Jul 2026· Journal of English language teaching and applied linguistics· Vol 8, pp. 130-141· 0 citations· 51 references
TL;DR
Results suggest that AI-based feedback tools represent a powerful and scalable approach to enhancing English writing skills, and blended learning environments yielded stronger effects than traditional classroom settings, highlighting the importance of flexible and technology-supported contexts.
Abstract
Artificial intelligence has increasingly transformed educational practices, particularly in writing instruction. Among these innovations, AI-based feedback tools have appeared as favourable solutions for giving personalized, immediate, and accessible support to learners. This study aims to assess the effect of AI-based feedback on English writing performance, filling research gaps regarding the impact of English language proficiency level, duration of intervention, and implementation setting on the effectiveness of AI-based feedback tools in improving students’ English language writing performance. A systematic review and meta-analysis were utilized to examine 24 empirical studies published from January 2022 to March 2026 across five databases (including EBSCO, ProQuest, ERIC, Web of Science, and Wiley Online Library). The results indicated that AI-based feedback tools had a large overall effect size (Hedges' g = 1.298, p < 0.001) on students’ writing performance. Moreover, moderator analyses revealed that learners with intermediate proficiency benefited more substantially compared to advanced learners, suggesting that AI feedback is particularly effective for developing writers. In terms of implementation settings, blended learning environments yielded stronger effects than traditional classroom settings, highlighting the importance of flexible and technology-supported contexts. Although the duration of intervention did not significantly moderate the results, consistently strong effects were observed across the three periods of intervention: short, medium, and long-term. These findings suggest that AI-based feedback tools represent a powerful and scalable approach to enhancing English writing skills.
This study aims to explore the application effects of AI-assisted writing tools in English for Specific Purposes (ESP) writing instruction and their impact on learners' writing strategies. Using a blended learning empirical research design, this study recruited 128 students majoring in economics and management at a college. Through a 16-week teaching experiment, by combining writing tests, questionnaires, and in-depth interviews, this study systematically explored the specific mechanisms by which AI-assisted tools affect the quality of ESP writing. The results show that AI-assisted writing tools have the potential to improve learners' writing performance in five dimensions: grammatical accuracy, lexical richness, register appropriateness, content completeness, and structural logic. Simultaneously, these tools encourage learners to develop three adaptive writing strategies: human-computer collaboration, process monitoring, and metacognitive regulation. Interestingly, there were significant intergroup differences in tool use and strategy selection based on learners' English proficiency levels. This study provides empirical evidence and practical insights for AI-assisted ESP writing instruction in blended learning environments.
The integration of artificial intelligence (AI) technology into education has become increasingly important in supporting the writing proficiency of non-English major students in English for Specific Purposes (ESP) courses. This study investigates the effectiveness of ChatGPT in improving the clarity, coherence, and grammatical accuracy of students’ writing drafts. Employing a quasi-experimental design, the study examined differences in learning outcomes between a ChatGPT-assisted class (experimental group) and a traditionally taught class (control group). Data were collected through writing draft assessments, participant surveys, and interviews. The findings indicate that the experimental group demonstrated a significant improvement in writing performance, particularly in organizing and presenting ideas clearly and coherently. Furthermore, the personalized guidance provided by ChatGPT contributed to students’ learning autonomy and self-confidence. These findings highlight the potential of ChatGPT as a supportive tool for language learning while emphasizing the continued role of lecturers in validating recommendations generated by AI systems. Future research is encouraged to explore AI-assisted writing in more specialized disciplinary contexts and to investigate its long-term effects on students’ learning retention and writing development.
