ai-feedback
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- PaperComputers & Education1 Jul 2026
Developing L2 writing student self-assessment literacy through AI-generated feedback and AI chain-of-thought: An action research study with lower-proficiency university EFL learners
Pan Ye, Shulin Yu, Icy Lee, Chenggang Liang
An action research study investigates how AI-generated feedback and AI chain-of-thought promote self-assessment literacy in L2 writing among lower-proficiency university EFL learners.
- PaperAssessment & Evaluation in Higher Education23 Jun 2026
AI in the gatekeeper’s chair: elite researchers’ perceptions of AI-assisted feedback in journal peer review
Heng Li
Elite researchers (Nature/Science authors) perceive AI-assisted peer reviews as less fair, useful, and acceptable than human reviews, and show aversion to both the AI itself and researchers who delegate reviews to AI. This suggests AI integration may erode trust in peer review unless human oversight is preserved.
Original abstract
Peer review serves as the structural foundation of scientific integrity, yet the system currently faces unprecedented strain. In response, AI tools are being deployed to assist human expert review, a development that has generated considerable debate. However, the psychological impact of this transition on the scientific community remains only partially understood. Our study utilises a sequential mixed-methods approach to examine the perceptions of AI-mediated feedback among a cohort of elite researchers (Nature and Science authors). Quantitative findings from our randomised experimental survey (N = 495) demonstrated that AI-assisted reviews were viewed as deficient in fairness, usefulness, and acceptance compared to human-led evaluations. Qualitative evidence from 47 in-depth interviews further identified a dual-layered aversion to AI-assisted feedback, concerning both the technology (AI use) and the agent (AI user). Specifically, scholars perceived AI-generated critiques as devoid of the necessary disciplinary nuance required for high-stakes evaluation. Moreover, a notable ‘AI user aversion’ emerged: reviewers who delegated tasks to AI were perceived as lacking the diligence and empathic engagement essential to the peer-review contract. Together, these finding suggest that the integration of AI into peer review may erode trust in the research evaluation process and journals should implement robust governance that preserves human oversight.
- PaperJournal of Second Language Writing20 Jun 2026
Question-only AI Socratic dialogue as dialogic feedback in L2 argumentative writing: A quasi-experimental study
Li-Jen Wang
The paper reports a quasi-experimental study investigating the use of question-only AI Socratic dialogue as a form of dialogic feedback for L2 argumentative writing.
- PaperAssessing Writing8 Jun 2026
Examining linguistic reasoning and metalinguistic strategies in L2 writing: Insights from teacher- and AI-feedback revisions
Huican Huo, Lawrence Jun Zhang
A longitudinal study of 10 L2 writers over 12 weeks found that repeated feedback-revision cycles with teacher and AI suggestions shifted learners from correctness-focused edits to deeper metalinguistic reasoning, including structural comparison, self-explanation, and integrative argumentation. Composite reasoning scores increased, with largest gains in depth of explanation, and patterns emerged linking feedback characteristics to specific reasoning behaviors.
Original abstract
Second language (L2) writers’ capacity to reason about language choices during revision is theoretically central to advanced writing development, yet few studies trace how this linguistic reasoning emerges in repeated feedback-revision cycles that integrate teacher and AI suggestions. This multi-case, longitudinal mixed-methods study examined how ten L2 learners enacted metalinguistic strategies and developed linguistic reasoning across a 12-week online Academic English program. Triangulated data sources included 1246 feedback revision episodes, pre-post reasoning-task responses, and semi-structured exit interviews. We operationalized linguistic reasoning as the processual construct of interest and treated metalinguistic strategies as observable indicators; reasoning responses were scored with a three-dimensional analytic rubric while revisions and interaction logs were coded thematically and by strategy. Results show a systematic shift from early, correctness-focused edits toward later revisions characterized by coordinated structural comparison, more explicit self-explanation, and integrative argumentation; composite reasoning scores increased, with the largest gains observed on depth of explanation. Process analyses identified recurrent patterns in which alternative-rich feedback coincided with comparison moves, partially divergent suggestions often coincided with more explicit self-explanation, and integrative argumentation involving discourse-level revision became more visible over time. Pedagogical implications are also discussed.
- PaperAssessing Writing3 Jun 2026
Using GenRewrite to provide personalized feedback for form-function alignment in EAP writing
Yizhe Wei, Yue Wang, Tan Jin
The study introduces GenRewrite, a tool that provides personalized feedback to help EAP writers align form and function in their writing.
- PaperAssessment & Evaluation in Higher Education1 Jun 2026
The human touch of feedback: students’ experiences of CARE in peer versus AI-generated feedback
Lan Li, Jiming Zhou
A study comparing AI and peer feedback in an interpreting course found that while AI offered comprehensive, criterion-based comments with a positive tone, peer feedback demonstrated greater developmental sensitivity and relational grounding. Students valued the contextual understanding and authentic support from peers, leading to the CARE framework (Care respect, Attainable goals, Relational recognition, Emphasized problem identification). The findings suggest that productive AI integration should design complementary systems rather than simulating human touch.
Original abstract
The integration of AI-generated feedback into higher education has increased feedback volume and efficiency. Yet concerns persist that it lacks the ‘human touch’, a construct that remains undertheorised and empirically unexamined. To examine what constitutes the human touch, this study compared AI and peer feedback in an interpreting course, capturing 41 university students’ immediate responses through the think-aloud method across seven weeks. Analysis revealed that whereas AI provided comprehensive, criterion-based commentary with a more positive tone, peer feedback demonstrated greater developmental sensitivity and relational grounding. Students showed emotional indifference to AI feedback but valued the contextual understanding and authentic support that peer feedback provided. These patterns informed the empirically grounded CARE framework: Care respect, Attainable goals, Relational recognition, and Emphasised problem identification. Each CARE dimension depends on qualities emerging from shared participation in learning communities that algorithmic systems struggle to replicate. Theoretically, CARE offers concrete dimensions for understanding feedback effectiveness beyond content coverage. Productive AI integration requires not simulating human touch but designing complementary systems that leverage the strengths of different feedback sources. The presence of human feedback providers does not guarantee the human touch, either. The CARE dimensions demand deliberate assessment design and invite further exploration.