Peer Feedback with ChatGPT: A Case Study on the Customization of Feedback in Higher Education
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Abstract
Despite the pedagogical benefits of peer feedback, challenges remain in consolidating diverse comments and providing customized feedback to each student or group. Generative AI (GenAI), such as ChatGPT, offers a promising solution to these limitations by leveraging its LLM capabilities to process, restructure, and consolidate the peer feedback. This study examined the effectiveness and usability of AI-supported customized feedback derived from peer evaluation data. 30 pre-service teachers at a South Korean university developed technology-integrated teaching-learning scenarios and exchanged peer feedback using detailed rubrics. ChatGPT was employed to process and restructure the feedback into more coherent and tailored reports. Data were collected through surveys and interviews, which revealed that learners perceived the AI-generated feedback as effective, usable, and supportive of their revision processes. The findings highlight the potential of GenAI within the framework of AI-Mediated Assessment, serving as a mediator that restructures and customizes peer feedback into a more effective form of personalized feedback for learners.
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