Jul 2026· International journal of social science and human research· 0 citations
TL;DR
GenAI is most educationally defensible when integrated as a guided formative-feedback resource rather than as a substitute writer or replacement for teacher expertise, as well as for assignment design, AI-use disclosure, feedback literacy, prompt literacy, and process-based assessment.
Abstract
Generative artificial intelligence (GenAI), particularly large language model-based tools such as ChatGPT, has rapidly entered university English as a Foreign Language (EFL) writing instruction. These tools can support brainstorming, outlining, drafting, corrective feedback, revision, and academic language refinement. Yet their use also raises concerns about over-reliance, authorship, academic integrity, assessment validity, and the changing role of teachers in writing pedagogy. This systematic literature review synthesizes recent evidence on GenAI in university EFL writing instruction using the PRISMA 2020 framework. Searches were designed for Scopus, Web of Science Core Collection, ERIC, and Education Source/EBSCOhost, covering publications from 1 November 2022 to 22 June 2026. After duplicate removal, title/abstract screening, full-text eligibility assessment, and quality appraisal, 120 studies were included in the qualitative synthesis. Narrative thematic synthesis identified six recurring themes: GenAI as a writing-process scaffold, GenAI-generated feedback, revision uptake and learner engagement, teacher-AI feedback alignment, academic integrity and authorship, and methodological limitations in the existing evidence base. The review concludes that GenAI is most educationally defensible when integrated as a guided formative-feedback resource rather than as a substitute writer or replacement for teacher expertise. Practical implications are offered for assignment design, AI-use disclosure, feedback literacy, prompt literacy, and process-based assessment.
Artificial intelligence (AI) tools are increasingly used in English as a foreign or second language (EFL/ESL) academic writing classrooms, yet evidence on their instructional use and effects remains fragmented across tools, contexts, and study designs. This systematic literature review synthesized primary empirical studies published between 2019 and 2025 and retrieved from Scopus, Web of Science, ERIC, and Google Scholar, following the PRISMA 2020 guidelines, and appraised the methodological quality of the included studies. Three research questions were addressed, concerning the AI tools used in EFL/ESL writing instruction, their effects on writing quality and skills, and the associated challenges and ethical considerations. The findings indicate that three categories of tools, automated writing evaluation and grammar-checking tools, paraphrasing tools, and generative AI are used across all stages of the writing process, frequently in combination. Their reported effects reveal a tension between consistently documented gains at the surface level of grammar, mechanics, and vocabulary and less certain gains in higher-order aspects such as content, organization, and coherence, which appear mainly in studies with stronger designs or newer generative models. Recurring concerns include student overreliance, academic integrity, and the uneven distribution of benefits across learners of differing proficiency. The review contributes an EFL-specific synthesis spanning multiple tool types and educational levels, and argues that the value of AI tools depends less on the technology itself than on how it is pedagogically mediated. Implications for teacher-guided, critically evaluated use and directions for future research are discussed.
R. Santika, Rafi Farizki· International Multidisciplin...· 0 citations
It is suggested that AI can enhance drafting, revision, and feedback processes, improving coherence, metacognition, and writing confidence, however, these benefits are accompanied by persistent concerns regarding ethical ambiguity, inconsistent policy guidance, and insufficient faculty training.
Samira Dichari, Fadi Jaber· Journal of Education and Tra...· 0 citations
Across the reviewed studies, generative AI was found to enhance language learning through personalized feedback, increased learner autonomy, and greater learning engagement, but concerns regarding academic integrity, AI literacy, ethical issues, and institutional readiness remain significant challenges to its sustainable implementation.
N. H. Hong Nhung· International journal of soc...· 0 citations
In this systematic review, the impact of the AI writing assistants on the quality of writing in English as a Foreign Language (EFL) is explored, focusing on measurable outcomes and learners' experience. The review collates data from 20 recent studies conducted between 2020 and 2026 that have examined the use of Grammarly, ChatGPT, ChatGPT-4, Gemini, Copilot, and Automated Writing Evaluation Systems. The review is based on the writing elements of PRISMA 2020 which are writing accuracy, grammar, vocabulary, coherence, cohesion, organisation, argumentation, revision behaviour, confidence and teacher mediation. The results indicate that AI writing assistants are most consistent in enhancing lower-order writing skills, particularly in the areas of grammar, mechanics, spelling, punctuation, vocabulary selection, and sentence-level clarity. Evidence for higher-order writing improvement is more conditional, generative AI can help with coherence, organisation and idea development, but learners need to be reflective when using feedback, and teachers need to guide learners through the revision process. The review also reveals methodological gaps in the literature, such as the use of small samples, short intervention periods, lack of longitudinal studies, over-representation of perception data, and inconsistencies in reporting writing assessment procedures. The findings are related to the theories of Computer-Assisted Language Learning, sociocultural, cognitive load, constructivism and self-determination theory. It posits that AI-assisted writing platforms are most useful as scaffolding tools for process-based writing instruction, not as a replacement for teacher feedback nor as a stand-alone method of writing. It is concluded that using AI in EFL writing can be guided, ethical and transparent to increase EFL writing development, and future research is needed to investigate long-term transfer, unaided writing performance and comparative feedback models. It also suggests more transparent classroom policies to help students recognise when feedback is provided via AI and when it is not.
Alcibiades Javier Lobo Mela· Latitude· 0 citations
Artificial intelligence is increasingly embedded in English language learning, but evidence on generative AI and large language models (LLMs) remains fragmented across skills, learner groups, and tool designs. This systematic review synthesized evidence on personalized learning, automated feedback, speaking, writing, vocabulary, assessment, English for specific purposes (ESP), and physical education. Reporting followed PRISMA 2020. Scopus, PubMed, and ScienceDirect returned 403 records; 14 duplicates were removed, 389 were screened, 54 reports were sought, 48 full texts were assessed, and 17 studies were included. The corpus spanned experimental, quasi-experimental, comparative, mixed-methods, qualitative, survey, review, and system-development designs. Where reported, participant samples included 40 undergraduates, 79 graduate students, and 327 primary pupils, while several design and qualitative studies did not report a single numeric sample. Narrative/thematic synthesis identified four themes: feedback and writing support; speaking and personalized tutoring; changing learner-teacher roles; and affective and ethical conditions. The evidence is recent and heterogeneous, limiting quantitative pooling. FICO was used as an internal appraisal rubric; reviewer-level logs were not retained, so Cohen’s kappa and aggregate FICO scores were not reconstructed retrospectively. Future research should prioritize controlled longitudinal designs, validated outcome measures, transparent reporting, data privacy, and under-represented ESP and sport-science populations.
Lentera Negeri, Kristian Burhan, M. Desman et al.· Lentera Negeri· 0 citations
This study systematically reviews the current evidence on AI in academic writing using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines and contributes a comprehensive synthesis of current research by integrating pedagogical, assessment, and human perspectives through the TPACK, assessment, and Technology Acceptance Model frameworks.
Saidatul Akmar Zainal Abidin, Noor Hanim binti Rahmat· International journal of res...· 0 citations
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