Aug 2026· Comprehensive Journal of Science· 0 citations· 35 references
TL;DR
It is argued that the thoughtful integration of AI feedback with teacher feedback, grounded in pedagogical principles and human judgment, can significantly enhance L2 writing instruction.
Abstract
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.
Artificial intelligence has introduced new possibilities for academic writing instruction through immediate feedback, personalized language support, interactive revision, and assistance across different stages of the writing process. Generative artificial intelligence systems can identify linguistic and organizational weaknesses, explain academic conventions, offer revision questions, and provide examples suited to learners’ proficiency levels. Nevertheless, the educational value of such systems depends on how they are incorporated into teaching. Uncritical reliance on AI may reduce independent thinking, weaken authorial voice, generate inaccurate information, and create ethical concerns involving privacy, academic integrity, authorship, and equitable access. This conceptual paper examines the role of artificial intelligence in academic writing instruction, with particular attention to innovations in formative feedback and language development. It employs an integrative review methodology to analyse recent scholarship on generative AI, automated writing evaluation, second-language writing, feedback literacy, and AI-supported teaching. The discussion identifies major applications of AI in immediate feedback, individualized language instruction, writing-process support, feedback literacy, and teacher workload management. It also considers limitations involving inconsistent feedback, disciplinary inaccuracies, linguistic homogenization, cognitive dependence, and unequal technological access. The paper proposes a human-centred instructional framework in which AI-generated feedback is critically evaluated and supplemented by student judgment, teacher guidance, peer interaction, transparent acknowledgement, and reflective revision. It concludes that AI should not replace writing teachers or student authorship but should function as a supervised pedagogical resource that increases opportunities for practice, reflection, feedback, and academic language development.
K.Savitha, P. Kavinkumar· Stanzaleaf International Jou...· 0 citations
In recent years, artificial intelligence (AI) has become popular in English as a Foreign Language (EFL) writing classes; however, the effects of AI training on students’ learning remain underexplored. This study explores the effects of AI-supported writing instruction on tertiary-level EFL students’ language classroom engagement (LCE) and writing achievement. The Language Classroom Engagement Scale (LCES) (Eerdemutu et al., 2024) and students’ argumentative essays, students’ reflections and interviews were conducted. The results revealed statistically significant improvement in LCE, except for emotional engagement. Moreover, their achievement in topic, organization, support, style, and sources occurred while enhancing knowledge of conventions (grammar, punctuation and mechanics) was found limited. Qualitative findings indicated increased self-confidence and self-reflection. Overall, the findings highlight the potential of AI-supported tools in EFL writing instruction.
N. Erdemir, Hazal Tekeli, İdil Sayın· Journal of Computer and Educ...· 0 citations
This mixed-methods study examines English as a Foreign Language (EFL) teachers’ perceptions of AI tools, focusing on usage practices, perceived opportunities, challenges, and professional development needs, and highlights a dual identity in teacher discourse.
Memidin Braha, Rezarta Ramadani· Open Research Europe· 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
GenAI is positioned as a transformative force in language education that offers technological innovation and new frameworks for inclusive, responsive, and emotionally attuned instruction.
Abderahman Rejeb, Karim Rejeb, Heba F. Zaher et al.· Quality & Quantity· 0 citations
Artificial intelligence (AI) has become increasingly prominent in English language education, offering new possibilities for supporting reading instruction and independent learning among English as a second language (ESL) learner. This study aims to examine the existing body of empirical research concerning the use of AI to improve reading skills and encourage self-regulated learning (SRL). The significance of this study lies in its synthesis of fragmented data to provide a unified framework for future technology-mediated reading instruction. Using a systematic literature review approach that strictly followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, 19 empirical studies were extracted from major academic databases including Scopus, ERIC, and Google Scholar and analyzed through thematic analysis. The synthesized results reveal that AI applications, particularly adaptive learning platforms and conversational agents, effectively support reading development by giving feedback, personal learning experiences, and opportunities for learners to monitor their own progress. Concurrently, the results highlight several concerns, including excessive dependence on technological support and limited development of higher-order reading skills. Overall, AI can make a valuable contribution to reading instruction when integrated with pedagogical practices that encourage learner independence. Future research should investigate the influence of AI on reading comprehension through longitudinal studies conducted in authentic educational settings.
Ruba Salim Abdulaziz Al Rawas, Maslawati Mohamad, Intan Farahana Kamsin· Arab World English Journal· 0 citations
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