Speaker
Abstract section 5: References
Carless, D., & Boud, D. (2018). The development of student feedback literacy: Enabling
uptake of feedback. Assessment & Evaluation in Higher Education, 43(8), 1315–1325. https://doi.org/10.1080/02602938.2018.1463354
Little, T., Dawson, P., Boud, D., & Tai, J. (2024). Can students’ feedback literacy be improved? A scoping review of interventions. Assessment & Evaluation in Higher Education, 49(1),39–52. https://doi.org/10.1080/02602938.2023.217761
Guo, K., & Wang, D. (2024). To resist it or to embrace it? Examining ChatGPT’s potential
to support teacher feedback in EFL writing. Education and Information Technologies, 29(7),8435–8463. https://doi.org/10.1007/s10639-023-12146-0
Zhan, Y., & Yan, Z. (2025). Students’ engagement with ChatGPT feedback: Implications for student feedback literacy in the context of generative artificial intelligence. Assessment & Evaluation in Higher Education, 1–14. Advance online publication.
https://doi.org/10.1080/02602938.2025.2471821
Abstract section 4: Outcomes/results
First, the practice had a minimal impact on judgement but significantly enhanced students' feedback literacy in four domains: valuing feedback, identifying diverse feedback resources, managing emotions, and taking action. Second, GenAI fulfilled many functions. Practically, it enhanced student motivation, engagement, and perception of achievement in mastering a new tool. Peer feedback enhanced students' ability to recognise diverse feedback resources, regulate their emotions, and respond at product level. Third, limited awareness of assessment rubrics, persistent reliance on GenAI, and a failure to comprehend its limitations may exacerbate the tools adverse effects and perhaps hinder the development of evaluative judgement.
Abstract section 3: Content/method
The mixed-methods literacy includes a five-dimensional framework of feedback literacy that guided the thematic analysis of qualitative interview data, while repeated measures multivariate analysis of variance were employed to gather and analyse the questionnaire data.
Abstract section 2: Contribution/research questions
RQ1: Did the students' feedback literacy in L2 writing evolve following the implementation of GenAI-supported feedback practices? What alterations occurred?
RQ2: How may alterations in feedback literacy be interpreted through students' impressions of the GenAI-supported feedback practice?
Abstract section 1: Relevance
Investigations into student feedback, encompassing the knowledge, skills, and attitudes necessary to comprehend and respond to feedback, have supplanted research focused on the successful provision of feedback (Carless & Boud, 2018; Little et al., 2024). Generative artificial intelligence (GenAI) tools can enhance student participation in writing processes by offering dialogic and context-sensitive feedback (Guo & Wang, 2024; Zhan & Yan, 2025). There is a lack of research on the impact of GenAI-supported feedback practices on learners' subjective perceptions of feedback practices. This mixed-methods study addresses this gap by assessing a 14-week feedback practice involving 28 Japanese university students.
| Title | GenAI and Student Feedback in L2 Writing |
|---|---|
| Teaching Context | College and university education |