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<p style="text-align: justify;">This reflective commentary revisits key insights from research on written corrective feedback (WCF) within the context of feedback facilitated by large language models (LLMs) in higher-education L2 writing. The CAF framework shows that accuracy is most consistently improved through targeted, well-designed feedback. In contrast, claims about complexity and fluency should be approached cautiously, as they depend on factors like task design, sequencing, and learner engagement. The paper distinguishes between traditional automated writing assessments and the generative, dialogic features of LLMs. It proposes a straightforward approach for teachers to provide feedback, including specific prompts, metalinguistic explanations, learner reformulation, genre confirmation, and reflection periods. The paper emphasises that the educational value of LLM-based WCF relies more on thoughtful mediation, ethical considerations, and alignment with assessment goals than on speed or novelty. It concludes by highlighting priority areas for future research, such as the durability of learning gains, transfer from assisted to independent writing, the interaction of dose, timing, and scope, maintaining learner engagement, and ensuring equitable access to AI-mediated feedback across proficiency levels.</p>

frsl.2026.04.02.pdf

Perspective Article

frsl.2026.04.02.xml

AI-Mediated Written Corrective Feedback in L2 Writing: What Changes, What Doesn’t, and What We Still Don’t Know

Guest-editors: 
Deborah Crusan
Wright State University, USA
Hassan Mohebbi 
European Knowledge Development Institute, Turkey

Special Issue: The Role of AI and ChatGPT in Giving Corrective Feedback: Panacea or Unfulfilled Promise