AI in EFL Writing Instruction: A Systematic Review of Writing Conceptualizations and Learner Agency

AI-Mediated Writing Instruction in Higher Education: A Systematic Review of Empirical Evidence

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.

AI-Integrated Genre-Based Writing Instruction: A Systematic Review and Pedagogical Framework

This review investigates how recent studies conceptualise the theoretical foundations, pedagogical practices, learning outcomes, and the integration of technology and artificial intelligence within genre-based writing instruction, and proposes an integrated framework that links the genre-based approach with emerging AI-supported writing practices.

Generative AI in University EFL Writing Instruction: A Systematic Literature Review

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.

Writing as Orchestration: Reconceptualizing the L2 Writing Construct in AI-Mediated Environments

Traditional second language (L2) writing instruction and assessment frequently emphasize unaided, timed production, a model that no longer fully represents the communicative realities of AI-mediated contexts. This conceptual article aims to reconceptualize the L2 writing construct for educational settings in which generative AI is routinely and legitimately used. The study uses a theory-driven integrative conceptual synthesis. Sources were located through purposive searching of Scopus, ERIC, Web of Science, and Google Scholar, supplemented by citation chaining and journal hand-searching, and screened against stated inclusion criteria across two streams: foundational scholarship on mediated cognition, genre, literacy, and validity, and work on generative AI and writing published from 2020 onward. Forty-seven sources were retained for close analysis, spanning sociocultural learning theory, activity theory, distributed cognition, multiliteracies research, computer-assisted language learning, and language assessment scholarship. Analysis proceeded through manual thematic coding of construct-relevant claims, conducted by the first author and independently reviewed by the second. The resulting orchestration model defines AI-mediated writing as the purposeful coordination of human judgment with machine-generated output under conditions of authorial responsibility. It specifies four interdependent competencies: prompting, critical evaluation, adaptation, and ethical accountability. The analysis shows that traditional dimensions of writing, including coherence, organization, language use, critical thinking, and audience awareness, are not displaced by AI-mediated writing but redistributed across these competencies. The paper also identifies specific challenges for L2 writers, especially the difficulty of evaluating and reshaping fluent AI-generated output in a language still being acquired. The article recommends process-visible assessment designs, genre-specific orchestration tasks, and empirical validation studies that examine construct structure, scoring reliability, and consequential validity.

Rewriting the Curriculum: Integrating AI Prompt Engineering into EFL Writing Instruction

Artificial Intelligence as a Source of Feedback in L2 Writing Classrooms: Best Practices and Pedagogical Strategies

Writing proficiency is a key element of academic success for English as a Second Language (ESL) and English as a Foreign Language (EFL) students, but it presents ongoing challenges that demand innovative pedagogical solutions. The rapid advancement of Artificial Intelligence (AI) has created unprecedented opportunities for enhancing writing instruction through automated feedback systems. This article brings together theoretical frameworks, empirical research, and pedagogical best practices to examine how AI tools can serve as effective sources of feedback in second language (L2) writing classrooms. Drawing on cognitive, sociocultural, and contrastive rhetoric theories of writing, the article explores the nature of L2 writing difficulties, the critical role of feedback in writing development, and the strengths and limitations of AI-generated feedback. A systematic review of recent empirical studies reveals that AI-powered feedback systems are consistently effective in enhancing micro-level writing skills, particularly grammatical accuracy and lexical diversity. However, limitations remain in addressing macro-level writing concerns, and hybrid feedback models that combine AI with teacher input appear to yield the most comprehensive benefits. The article presents evidence-based recommendations for teachers and students, including strategies for scaffolding AI use, developing feedback literacy, designing effective prompts, and integrating AI feedback with teacher and peer feedback. It also addresses ethical considerations and practical concerns to ensure effective use of AI tools. It concludes with implications for pedagogy and directions for future research. This article argues that the thoughtful integration of AI feedback with teacher feedback, grounded in pedagogical principles and human judgment, can significantly enhance L2 writing instruction.