The growing use of artificial intelligence in higher education has reshaped how writing feedback is delivered, received, and revised. In language education, AI-based feedback has become especially relevant because it can provide immediate, individualized, and scalable comments on students' academic texts. This article examines the pedagogical effectiveness of AI-based feedback and its impact on university students' academic writing. The discussion is grounded in established feedback theory, which emphasizes that useful feedback should help learners understand goals, monitor performance, and plan further improvement. Recent studies suggest that AI-supported feedback can improve students' writing quality, feedback engagement, and revision practices, particularly in areas such as grammar, vocabulary, structure, and organization. At the same time, the literature also shows that the educational value of AI feedback depends on teacher mediation, students' feedback literacy, and the degree to which learners engage critically rather than mechanically with automated suggestions. This article argues that AI-based feedback is most effective when used not as a replacement for teachers, but as a pedagogical support tool integrated into process writing, reflection, and revision.
The Pedagogical Effectiveness Of Ai-Based Feedback In Language Education: Evidence From University Students' Academic Writing
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References
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