Rachmat Ari Wibowo· English Language and Educati...· 0 citations
Artificial intelligence (AI) is rapidly transforming academic writing in higher education by enhancing teaching practices, writing assessment, and student learning. Despite the growing adoption of AI-powered tools such as ChatGPT, Grammarly, PaperPal, and Automated Writing Evaluation (AWE) systems, existing research remains fragmented across pedagogical, assessment, and teacher-related perspectives. 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. Guided by four research questions formulated through the PICo framework, a comprehensive search of Scopus and Web of Science identified 236 records, from which 24 peer-reviewed studies published between 2021 and 2026 met the inclusion criteria following screening, eligibility assessment, and quality appraisal. The findings reveal three dominant themes: (1) AI integration in academic writing teaching and learning, (2) AI-assisted assessment, feedback, and writing evaluation, and (3) teachers' perspectives, ethical issues, and AI adoption. Collectively, the reviewed studies demonstrate that AI enhances writing quality, learner engagement, self-regulated learning, and formative assessment through timely and personalized feedback. However, AI remains limited in evaluating higher-order writing competencies, including critical thinking, originality, and contextual reasoning, reinforcing the continued importance of human expertise in writing assessment. The review further highlights the influence of teachers' perceptions, ethical concerns, and institutional readiness on successful AI implementation. The study contributes a comprehensive synthesis of current research by integrating pedagogical, assessment, and human perspectives through the TPACK, Assessment for Learning, and Technology Acceptance Model frameworks. These findings provide practical implications for educators, institutions, and policymakers while identifying priorities for future research on responsible and pedagogically sound AI integration in academic writing within higher education.
Saidatul Akmar Zainal Abidin, Noor Hanim binti Rahmat· International journal of res...· 0 citations
Writing skills are essential in English as a Foreign Language (EFL) learning, yet many high school students experience difficulties in generating ideas, organizing texts, and revising their writing effectively. Integrating Artificial Intelligence (AI) Writing Assistants with Collaborative Online Writing (COW) offers a promising approach to enhancing writing performance through collaborative and process-oriented learning. This study investigates the effectiveness of AI-assisted COW in improving students' English writing skills, with self-efficacy and digital literacy examined as moderating variables. A quantitative approach employing a quasi-experimental non-equivalent control group design was adopted. Participants consisted of eleventh-grade public high school students assigned to experimental and control groups. Data were collected through pre- and post-writing tests, self-efficacy questionnaires, and digital literacy questionnaires, then analyzed using inferential statistics and moderation regression analysis. The results revealed that students receiving AI-assisted COW achieved significantly greater improvements in writing skills than those in the control group, with a substantial effect size. Moreover, self-efficacy and digital literacy significantly strengthened the positive relationship between AI-assisted COW and writing achievement, indicating that students with higher levels of both variables gained greater learning benefits. These findings demonstrate that integrating collaborative online writing with AI technology effectively enhances EFL students' writing skills while highlighting the importance of fostering self-efficacy and digital literacy to maximize learning outcomes.
Firlia Nurul Anisa, Mohammad Sofyan Adi Pranata· JURNAL ILMIAH GEMA PERENCANA· 0 citations
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.
O. Jamoom, Saaid Ali Omar· Comprehensive Journal of Sci...· 0 citations
Artificial intelligence (AI) is increasingly transforming mathematics education through adaptive tutoring systems, automated feedback, and AI-supported assessment tools. However, evidence regarding the effectiveness of these technologies remains dispersed across diverse contexts and study designs. This systematic review synthesised empirical research on the effectiveness of AI-based tutoring and assessment systems in mathematics education published between 2015 and 2025. Guided by the PRISMA framework, a comprehensive search was conducted in Scopus and Web of Science using database-specific search strings. After screening 1,749 records and assessing 76 full-text articles for eligibility, 12 studies met the inclusion criteria and were included in the final synthesis. Data were extracted using a structured framework and study quality was appraised using the Mixed Methods Appraisal Tool (MMAT). Due to heterogeneity in interventions, populations, and outcome measures, a narrative synthesis was employed. Findings indicate that AI-based tutoring systems and adaptive learning platforms generally support improvements in mathematics achievement, particularly among lower-performing learners, although effectiveness varies depending on implementation fidelity, learner characteristics, and instructional context. Studies focusing on generative AI tools such as ChatGPT primarily reported positive perceptions and increased engagement, but evidence of direct achievement gains remains limited. Overall, the evidence base demonstrates moderate methodological quality, with stronger conclusions drawn from experimental and quasi-experimental designs. The review highlights the potential of AI-based tutoring and assessment systems to enhance mathematics learning while emphasising the need for rigorous, large-scale experimental research and clearer reporting of intervention mechanisms.
Neo J. Molemane, Moeketsi Mosia, F. Egara· Discover Education· 2 citations
