Linguistics and Languages

L2 writer engagement with automated written corrective feedback provided by ChatGPT: A mixed-method multiple case study

D. Yan and S. Zhang

This fascinating mixed-method study by Da Yan and Shuxian Zhang explores how L2 writers interact with ChatGPT as an automated written corrective feedback provider. Discover the intricate dynamics of language proficiency, technological competence, and affective engagement in this innovative learning environment.

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Playback language: English

Index

Abstract

This mixed-method multiple case study investigated L2 writers’ behavioral, cognitive, and affective engagement with ChatGPT as an automated written corrective feedback (AWCF) provider. Four L2 writers' engagement was explored through prompt writing techniques, revision operations, metacognitive and cognitive strategies, and attitudinal responses. Results showed behavioral engagement related to language proficiency and technological competence; ineffective metacognitive regulation; and an affectively engaging but demanding learning environment. The study offers implications for using GAI-based technologies in language education.

Publisher

Humanities and Social Sciences Communications

Published On

Aug 26, 2024

Authors

Da Yan, Shuxian Zhang

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