INTEGRATION OF AI INTO PEER FEEDBACK: STUDENTS’ PERCEPTIONS AND EXPERIENCES IN A PROGRAMMING COURSE

Date

2026-4-21

Author

Bashirzadeh, Motahhareh

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As Artificial Intelligence (AI) becomes increasingly integrated into Computer Science Education (CSE), many existing approaches have focused on automating feedback processes, primarily emphasizing error detection and correction. However, this focus often overlooks the pedagogical importance of formative feedback that supports student understanding. This study investigates the role of AI not as a replacement for human instruction, but as a supportive "co-pilot" designed to enhance the quality and depth of peer feedback. The research consists of two main components. First, a systematic literature review on feedback mechanisms identifies a gap where automation frequently prioritizes efficiency over meaningful engagement. Based on these findings, the study proposes the Multi-Layered AI-Augmented Peer Feedback (MLA-PF) framework, consisting of three layers: diagnostic (identifying structural and conceptual issues), augmentation (supporting constructive peer feedback generation), and human-in-the-loop (ensuring pedagogical alignment). Second, an AI-assisted platform grounded in this framework was evaluated through a pilot study. Findings indicate that students perceived the AI-assisted feedback as effective, with qualitative evidence suggesting meaningful engagement rather than superficial correction. Students made substantive revisions to logical errors and control flow, with over 85% incorporating feedback into their work. By repositioning AI as a tool to scaffold critical thinking, this research offers a grounded approach to enhancing programming education, providing a foundation for future scalability and impact on learning outcomes.

Subject Keywords

Artificial Intelligence (AI), Automated Feedback, Peer Feedback, Programming Education, Formative feedback

URI

https://hdl.handle.net/11511/119419

Collections

Graduate School of Natural and Applied Sciences, Thesis