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Arab World English Journal (AWEJ) Volume 17. Number 2, June 2026 Pp.3-18
English Department, University of Prince Mugrin, Madina, KSA.
ICT Department, University of Prince Mugrin (UPM), Madinah, KSA
This study investigated the effects of utilizing different artificial intelligence vocabulary tools on the writing performance of English as a foreign language (EFL) learners. Specifically, it examined three dimensions of writing performance: lexical diversity, lexical accuracy, and writing coherence. The study is significant as it addresses a gap in existing research by focusing on academic writing outcomes and discipline‑specific vocabulary use rather than general language improvement. This study investigated the efficacy and usability of two artificial intelligence vocabulary tools in enhancing the vocabulary of a group of undergraduate English language learners. We adopted a mixed-methods approach and collected both quantitative data from pre- and post-vocabulary assessments of the participants and qualitative data from their surveys and in-depth interviews. A total of 160 EFL students from three English composition courses participated in this study over the course of one semester. They completed 8 sessions of guided writing using the two vocabulary tools, and their perceptions of the tools’ usability and efficacy were collected and analyzed. The findings showed significant improvements in lexical diversity, vocabulary accuracy, and writing coherence. Also, they show positive perceptions of the AI tools’ usability for enhancing their vocabulary and writing skills. While there are many difficulties in vocabulary instruction using AI-powered tools, the potential is immense, and their efficacy will ultimately depend on the quality of the pedagogically driven, teacher-supported, face-to-face or digital instruction that structures their use. This article offers practical advice for teachers and researchers on classroom instruction focused on long-term vocabulary acquisition.
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Dr. Ahmed T. M. Braima is an assistant professor of English at the University of Prince Mugrin, a higher education researcher and academic practitioner whose work spans quality assurance and sustainable development in universities, as well as research in English Language learning, vocabulary acquisition, and English linguistics. ORCID: https://orcid.org/0009-0008-7303-6027
Dr. Asad Shafi is a senior lecturer of English at the University of Prince Mugrin. He has extensive experience teaching academic writing to undergraduate students. His research interests include corpus linguistics, lexis in ESP, L1 interference, language assessment, and pedagogy. ORCID: https://orcid.org/0009-0009-9277-8330
Dr. Jamal Mohamed Hussien is working as the IT Infrastructure Operations Manager in the ICT Department, UPM. His research focuses on Visual Informatics, Generative Artificial Intelligence, Large Language Model (LLM), Machine Learning (ML), Deep Learning (DL), Cybersecurity, and Digital Transformation. ORCID: https://orcid.org/0000-0002-9015-2694