peer-feedback
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- PaperAssessing Writing21 Jul 2026
Student-AI collaboration in peer feedback: Effects on perceived feedback quality, emotional responses, and feedback literacy development
Hua Wang, Kai Guo
A quasi-experimental study with 60 Chinese EFL undergraduates found that student reviewers using generative AI (Doubao) for feedback generation in peer assessment produced progressively higher-quality feedback (in affect, description, justification, constructiveness) and reported greater task enjoyment and lower anxiety than a control group. The intervention also enhanced all dimensions of feedback literacy, including knowledge, willingness, cooperative learning, and appreciation of peer feedback.
Original abstract
This study investigates how English as a foreign language (EFL) student reviewers engage in open-ended, dialogic interactions with generative artificial intelligence (AI) during the feedback generation process in peer assessment within EFL writing classrooms. It examines the impact of these interactions on feedback quality perceived by recipients, emotional responses (task enjoyment and anxiety), and feedback literacy. A quasi-experimental design was employed with 60 Chinese undergraduate students, divided into an experimental group (EG) that used generative AI (Doubao) for support and a control group (CG) that did not. Over three intervention cycles, data from chat histories, feedback quality ratings by recipients, and pre/post questionnaires on emotions and feedback literacy were analyzed. The results indicated that EG students primarily employed AI for linguistic refinement of their comments, with limited use for enhancing the content or structure. Nevertheless, AI support led to significant, progressive improvements in the perceived quality of feedback, particularly in affect, description, justification, and constructiveness. Furthermore, EG students reported significantly higher task enjoyment and lower anxiety compared to the CG. The intervention also positively enhanced all dimensions of feedback literacy: knowledge and abilities, willingness to participate, cooperative learning, and appreciation of peer feedback. The findings suggest that generative AI can serve as a powerful scaffold, reducing the emotional and cognitive burdens of peer assessment while fostering a more supportive and effective feedback environment. This study underscores the value of integrating AI into peer feedback practices to develop students’ feedback literacy and improve the overall quality of peer learning experiences.
- PaperLanguage Teaching Research9 Jul 2026
Exploring Teacher Agency in Conducting Peer Feedback Activities in English-as-a-Foreign-Language Writing: An Ecological Perspective
Yao Lu, Ying Gao, Tiantian Xu
Two EFL writing teachers exhibited contrasting patterns of agency in conducting peer feedback activities, with a novice teacher shifting from proactive to constrained agency and an experienced teacher moving from automatic to autonomous agency, shaped by temporal affordances and constraints linked to individual and contextual factors. The findings highlight the need for tailored support to enhance teacher agency in facilitating peer feedback.
Original abstract
Although the learning benefits of peer feedback in English-as-a-foreign-language writing instruction have been well recognized, the process and effects of peer feedback are largely reliant on teacher agency in conducting such activities. However, inadequate research has been conducted on this issue, leaving the dynamic features and causes of teacher agency in peer feedback largely unknown. Drawing on an ecological perspective, this comparative case study examined how two English-as-a-foreign-language teachers from two universities enacted agency in organizing peer feedback activities in their writing instructions. Multiple data including classroom observation, interviews and relevant teaching documents were collected and coded for themes. The study revealed that both teachers exerted dynamic agency in creating peer feedback experiences for students, but showed considerable differences before, during and after peer feedback. Overall, the novice teacher shifted from showing proactive agency to constrained agency, whereas the experienced teacher moved from demonstrating automatic agency to autonomous agency. This stark contrast was shaped by the synergism of different temporal affordances and constraints in relation to individual and contextual factors. The study highlights the need to provide tailored support to enhance teacher agency in dealing with possible challenges in facilitating students’ experiences with peer feedback.
- 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 compared AI-generated and peer feedback in an interpreting course using think-aloud methods. Findings showed peer feedback offered greater developmental sensitivity and relational grounding, while AI was comprehensive but emotionally neutral. The analysis led to the CARE framework (Care respect, Attainable goals, Relational recognition, Emphasized problem identification) for understanding feedback effectiveness beyond content coverage.
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.
- PaperAssessment & Evaluation in Higher Education31 May 2026
Integrating GenAI feedback and dialogic peer feedback to improve academic writing performance and revision practices: a quasi-experimental study
Sihui Li, Siyao Wang, Yating Huang
A quasi-experimental study compared the effects of GenAI feedback alone, dialogic peer feedback alone, and their combination on undergraduate academic writing performance and revision behaviors. The combination most effectively enhanced writing performance, promoted sustained improvement, and led to more full and extra revisions. Interviews revealed that while GenAI alone provided structural guidance but imposed cognitive burden, combining it with dialogic peer feedback created a shared interpretive space that fostered collaborative negotiation and transformed students from passive recipients into agentic participants.
Original abstract
While GenAI offers unprecedented efficiency in delivering feedback, its use often reinforces transmission-oriented models that position undergraduate students as passive recipients. This study investigated how combining GenAI feedback with dialogic peer feedback influenced undergraduate students’ academic writing performance and revision behaviours through a quasi-experimental design. The results revealed that the combination of GenAI feedback and dialogic peer feedback most effectively enhanced academic writing performance and produced more sustained improvement. In addition, the GenAI feedback combined with dialogic peer feedback generated a higher frequency of full and extra revisions, whereas reliance on GenAI feedback alone more often resulted in partial revisions or non-implementation. Semi-structured interviews revealed distinct responses across conditions: GenAI feedback alone provided initial structural guidance but imposed cognitive burden and limited interpretive support; in contrast, GenAI feedback combined with dialogic peer feedback created a shared interpretive space that enabled collaborative negotiation and supported undergraduate students’ transformation from passive recipients into agentic participants. These findings advocate for student-centered approaches to GenAI integration, demonstrating how collaborative dialogue transforms GenAI output from prescriptive information into meaningful learning resources.