Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/114336
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dc.contributorDepartment of Chinese and Bilingual Studiesen_US
dc.creatorChen, Zen_US
dc.creatorZhu, Xen_US
dc.creatorLu, Qen_US
dc.creatorWei, Wen_US
dc.date.accessioned2025-07-25T03:28:16Z-
dc.date.available2025-07-25T03:28:16Z-
dc.identifier.issn0958-8221en_US
dc.identifier.urihttp://hdl.handle.net/10397/114336-
dc.language.isoenen_US
dc.publisherRoutledgeen_US
dc.rights© 2024 Informa UK Limited, trading as Taylor & Francis groupen_US
dc.rightsThis is an Accepted Manuscript of an article published by Taylor & Francis in Computer Assisted Language Learning on 06 Nov 2024 (published online), available at: https://doi.org/10.1080/09588221.2024.2422478.en_US
dc.subjectAI-generated feedbacken_US
dc.subjectChatGPTen_US
dc.subjectGenerative AIen_US
dc.subjectRevision strategiesen_US
dc.subjectUptakeen_US
dc.titleL2 students’ barriers in engaging with form and content-focused AI-generated feedback in revising their compositionsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage715en_US
dc.identifier.epage735en_US
dc.identifier.volume39en_US
dc.identifier.issue3en_US
dc.identifier.doi10.1080/09588221.2024.2422478en_US
dcterms.abstractProviding corrective feedback to second language (L2) writing constitutes a crucial digital affordance for AI-assisted writing systems. However, L2 writers’ revision strategies and obstacles to adopting AI-generated feedback, such as ChatGPT, remain unclear. Forty-five L2 students in a computer science program were tasked with seeking corrective feedback from ChatGPT for their argumentative essays, followed by an analysis of their revisions and rationale for feedback uptake strategies. The findings revealed that approximately 38% of the feedback was either explicitly argued (22%) or ignored (16%). Upon controlling for writing proficiency, participants statistically rejected a significantly higher proportion of feedback at the content level (e.g. evidence) than at the form level (e.g. grammar). Utilizing the Technology Acceptance Model, the reasons for rejecting or ignoring ChatGPT-generated feedback were examined through participants’ reflective data, focusing on two perspectives: inconvenience to use and unusefulness. Inconvenient factors included (1) overload feedback, (2) provision of general descriptions instead of specific error highlighting, and (3) repetitive and tedious comments. Themes related to unusefulness encompassed (1) misinterpretation of authors’ intentions, (2) lack of clarity and illustrative examples, and (3) extraneous and irrelevant feedback. The implications entail pedagogical strategies to mitigate barriers and foster feedback literacy in AI-assisted educational environment.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationComputer assisted language learning, 2026, v. 39, no. 3, p. 715-735en_US
dcterms.isPartOfComputer assisted language learningen_US
dcterms.issued2026-
dc.identifier.scopus2-s2.0-85209539850-
dc.identifier.eissn1744-3210en_US
dc.description.validate202507 bcchen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera3943c-
dc.identifier.SubFormID51770-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryGreen (AAM)en_US
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