Feedback sources in essay writing: peer-generated or AI-generated feedback?

• Published in: International Journal of Educational Technology in Higher Education Vol. 21; no. 1; pp. 15 - 23
• Main Authors: Banihashem, Seyyed Kazem, Kerman, Nafiseh Taghizadeh, Noroozi, Omid, Moon, Jewoong, Drachsler, Hendrik
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2024; Springer Nature B.V; SpringerOpen
• Subjects: AI-generated feedback; Artificial intelligence; Chatbots; ChatGPT; Coding; Colleges & universities; Computer Appl. in Social and Behavioral Sciences; Computer Science; Computers and Education; Critical Thinking; Educational Technology; Essay writing; Essays; Feedback; Feedback sources; Graduate students; Higher Education; Humanities; Information Systems Applications (incl.Internet); Law; Learning Strategies; Peer feedback; Peers; Research Article; Statistics for Social Sciences; Student writing; Task complexity; Thinking Skills; AI-generated feedback; Essay writing; Peer feedback; Feedback sources; Higher education; ChatGPT
• DOI: 10.1186/s41239-024-00455-4
• ISSN: 2365-9440

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.