Peer feedback is introduced as an effective learning strategy, especially in large-size classes where teachers face high workloads. However, for complex tasks such as writing an argumentative essay, without support peers may not provide high-quality feedback since it requires a high level of cognitive processing, critical thinking skills, and a deep understanding of the subject. With the promising developments in Artificial Intelligence (AI), particularly after the emergence of ChatGPT, there is a global argument that whether AI tools can be seen as a new source of feedback or not for complex tasks. The answer to this question is not completely clear yet as there are limited studies and our understanding remains constrained. In this study, we used ChatGPT as a source of feedback for students’ argumentative essay writing tasks and we compared the quality of ChatGPT-generated feedback with peer feedback. The participant pool consisted of 74 graduate students from a Dutch university. The study unfolded in two phases: firstly, students’ essay data were collected as they composed essays on one of the given topics; subsequently, peer feedback and ChatGPT-generated feedback data were collected through engaging peers in a feedback process and using ChatGPT as a feedback source. Two coding schemes including coding schemes for essay analysis and coding schemes for feedback analysis were used to measure the quality of essays and feedback. Then, a MANOVA analysis was employed to determine any distinctions between the feedback generated by peers and ChatGPT. Additionally, Spearman’s correlation was utilized to explore potential links between the essay quality and the feedback generated by peers and ChatGPT. The results showed a significant difference between feedback generated by ChatGPT and peers. While ChatGPT provided more descriptive feedback including information about how the essay is written, peers provided feedback including information about identification of the problem in the essay. The overarching look at the results suggests a potential complementary role for ChatGPT and students in the feedback process. Regarding the relationship between the quality of essays and the quality of the feedback provided by ChatGPT and peers, we found no overall significant relationship. These findings imply that the quality of the essays does not impact both ChatGPT and peer feedback quality. The implications of this study are valuable, shedding light on the prospective use of ChatGPT as a feedback source, particularly for complex tasks like argumentative essay writing. We discussed the findings and delved into the implications for future research and practical applications in educational contexts.

同伴反馈被作为一种有效的学习策略引入，特别是在教师面临高工作量的大班级中。然而，对于撰写议论文等复杂任务，如果没有同伴的支持，可能无法提供高质量的反馈，因为它需要高水平的认知处理、批判性思维技能和对主题的深刻理解。随着人工智能（AI）的蓬勃发展，特别是 ChatGPT 的出现之后，全球范围内出现了关于人工智能工具是否可以被视为复杂任务的新反馈来源的争论。这个问题的答案尚不完全清楚，因为研究有限，我们的理解仍然有限。在本研究中，我们使用 ChatGPT 作为学生议论文写作任务的反馈来源，并将 ChatGPT 生成的反馈的质量与同行反馈进行了比较。参与者由来自荷兰大学的 74 名研究生组成。该研究分两个阶段进行：首先，收集学生就某一特定主题撰写论文时的论文数据；随后，通过让同行参与反馈过程并使用 ChatGPT 作为反馈源来收集同行反馈和 ChatGPT 生成的反馈数据。采用论文分析编码方案和反馈分析编码方案两种编码方案来衡量论文和反馈的质量。然后，采用多元方差分析来确定同行生成的反馈和 ChatGPT 之间的任何区别。此外，还利用 Spearman 相关性来探索论文质量与同行和 ChatGPT 生成的反馈之间的潜在联系。结果显示 ChatGPT 和同行生成的反馈之间存在显着差异。虽然 ChatGPT 提供了更多描述性反馈，包括有关如何撰写论文的信息，但同行提供的反馈包括有关识别论文中问题的信息。对结果的总体观察表明 ChatGPT 和学生在反馈过程中具有潜在的互补作用。关于论文质量与 ChatGPT 和同行提供的反馈质量之间的关系，我们发现总体上不存在显着关系。这些发现意味着论文的质量不会影响 ChatGPT 和同行反馈的质量。这项研究的意义很有价值，它揭示了 ChatGPT 作为反馈源的预期用途，特别是对于议论文写作等复杂任务。我们讨论了这些发现，并深入探讨了对未来研究和教育背景下实际应用的影响。