Mixed-methods study examines how L2 writers improve through teacher and AI feedback, suggesting new pedagogical strategies.
Second language (L2) writers’ capacity to reason about language choices during revision is theoretically central to advanced writing development, yet few studies trace how this linguistic reasoning emerges in repeated feedback-revision cycles that integrate teacher and AI suggestions. This multi-case, longitudinal mixed-methods study examined how ten L2 learners enacted metalinguistic strategies and developed linguistic reasoning across a 12-week online Academic English program. Triangulated data sources included 1246 feedback revision episodes, pre-post reasoning-task responses, and semi-structured exit interviews. We operationalized linguistic reasoning as the processual construct of interest and treated metalinguistic strategies as observable indicators; reasoning responses were scored with a three-dimensional analytic rubric while revisions and interaction logs were coded thematically and by strategy. Results show a systematic shift from early, correctness-focused edits toward later revisions characterized by coordinated structural comparison, more explicit self-explanation, and integrative argumentation; composite reasoning scores increased, with the largest gains observed on depth of explanation. Process analyses identified recurrent patterns in which alternative-rich feedback coincided with comparison moves, partially divergent suggestions often coincided with more explicit self-explanation, and integrative argumentation involving discourse-level revision became more visible over time. Pedagogical implications are also discussed.
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Huo et al. (2026) studied this question.
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