In higher education, generative AI is increasingly framed as a solution to longstanding problems in student feedback, particularly where peer feedback is experienced as uneven, superficial, or linguistically unreliable (Kerman et al., 2024). This issue may be especially acute in second-language (L2) writing contexts, where the effectiveness of peer feedback depends not only on participation but also on trust, engagement, and the ability to provide usable commentary (Yu & Lee, 2016; Zhang & Hyland, 2023; Huseynli, 2024). Drawing on findings from a dissertation study conducted in an L2 higher-education context in Pakistan, this paper argues that students’ preference for AI feedback should not be understood simply as a matter of efficiency or feedback quality (Mahmood, 2025). In that study, students tended to perceive AI feedback as more comprehensive, specific, and accurate, while peer feedback was seen as more natural and friendly but less systematic and dependable (Mahmood, 2025). The paper uses these findings to argue that AI is being normalized not only because it performs certain feedback functions well, but because it enters a space in which trust in peers, shared responsibility, and cultures of care have already weakened. Situating these developments within scholarship on L2 peer feedback, dialogical feedback and care, and critiques of techno-solutionism in education (Bozalek et al., 2016; UNESCO, 2023), the paper contends that AI risks becoming a crutch that allows institutions to bypass the harder work of rebuilding human feedback relations. Rather than resolving a feedback crisis, AI may deepen the erosion of relational pedagogy by substituting technically effective but socially thinner forms of support.
References
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