Because the study used intact classes rather than random assignment, the findings should be interpreted as quasi-experimental rather than fully causal. Qualitative interviews were also conducted with a subsample of participants to understand their perceptions and the processes involved in engaging with each feedback source. The quantitative results showed significant gains for both the AI and peer feedback groups, but with distinct patterns.

Students who received AI-generated feedback showed a particular advantage in acknowledging different feedback sources. The peer feedback group improved across a broader set of dimensions, including appreciating feedback, acknowledging different feedback sources, and making judgments about the comments they received. In contrast, the teacher feedback group showed limited improvement, with no significant changes in any of the measured dimensions of writing feedback literacy.

The authors note that most previous research on feedback literacy has focused mainly on teacher or peer feedback, leaving open the question of how feedback from different sources may differentially shape students' development in this area. The qualitative interviews helped explain these quantitative patterns. Students working with AI and peer feedback reported greater autonomy and more critical engagement when interpreting and using feedback, whereas teacher feedback did not foster these qualities to the same degree.

The authors suggest that these differentiated effects across feedback sources point to the value of integrating multiple feedback channels in L2 writing instruction, rather than relying on a single source, to optimize how students develop as receivers and users of feedback. Within the scope of this study, combining AI, peer, and teacher feedback appears promising for cultivating different components of feedback literacy. The study's scope carries limitations worth noting.

It involved a specific population of Chinese university students over a single 10-week intervention, and the use of intact classes means pre-existing differences between groups cannot be fully ruled out. The published version is an accepted manuscript shared early to provide faster access, and it remains subject to further edits before the final Version of Record replaces it. The findings therefore describe measurable changes in this particular context rather than universal outcomes, and the authors frame their results as highlighting how different feedback sources can be combined to support writing instruction.

Subject of Research: Effects of AI, peer, and teacher feedback on university students' L2 writing feedback literacy

Article Title: Exploring the impact of AI, peer, and teacher feedback on university students’ L2 writing feedback literacy

Article References: Yan, J., & Meng, Y. (2026). Exploring the impact of AI, peer, and teacher feedback on university students’ L2 writing feedback literacy. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-09383-2

Image Credits: AI Generated

DOI: 10.1057/s41599-026-09383-2

Keywords: feedback literacy, second-language writing, AI feedback, peer feedback, teacher feedback, quasi-experimental study, Chinese university students, L2 education, writing instruction, mixed methods, Exploring, impact