Artificial Intelligence in EFL and ESP Academic Writing: A Bibliometric and Critical Systematic Review of Pedagogical Gains and Assessment Risks
Year: 2026
Type: preprint
Study design: Systematic Review
Abstract: Background Artificial intelligence (AI) writing tools, from automated writing evaluation (AWE) and paraphrasers to generative chatbots, are now routine in the academic writing of learners of English as a foreign language (EFL) and English for specific purposes (ESP). The literature has expanded faster than it has been integrated: studies of learning gains, feedback, teacher attitudes and academic integrity have developed largely in parallel. Aim This review maps the structure and evolution of research on AI in EFL/ESP academic writing and critically evaluates what the evidence does and does not show, with particular attention to the relation between pedagogical benefit and assessment risk. Method A hybrid bibliometric–systematic design informed by PRISMA 2020 was applied to OpenAlex. A three-block title/abstract search (2019–2026) and a journal-scope supplementary search yielded 4,846 records; transparent rule-based screening retained 2,612 journal documents. VOSviewer 1.6.21 was used for term co-occurrence (130 terms), overlay, reference co-citation (200 references) and country co-authorship (36 countries) analyses. Bibliometric results were integrated with abstract-level coding of 2,448 records and close reading of the most influential and gap-relevant studies. Results Output grew from 6 documents in 2019 to 1,261 in 2026 (to 4 November); 79.5% appeared in 2025–2026. Six thematic clusters emerged: feedback efficacy, integrity and governance, assessment and measurement, agency and authorship, scholarly publishing by non-native writers, and technology acceptance. Only 16.1% of term-link strength connected the pedagogical clusters with the integrity/authorship clusters. The intellectual base comprised an AWE and corrective-feedback foundation, a 2023 ChatGPT opportunity–risk wave and an emerging GenAI-feedback and assessment front. Of 492 experimental or quasi-experimental studies, 9.8% reported delayed or longitudinal measures, 5.7% examined transfer to unaided writing and 3.7% were situated in ESP; only 61 records (2.5%) addressed AI-text detection. Conclusions The field documents robust, short-term text-level gains but has rarely tested durable learning, and it has not connected those gains with the assessment regimes that judge AI-assisted texts. We conceptualise this disconnection as a mediated-accuracy paradox: the linguistic polish that AI makes available to L2 writers is also a feature on which style-sensitive detectors may act. An integrative framework, five propositions and a research agenda are proposed.
Source: Research Square
Author: Zulfiya Khabirova Anvarbekovna
Institution: Ajou University in Tashkent
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7225415313