Research article | Open Access
International Journal of Contemporary Approaches in Education 2025, Vol. 4(2) 81-100
pp. 81 - 100 | DOI: https://doi.org/10.29329/ijcae.2025.1406.1
Publish Date: December 31, 2025 | Single/Total View: 12/6 | Single/Total Download: 15/6
Abstract
This study aimed to compare the effects of teacher feedback (TF) and AI-supported feedback (AIF) on academic achievement, perceived self-regulation, and feedback literacy among 42 sixth-grade students in a private school in Istanbul, Türkiye. Forty-two students were assigned to either a TF group (n=21), which received written feedback from the teacher, or an AIF group (n=21), which received AI-generated feedback through a Python-based natural language processing platform integrated with Cognitive Diagnostic Modelling. Both groups completed weekly quizzes over a four-week intervention period, aligned with English curriculum learning objectives. A 2 (time: pre-test vs. post-test) × 2 (group: TF vs. AIF) mixed-design multivariate analysis of variance (Mixed MANOVA) revealed significant improvements in all measured outcomes from pre-test to post-test (p<.001), with no significant differences between the TF and AIF groups or their interaction. These findings suggest that formative feedback enhances student outcomes regardless of delivery mode. The study underscores the potential of “AI + Teacher” collaborative models in middle school education, supporting essential skills development while addressing resource constraints for individualized feedback.
Keywords: Academic Achievement, AI-Supported Feedback, Feedback Literacy, Formative Feedback, Self-Regulation
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Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431-440. https://doi.org/10.1007/s43681-021-00096-7 Anderman, E. M., & Midgley, C. (1997). Changes in achievement goal orientations, perceived academic competence, and grades across the transition to middle-level schools. Contemporary educational psychology, 22(3), 269-298. https://doi.org/10.1006/ceps.1996.0926 Arslan, S., & Gelişli, Y. (2015). Algılanan öz-düzenleme ölçeği: Bir ölçek geliştirme çalışması. Sakarya University Journal of Education, 5(3), 67-74. https://doi.org/10.19126/suje.07146 Bailey, R., & Garner, M. (2010). Is the feedback in higher education assessment worth the paper it is written on? Teachers' reflections on their practices. Teaching in higher education, 15(2), 187-198. https://doi.org/10.1080/13562511003620019 Bennett, R. E. (2011). Formative assessment: a critical review. Assessment in Education: Principles, Policy & Practice, 18(1), 5–25. https://doi.org/10.1080/0969594X.2010.513678 Black, P., & Wiliam, D. (1998). Assessment and Classroom Learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. https://doi.org/10.1080/0969595980050102 Brookhart, S. M. (2017). How to give effective feedback to your students. Ascd. Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of educational research, 65(3), 245-281. https://doi.org/10.3102/00346543065003245 Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Ravenio Books. Carless, D. (2013). Sustainable feedback and the development of student self-evaluative capacities. In Reconceptualising feedback in higher education (pp. 113-122). Routledge. 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 Carless, D., Salter, D., Yang, M., & Lam, J. (2011). Developing sustainable feedback practices. Studies in higher education, 36(4), 395-407. https://doi.org/10.1080/03075071003642449 Castro, G. P. B., Chiappe, A., Rodríguez, D. F. B., & Sepulveda, F. G. (2024). Harnessing AI for Education 4.0: Drivers of Personalized Learning. Electronic Journal of e-Learning, 22(5), 1-14. https://doi.org/10.34190/ejel.22.5.3467 Chickering, A. W., & Gamson, Z. F. (1987). Seven principles for good practice in undergraduate education. AAHE bulletin, 3, 7. Common Sense Media. (2024). Generative AI in K-12 education: A white paper on current applications, future possibilities, and key considerations. Retrieved from https://www.commonsensemedia.org/sites/default/files/research/report/generative-ai-in-k-12-education-white-paper-updated-aug-2024-final-2.pdf Crompton, H., & Burke, D. (2022). Artificial intelligence in K-12 education. SN Soc Sci 2, 113. https://doi.org/10.1007/s43545-022-00425-5 Cutumisu, M., & Schwartz, D. L. (2016). The Impact of Middle-School Students' Feedback Choices and Performance on Their Feedback Memory. International Association for Development of the Information Society. de Kleijn, R. A. M. (2021). Supporting student and teacher feedback literacy: an instructional model for student feedback processes. Assessment & Evaluation in Higher Education, 48(2), 186-200. https://doi.org/10.1080/02602938.2021.1967283 Er, E., Akçapınar, G., Bayazıt, A., Noroozi, O., & Banihashem, S. K. (2024). Assessing student perceptions and use of instructor versus AI‐generated feedback. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13558 Evans, C. (2013). Making sense of assessment feedback in higher education. Review of educational research, 83(1), 70-120. https://doi.org/10.3102/0034654312474350 Erisyerico, M. L., & Fauzan, A. (2024). Comparing Students’ Perceptions of AI and Human Feedback in Improving Writing Skills. Scientia, 3(2). Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage. Gao, L., López-Pérez, M. E., Melero-Polo, I., & Trifu, A. (2024). Ask ChatGPT first! Transforming learning experiences in the age of artificial intelligence. Studies in Higher Education, 49(12), 2772-2796. https://doi.org/10.1080/03075079.2024.2323571 George, D., & Mallery, P. (2010). SPSS for Windows step by step: A simple guide and reference (10th ed.). Pearson. Gierl, M. J., & Lai, H. (2018). Using automatic item generation to create solutions and rationales for computerized formative testing. Applied psychological measurement, 42(1), 42-57. https://doi.org/10.1177/0146621617726788 Gierl, M. J., Lai, H., & Tanygin, V. (2021). Advanced methods in auto-matic item generation. New York: Routledge. Han, J., & Li, M. (2024). Exploring ChatGPT-supported teacher feedback in the EFL context. System, 126, 103502. https://doi.org/10.1016/j.system.2024.103502 Hattie, J., & Gan, M. (2011). Instruction based on feedback. In Handbook of research on learning and instruction (pp. 263-285). Routledge. Hattie, J., & Timperley, H. (2007). The power of feedback. Review of educational research, 77(1), 81-112. https://doi.org/10.3102/003465430298487 Hyland, K. (2019). Second language writing. Cambridge University Press. Kluger, A. N., & DeNisi, A. (1996). The effects of feedback interventions on performance: a historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological bulletin, 119(2), 254. https://doi.org/10.1037/0033-2909.119.2.254 Kusam, V. A. (2024). Generative-AI assisted feedback provisioning for project-based learning in CS education (Unpublished master’s thesis). University of Michigan–Dearborn, Dearborn, MI. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education. Mejeh, M., Sarbach, L., & Hascher, T. (2024). Effects of adaptive feedback through a digital tool–a mixed-methods study on the course of self-regulated learning. Education and Information Technologies, 29(14), 1-43. https://doi.org/10.1007/s10639-024-12510-8 Morris, R., Perry, T., & Wardle, L. (2021). Formative assessment and feedback for learning in higher education: A systematic review. Review of Education, 9(3), e3292. https://doi.org/10.1002/rev3.3292 Nicol, D. J., & Macfarlane‐Dick, D. (2006). Formative assessment and self‐regulated learning: A model and seven principles of good feedback practice. Studies in higher education, 31(2), 199-218. https://doi.org/10.1080/03075070600572090 Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In Handbook of self-regulation (pp. 451-502). Academic Press. Pozdniakov, S., Brazil, J., Mohammadi, M., Dollinger, M., Sadiq, S., & Khosravi, H. (2025). AI-Assisted Co-Creation: Bridging Skill Gaps in Student-Generated Content. Journal of Learning Analytics, 1-23. https://doi.org/10.18608/jla.2025.8601 Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional science, 18(2), 119-144. https://doi.org/10.1007/BF00117714 Sandal, A. K., Helleve, I., Smith, K., & Gamlem, S. M. (2022). Feedback practice in lower secondary school: Exploring development of perceptions of feedback practice among students and teachers. Cogent Education, 9(1), 2101236. https://doi.org/10.1080/2331186X.2022.2101236 Say, R., Visentin, D., Saunders, A., Atherton, I., Carr, A., & King, C. (2024). Where less is more: Limited feedback in formative online multiple‐choice tests improves student self‐regulation. Journal of Computer Assisted Learning, 40(1), 89-103. https://doi.org/10.1111/jcal.12868 Sayın, A., & Gierl, M. J. (2023). Automatic item generation for online measurement and evaluation: Turkish literature items. International Journal of Assessment Tools in Education, 10(2), 218-231. https://doi.org/10.21449/ijate.1249297 Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International journal of educational technology in higher education, 18, 1-23. https://doi.org/10.1186/s41239-021-00292-9 Shute, V. J. (2008). Focus on formative feedback. Review of educational research, 78(1), 153-189. https://doi.org/10.3102/0034654307313795 Steiss, J., Tate, T., Graham, S., Cruz, J., Hebert, M., Wang, J., & Olson, C. B. (2024). Comparing the quality of human and ChatGPT feedback of students’ writing. Learning and Instruction, 91, 101894. https://doi.org/10.1016/j.learninstruc.2024.101894 Stevenson, M., & Phakiti, A. (2014). The effects of computer-generated feedback on the quality of writing. Assessing Writing, 19, 51-65. https://doi.org/10.1016/j.asw.2013.11.007 Sutton, P. (2012). Conceptualizing feedback literacy: Knowing, being, and acting. Innovations in Education and Teaching International, 49(1), 31–40. https://doi.org/10.1080/14703297.2012.647781 Sweller, J. (2011). Cognitive load theory. In Psychology of learning and motivation (Vol. 55, pp. 37-76). Academic Press. Tabachnick, B.G.,& Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson. Thomas, D. R., Borchers, C., Kakarla, S., Lin, J., Bhushan, S., Guo, B., Gatz, E., & Koedinger, K. R. (2025). Does multiple choice have a future in the age of generative ai? a posttest-only rct. In Proceedings of the 15th International Learning Analytics and Knowledge Conference (pp. 494-504). https://doi.org/10.1145/3706468.3706530 Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. Wiliam, D. (2011). Embedded formative assessment. Solution Tree Press. Wisniewski, B., Zierer, K., & Hattie, J. (2020). The power of feedback revisited: A meta-analysis of educational feedback research. Frontiers in psychology, 10, 487662. https://doi.org/10.3389/fpsyg.2019.03087 Wongvorachan, D., & Bulut, O. (2022). Feedback generation through artificial intelligence. In The Open/Technology in education, society, and scholarship association conference (Vol. 2, No. 1, pp. 1-9). https://doi.org/10.18357/otessac.2022.2.1.125 Yıldız, H., Bozpolat, E., & Hazar, E. (2022). Feedback literacy scale: A study of validation and reliability. International Journal of Eurasian Education and Culture, 7(19), 2214-2249. http://dx.doi.org/10.35826/ijoecc.624 Zeevy-Solovey, O. (2024). Comparing peer, ChatGPT, and teacher corrective feedback in EFL writing: Students' perceptions and preferences. Technology in Language Teaching & Learning, 6(3), 1482-1482. https://doi.org/10.29140/tltl.v6n3.1482 Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J., Yuan, J. & Li, Y. (2021). A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity, 2021(1), 812542 https://doi.org/10.1155/2021/8812542 Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In Handbook of self-regulation (pp. 13-39). Academic Press. |