VietTESOL International Convention 2026

Vocabulary plays a central role in second language acquisition, yet in many Vietnamese classrooms instruction continues to rely on rote memorization, with limited opportunities for meaningful use. This study aimed to evaluate the effectiveness of Gen-AI-assisted Digital Multimodal Composing (DMC) tasks in improving vocabulary knowledge among Grade 9 EFL students in Vietnam and to explore learner perceptions of this approach.
A mixed-methods design was adopted. Thirty-six students participated in a five-session intervention, with one group completing Gen-AI-assisted DST projects and a comparison group receiving traditional instruction. Data were collected through pre- and post-tests of receptive and productive vocabulary knowledge, alongside semi-structured interviews with students in the treatment group.
Results showed that the treatment group achieved significantly greater vocabulary gains than the comparison group, with improvements evident in both receptive and productive measures. Interview data indicated that students valued AI-generated feedback and creative support, which enhanced contextual understanding and recall, though some concerns about time demands and overreliance on AI were noted.
These findings suggest that Gen-AI-assisted DST can be an effective and engaging approach to vocabulary instruction in Vietnamese secondary schools. The study contributes theoretical insights into technology-mediated vocabulary learning and offers practical implications for teachers and policymakers. Future research should involve larger samples, longer interventions, and more comprehensive assessment tools.