AI-supported mobile practice not as a replacement for classroom teaching but as a structured, learner-directed tool for vocabulary reinforcement during break times and self-study periods — a design that is both institutionally manageable and pedagogically responsive to the restricted-phone-use context of military education. A quasi-experimental design was employed over six weeks with 40 cadets assigned to either a control group, which followed conventional self-study methods, or an experimental group, which additionally completed short AI-supported mobile vocabulary tasks outside class, including word explanation, technical example-sentence generation, pronunciation practice, and self-quizzing. Data were collected through pre- and post-vocabulary tests, a learner questionnaire measuring engagement and perceived usefulness, and classroom observation notes. The findings indicate that the experimental group demonstrated significantly greater vocabulary gains than the control group, alongside higher levels of learner engagement and autonomy. The study concludes that a carefully structured model of AI-supported MALL can effectively extend technical vocabulary learning beyond the classroom in specialized military education settings. Implications for technology-enhanced pedagogy, responsible AI use, and learner autonomy in technical English instruction are discussed.