EFFECTIVENESS OF GENERATIVE AI WRITING ASSISTANTS ON EFL ACADEMIC WRITING IN HIGHER EDUCATION

Аннотация

Generative AI writing assistants are increasingly present in higher education and promise to augment English as a Foreign Language (EFL) academic writing by providing rapid scaffolding across idea generation, organization, language accuracy, and cohesion. This article synthesizes cognitive and sociocognitive theories of writing with emerging evidence on human–AI collaboration to articulate a practice-ready model in which AI acts as a dynamic, feedback-rich partner during prewriting, drafting, revising, and reflecting. A quasi-experimental evaluation blueprint is outlined, emphasizing rubric-anchored ratings, automated discourse indices, process analytics, and learner-reported self-regulation and ethical use. Keywords: generative AI, EFL academic writing, higher education, writing assessment, cohesion, feedback, revision, academic integrity, human–AI collaboration, instructional design. ЭФФЕКТИВНОСТЬ ГЕНЕРАТИВНЫХ ПОМОЩНИКОВ ПО НАПИСАНИЮ ТЕКСТОВ С ИСПОЛЬЗОВАНИЕМ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В АКАДЕМИЧЕСКОМ ПИСЬМЕ EFL В ВЫСШИХ УЧЕБНЫХ ЗАВЕДЕНИЯХ Аннотация. Генеративные помощники письма в высшем образовании усиливают академическое письмо на английском как иностранном, обеспечивая быстрое «скаффолдирование» идей, структуры, языковой точности и связности. В статье объединяются когнитивные и социокогнитивные модели письма с данными о сотрудничестве человека и ИИ; ИИ рассматривается как динамический источник обратной связи на этапах преднаписания, черновика, редактирования и рефлексии. Предлагается квазиэкспериментальный план оценки, сочетающий рубрикативные рейтинги, автоматические индексы дискурса, аналитику процесса и самоотчёты студентов об авторегуляции и этическом использовании. Ключевые слова: генеративный ИИ, академическое письмо EFL, высшее образование, оценивание письма, связность, обратная связь, редактирование, академическая честность, сотрудничество человек–ИИ, дизайн обучения. The accelerating diffusion of generative artificial intelligence within universities compels a re-examination of how English as a Foreign Language academic writing is cultivated, supported, and judged. A central premise of contemporary writing pedagogy is that learners improve when they can iterate rapidly between planning, drafting, and revising while receiving targeted feedback calibrated to genre expectations and disciplinary registers. Generative models now provide an always-available interlocutor that can stimulate idea generation, surface organizational patterns, propose cohesion devices, and render language-level recommendations in near real time. The mere availability of such assistance, however, does not guarantee learning: gains are contingent on how the tools are embedded within instructional designs that preserve authorship, require justification of edits, and make the revision process visible for assessment. 2 37 International scientific journal ISSN: 2181-3906 «MODERN SCIENCE АND RESEARCH» 2025 VOLUME 4 / ISSUE 10 / UIF:8.2 / MODERNSCIENCE.UZ To move beyond intuition and polemics, the present study integrates theoretical, methodological, and practical strands into a coherent account of effectiveness, asking not only whether generative systems enhance EFL academic writing but also which dimensions improve, under what patterns of use, and with which trade-offs for learner agency and integrity. A theoretical synthesis begins with cognitive process views of writing that treat composition as recursive planning, translating, and reviewing under working-memory constraints. In EFL settings, limited linguistic resources magnify these constraints, making it difficult to hold rhetorical purpose, content development, and sentence-level realization in mind simultaneously. Sociocognitive perspectives extend this view by situating writing within disciplinary discourse communities where genre moves and citation practices signal membership. Generative AI complicates yet potentially relieves these burdens: prompt-driven scaffolds can externalize planning, highlight genre-conventional structures such as problem– gap–claim sequences, and model stance-taking language that is otherwise acquired slowly through exposure. At the same time, the facility with which models produce fluent prose can mask weaknesses in argument development or evidence integration, and their probabilistic nature risks hallucinated citations if guardrails are absent. The conceptual solution advanced here is scaffolded co-authoring, an arrangement in which AI is neither banned nor allowed to ghostwrite but is constrained to roles that foster metacognition and control over discourse: eliciting research questions during prewriting, suggesting alternatives for topic sentences and transitions during revision, and providing language-level options that the learner must adjudicate with reasons. Building a credible evidence base requires designs that can disentangle the influence of access from the influence of structure. A quasi-experimental arrangement across course section