Differences in Perceptions of Generative AI Feedback by Non-Native English-Speaking Students and Educators in Higher Education

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Bibliographic Details
Title: Differences in Perceptions of Generative AI Feedback by Non-Native English-Speaking Students and Educators in Higher Education
Language: English
Authors: Bunichi Otaki (ORCID 0009-0007-7840-4055), Shotaro Naganuma (ORCID 0000-0002-0665-2179), Takumi Jin, Jun Oshima (ORCID 0000-0002-9885-448X)
Source: Educational Psychology. 2026 46(1):112-149.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 38
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Tests/Questionnaires
Education Level: Higher Education
Postsecondary Education
Descriptors: Foreign Countries, Higher Education, Undergraduate Students, Graduate Students, College Faculty, Student Attitudes, Teacher Attitudes, Artificial Intelligence, Feedback (Response), English Learners, Technology Uses in Education, Ethics, Familiarity
Geographic Terms: Sweden
DOI: 10.1080/01443410.2025.2564893
ISSN: 0144-3410
1469-5820
Abstract: This article explores how non-native English-speaking (NNES) students and higher education educators perceive AI-generated feedback through dialogic feedback framework and ethical lens. Using a quantitative ethnography approach, Study 1 employed focus group interviews with 17 NNES students and nine educators, identified three themes: (1) instrumental efficiency vs. holistic understanding, (2) emotional comfort and social dynamics , and (3) ethical issues for trustable feedback. Study 2 employed epistemic network analysis (ENA) to identify that educators' epistemic frames integrated broader ethical considerations, while students' frames emphasized emotional and relational aspects to human educators. Findings indicate that while AI-generated feedback offers emotional neutrality and immediacy, it lacks contextual depth, relational continuity, and interpretive richness in human feedback. This suggests that AI-generated feedback should be designed to work alongside, not replace human educators' feedback, offering implications for responsible and dialogically informed feedback practices in higher education.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1500977
Database: ERIC
Description
Abstract:This article explores how non-native English-speaking (NNES) students and higher education educators perceive AI-generated feedback through dialogic feedback framework and ethical lens. Using a quantitative ethnography approach, Study 1 employed focus group interviews with 17 NNES students and nine educators, identified three themes: (1) instrumental efficiency vs. holistic understanding, (2) emotional comfort and social dynamics , and (3) ethical issues for trustable feedback. Study 2 employed epistemic network analysis (ENA) to identify that educators' epistemic frames integrated broader ethical considerations, while students' frames emphasized emotional and relational aspects to human educators. Findings indicate that while AI-generated feedback offers emotional neutrality and immediacy, it lacks contextual depth, relational continuity, and interpretive richness in human feedback. This suggests that AI-generated feedback should be designed to work alongside, not replace human educators' feedback, offering implications for responsible and dialogically informed feedback practices in higher education.
ISSN:0144-3410
1469-5820
DOI:10.1080/01443410.2025.2564893