The Implementation of Vocabulary Assessment Based on Deep Learning Approach

This research explores the application of deep learning technology in vocabulary assessment within English language instruction using a qualitative descriptive method. Data were obtained from one English teacher and twenty senior high school students through classroom observation, semi-structured interviews, and document review. The participants were purposively chosen based on their familiarity with AI-powered platforms such as Duolingo, Quizizz, and Quizlet. The qualitative data were analyzed using NVivo 12 software, employing thematic coding and word cloud visualizations. The results show that deep learning elements such as adaptive testing, individualized feedback, and real-time correction have been effectively integrated into vocabulary learning. Both the teacher and students found these technologies to be useful, reliable, and engaging. Nonetheless, several obstacles remain, including varying levels of student digital readiness, infrastructural constraints, and disparities in access, especially in formal educational settings. Despite these limitations, the study affirms the strong potential of deep learning to enrich vocabulary assessment by promoting interactive and learner-centered experiences. To support widespread and equitable adoption, the study recommends institutional support, infrastructure enhancement, and teacher professional development. These insights reinforce the relevance of AI in advancing precision learning and contemporary formative assessment models.