20–22 Nov 2026
The WINC Aichi
Asia/Tokyo timezone

Instructor, Peer, & AI: Learner Preferences for Writing Feedback

21 Nov 2026, 14:30
30m
The WINC Aichi

The WINC Aichi

Research-oriented Presentation (30-minutes) CUE: College and University Educators Room 1204

Speakers

Catherine LeBlanc (Kyoto University) Jacob Dunlap (Kyoto University) John Rylander (Kyoto University)

Abstract section 4: Outcomes/results

Findings suggest that learners differentiate between feedback sources. Instructor feedback is associated with expertise, peer feedback with collaborative learning, and AI-generated feedback with tailor-made language learning support. Differences across proficiency levels and faculty affiliation suggest that learners prefer feedback aligned to their levels and needs. Discussion includes insights into how instructors can combine feedback sources in support of learner needs while balancing professional workloads.

Abstract section 1: Relevance

Understanding how learners perceive different types of writing feedback is increasingly important, as instructors balance traditional practices with emerging AI technologies. While corrective feedback (CF) has long been viewed as central to second language (L2) development (Sheen, 2011), research suggests that foreign language (FL) learners respond differently depending on feedback sources and forms (Hyland & Hyland, 2006). Research in the Japanese FL context has highlighted the value of peer feedback (Wakabayashi, 2008), but also potential mismatches between teacher CF practices and learner preferences (Jones & Tang, 2019).

Abstract section 2: Contribution/research questions

The role AI-mediated technology plays in providing tailor-made CF remains underexplored. This study investigates how Japanese undergraduates enrolled in academic writing courses view the feedback they receive from instructors and peers, as well as their perceptions of the potential of AI tools to support language learning.

Do learner characteristics influence how undergraduates perceive the value of feedback?
Do learners view feedback from instructors, peers, and AI tools as having similar or differing levels of usefulness?

Abstract section 5: References

Hyland, K., & Hyland, F. (2006). Feedback on second language students’ writing. Language Teaching, 39(2), 83–101. https://doi.org/10.1017/S0261444806003399

Jones, K., & Tang, K. (2019). Teacher and student perspectives on written feedback. In JALT Postconference Publication 2018.1 (pp. 333–342). Japan Association for Language Teaching. https://doi.org/10.37546/JALTPCP2018-45

Sheen, Y. (2011). Corrective Feedback, Individual Differences and Second Language Learning. Springer. https://doi.org/10.1007/978-94-007-0548-7

Wakabayashi, R. (2008). The effect of peer feedback on EFL writing: Focusing on Japanese university students’ revision behavior. JALT Journal, 30(2), 177–196.

Abstract section 3: Content/method

Data collection involved a multidimensional survey administered to first- and second-year Japanese undergraduates enrolled in academic writing courses (N = 247). Rasch analysis was used to convert ordinal Likert responses into interval-level logit measures and to evaluate item functioning. Item dimensionality was examined using principal components analysis of Rasch residuals. Follow-up Bayesian statistical procedures were used to compare learner groups on gender, faculty affiliation, and English proficiency.

Title Instructor, Peer, & AI: Learner Preferences for Writing Feed
Teaching Context College and university education

Author

John Rylander (Kyoto University)

Co-authors

Catherine LeBlanc (Kyoto University) Jacob Dunlap (Kyoto University)

Presentation materials

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