Journals / Sınırsız Eğitim ve Araştırma Dergisi / 2026 / Cilt: 11 Sayı: 2

Feedback Literacy within Teacher Feedback vs. AI Feedback

Nermin Gökel(Milli Eğitim Bakanlığı, Antalya)0000-0002-2989-9334
Gülden Tüm(ÇUKUROVA ÜNİVERSİTESİ)0000-0001-7790-0857
Pages
473–485
Publication Date
2026-07-15
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings reveal that students reveal growing awareness of feedback processes.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aims to investigate how the age of artificial intelligence reshapes the world as numerous individuals get feedback.
📊
Key Findings & Data:Methodology employs a descriptive design, wherein data are collected from 52 undergraduate EFL students at a school of foreign languages at a state university in Adana-Türkiye.
💡
Conclusion & Impact:The findings reveal that students reveal growing awareness of feedback processes.
🔬Design:Survey & Empirical Study
📍Scope:2025-2026 Verileri
⚙️Variables:AI feedback · Feedback literacy · Teacher-feedback

Abstract

This study aims to investigate how the age of artificial intelligence reshapes the world as numerous individuals get feedback. Regarding this issue, exploring how students perceive, interpret, and utilize feedback given by their language instructors and AI-driven tools gains vital importance. Methodology employs a descriptive design, wherein data are collected from 52 undergraduate EFL students at a school of foreign languages at a state university in Adana-Türkiye. The data includes writing parts of the Module 2 Exit Exam, reflective diaries of the students who get feedback from their language instructors, and those who get AI-generated feedback. The survey responses are also submitted in the study in 2025-2026 Academic Year. Two types of reflective diaries are collected via e-mail organized by the researcher. Quantitative data are analyzed to identify frequencies of feedback types and their reliance with exit exam, while qualitative insights are derived from student reflections via feedback diaries. The findings reveal that students reveal growing awareness of feedback processes. They tend to value immediacy and clarity in AI feedback while still relying on human instructors for contextual and affective dimensions. The study assumes to contribute to the field by situating feedback literacy within the rapidly changing digital landscape in pedagogical implications for integrating AI-supported tools into EFL writing instruction.

Özet

This study aims to investigate how the age of artificial intelligence reshapes the world as numerous individuals get feedback. Regarding this issue, exploring how students perceive, interpret, and utilize feedback given by their language instructors and AI-driven tools gains vital importance. Methodology employs a descriptive design, wherein data are collected from 52 undergraduate EFL students at a school of foreign languages at a state university in Adana-Türkiye. The data includes writing parts of the Module 2 Exit Exam, reflective diaries of the students who get feedback from their language instructors, and those who get AI-generated feedback. The survey responses are also submitted in the study in 2025-2026 Academic Year. Two types of reflective diaries are collected via e-mail organized by the researcher. Quantitative data are analyzed to identify frequencies of feedback types and their reliance with exit exam, while qualitative insights are derived from student reflections via feedback diaries. The findings reveal that students reveal growing awareness of feedback processes. They tend to value immediacy and clarity in AI feedback while still relying on human instructors for contextual and affective dimensions. The study assumes to contribute to the field by situating feedback literacy within the rapidly changing digital landscape in pedagogical implications for integrating AI-supported tools into EFL writing instruction.

🔖

Cite This Article

Gökel, N., Tüm, G. (2026). Feedback Literacy within Teacher Feedback vs. AI Feedback. Sınırsız Eğitim ve Araştırma Dergisi, Cilt: 11 Sayı: 2, 473–485. https://doi.org/10.29250/sead.1867106

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (16)

  1. Abdullaev, D., Abdazimov, A., Khusainova, Z., Makhmudov, S., & Sagdullaeva, D. (2025). A Phenomenological Study on the Impact of Personalized AI Feedback on Writing Quality: Insights from EFL Students in Uzbekistan. Computer-Assisted Language Learning Electronic Journal, 26(7), 162-186. https://callej.org/index.php/journal/article/view/816/501
  2. 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
  3. Cohen, J. (1988). Statistical power for the behavioural sciences (2th ed.). Lawrence Erlbaum Associates.
  4. Cui, Y., Meng, Y., Qian, X., & Tang, L. (2025). Developing a college student feedback literacy scale for the GenAI context. Assessment & Evaluation in Higher Education, 1-14. https://doi.org/10.1080/02602938.2025.2495048
  5. Deep, P. D., & Chen, Y. (2025). The role of AI in academic writing: Impacts on writing skills, critical thinking, and integrity in higher education. Societies, 15(9), 247. https://doi.org/10.3390/soc15090247 Hyland, K., & Hyland, F. (2019). Feedback on second language students’ writing. Language Teaching, 53(2), 139–153. https://doi.org/10.1017/S0261444819000367
  6. Karatay, Y., & Karatay, L. (2024). Automated writing evaluation use in second language classrooms: A research synthesis. System, 123, 103332. https://doi.org/10.1016/j.system.2024.103332
  7. McCarthy, K. M., Wilkerson, D., & Ashirifi, G. (2022). Student and faculty perceptions on feedback in a graduate social work distance education program. Journal of Teaching in Social Work, 42(4), 392-408. https://doi.org/10.1080/08841233.2022.2102103
  8. Nouara, M., & Dehbia, M. (2023). Students’ attitudes towards and teachers’ perceptions of the utilization of ChatGPT to improve students’ writing proficiency, [Unpublished Doctoral dissertation]. Mouloud Mammeri University.
  9. Tian, L., & Zhou, Y. (2020). Learner engagement with automated feedback, peer feedback and teacher feedback in an online EFL writing context. System, 91, 102247. https://doi.org/10.1016/j.system.2020.102247
  10. Winstone, N. E., & Carless, D. (2019). Designing effective feedback processes in higher education: A learning-focused approach. Routledge. https://doi.org/10.4324/9781351115940
  11. Winstone, N. E., Nash, R. A., Parker, M., & Rowntree, J. (2017). Supporting learners' agentic engagement with feedback: A systematic review and a taxonomy of recipience processes. Educational Psychologist, 52(1), 17-37. https://doi.org/10.1080/00461520.2016.1207538
  12. Yu, S., & Liu, C. (2021). Improving student feedback literacy in academic writing: An evidence-based framework. Assessing Writing, 48, 100525. https://doi.org/10.1016/j.asw.2021.100525
  13. Rapanta, C. (2025). Postdigital Citizenship education and argumentation: Can AI support students’ critical literacy?. In Discourses of globalisation and citizenship education (pp. 69-86). Springer Nature.
  14. Taşkıran, A., & Göksel, N. (2022). Automated feedback and teacher feedback: Writing achievement in learning English as a foreign language at a distance. Turkish Online Journal of Distance Education, 23(2), 120-139. https://doi.org/10.17718/tojde.1096260
  15. Wu, J., Yan, Z., Yu, G., Xie, G., Li, L., Yang, Y., & Zhu, J. (2025). Enhancing mathematics achievement through teacher support: The mediating role of student feedback literacy. The Journal of Educational Research, 1-11. https://doi.org/10.1080/00220671.2025.2508701
  16. Zhan, Y., Boud, D., Dawson, P., & Yan, Z. (2025). Generative artificial intelligence as an enabler of student feedback engagement: a framework. Higher Education Research & Development, 44(5), 1289-1304. https://doi.org/10.1080/07294360.2025.2476513