The Assessment of the Effectiveness of AI-Generated Tools on Vocational Students’ Writing Skills
Year: 2025
Type: article
Abstract: Most vocational students at Politeknik Negeri Lhokseumawe achieve satisfactory grades in English. However, upon further investigation, they encounter challenges in writing. This phenomenon is evident in several personal statements when writing scholarship applications or assignments, although they had utilised the AI tools to support their writing. The research problem of this study is to determine the nature of AI-generated tools in enhancing writing among vocational students, with special emphasis on high-stakes application writing, e.g. applications to scholarships and internship essays. Although vocational students have a record of satisfactory performance in standardised evaluations of English, they experience considerable difficulty in producing coherent, convincing, and professionally relevant written reports required for international opportunities. This study employed an experimental research design, involving 27 students in the control group and 25 in the experimental group. The task was to investigate the effectiveness of AI-generated writing assistants such as Grammarly, SciSpace, and Jenni to bridge the gap between theoretical language knowledge and practical writing skills. Writing quality was evaluated using the Brown (2007) assessment of writing, focusing on grammar and mechanics, limited vocabulary, structural composition, genre consciousness, and rhetorical effectiveness in the work environment. The systematic analysis of the research compares the effects of AI tools on students’ skills to write error-free, context-related, and persuasive application documents. The findings have implications for the use of AI-assisted writing to prepare vocational students for global opportunities through improved application and professional writing competency.
Source: English Education Journal
Authors: Mahlil Mahlil, Ilham Jaya, Muhammad Nasir, Amru Amru
Institution: Politeknik Negeri Lhokseumawe
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $19
OpenAlex ID: W4417197575
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