Leveraging DeepL Write to Improve Writing Performance among Vocational High School Students

This research investigates how DeepL Write (henceforth DW) can address the challenge of low writing performance among vocational high school students by using an AI-assisted approach that supports more effective English writing outcomes. The aim of this research was to examine how DW was implemented in the classroom and the extent to which it enhanced students’ writing. This study adopted a quantitative quasi-experimental approach, incorporating pre- and post-tests along with Brown’s evaluation rubric to evaluate forty students assigned to control and experimental groups. The statistical analysis revealed that the p-value (2.98 × 10⁻⁹) was far below the 0.05 significance threshold, and the calculated t-statistic (10.3578) exceeded the critical two-tailed t-value (2.093). These suggest that DW offers effective real-time feedback, grammatical accuracy improvement, and vocabulary enrichment. It can serve as a powerful assistant in the classroom as an automatic writing feedback, although it should not replace traditional methods. The study recommends integrating AI-based writing tools to enhance learning outcomes while preventing student overreliance. Teachers are advised to integrate DW in a balanced manner with reflective activities to ensure that students continue to understand the thought process behind each correction provided by AI.