A Systematic Review of Empirical Research about Beyond Automated Correction by Human–AI Feedback Partnership for Developing L2 Writing

Authors

  • Muhammad Imran Senior Language Instructor, Sultan Qaboos University, Sultanate of Oman; PhD Candidate in English, Universiti Malaysia Pahang Al-Sultan Abdullah; Centre for Modern Languages, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia; Department of English, Daffodil International University, Dhaka, Bangladesh.
  • Zuraina Ali Department of English, Daffodil International University, Dhaka, Bangladesh.
  • Mohammad Musab Bin Azmat Ali Department of English, Daffodil International University, Dhaka, Bangladesh.

DOI:

https://doi.org/10.70917/ijcisim-2026-5330

Keywords:

Artificial intelligence, generative AI, human–AI feedback, L2 writing, systematic review, PRISMA 2020, feedback literacy, large language models

Abstract

This paper synthesizes empirical studies published from 2023 to 2026 to discuss the growing position of AI-aided writing feedback for L2 students. Drawing upon the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) statement, this study organizes and critically reviews past findings in the context of instructional transformation from existing AWE systems to LLM-empowered collaborative Human-AI writing feedback in L2 education; influence of learner engagement, trust, and AI writing feedback literacy on the adoptability and applicability of AI feedback; and (3) instructional and research limitations that need to be addressed in subsequent research. Our systematic review demonstrates that generative AI facilitates writing quality improvement, revision processes, learner autonomy and increased writing access by providing on-time, tailored and interactive support.  Moreover, this review reveals that the contribution of tool-aided feedback to learning lies not just in the features of AI feedback alone, but more in the users’ ability to critically understand, judge and utilize the suggested pieces of writing to advance writing competence within the teacher-supervised writing environment. Thus, the results would recommend the Human-AI Partnership framework, in which LLMs would substitute or at least assist the role of the human tutor rather than directly replace; at the same time, it would also shed light upon issues of conducting longitudinal, methodologically robust, and theoretically grounded studies regarding generative AI for L2 writing pedagogy.

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Published

2026-08-30

How to Cite

Muhammad Imran, Zuraina Ali, & Mohammad Musab Bin Azmat Ali. (2026). A Systematic Review of Empirical Research about Beyond Automated Correction by Human–AI Feedback Partnership for Developing L2 Writing. International Journal of Computer Information Systems and Industrial Management Applications, 18(21s), 548–570. https://doi.org/10.70917/ijcisim-2026-5330

Issue

Section

Original Articles