Feedback in second language writing: Current issues in research and practice
TL;DR
It is argued that feedback must extend beyond error correction to address communicative effectiveness, design, and intermodal coherence (e.g., image-text alignment) and proposed a research agenda that calls for longitudinal investigation of AI-mediated and multimodal feedback ecologies.
Abstract
This article reconceptualizes feedback in second language (L2) writing for an era of digital and multimodal composing. It argues that feedback must extend beyond error correction to address communicative effectiveness, design, and intermodal coherence (e.g., image-text alignment). To situate our discussion within the evolving ecology of digitally-mediated writing support, the article first introduces affordances of generative artificial intelligence (GenAI) tools for feedback and describes how AI can be used to support feedback practices. The article also accounts for the complexity of feedback in contemporary composing environments by drawing on several complementary frameworks: cognitive and sociocultural perspectives, which explain internal and socially-mediated processing of feedback (e.g., attention, noticing, cognitive load, mediation), and informs multimodal feedback practices. In particular, it focuses on learning by design, which positions feedback as a means of developing learners as active designers of meaning, and on D’Angelo’s (2016) model, which provides criteria for feedback provision on multimodal products. Pedagogically, the article operationalizes theseperspectives through examples that illustrate how instructor and AI feedback can be integrated to support revision in both monomodal and multimodal texts in L2 learning and teaching contexts. The article concludes by proposing a research agenda that calls for longitudinal investigation of AI-mediated and multimodal feedback ecologies and examines how learner characteristics shape the effective and ethical use of feedback in L2 classrooms.