Comparing generative AI and expert feedback to students’ writing: Insights from student teachers
Journal article › Research › Peer reviewed
Publication data
By | Thorben Jansen, Lars Höft, Luca Bahr, Johanna Fleckenstein, Jens Möller, Olaf Köller, Jennifer Meyer |
Original language | English |
Published in | Psychologie in Erziehung und Unterricht, 71(2) |
Pages | 80-92 |
Editor (Publisher) | Ernst Reinhardt Verlag |
ISSN | 0342-183X |
DOI/Link | https://doi.org/10.2378/peu2024.art08d |
Publication status | Published – 04.2024 |
Feedback is crucial for learning complex tasks like writing; yet its creation is time-consuming, often leading to students receiving insufficient feedback. Generative artificial intelligence, particularly Large Language Models (LLMs) like ChatGPT 3.5-Turbo, has been discussed as a solution for providing more feedback. However, there needs to be more evidence that AI-feedback already meets the quality criteria for classroom use, and studies have yet to investigate whether LLM-generated feedback already seems useful to its potential users. In our study, 89 student teachers evaluated the usefulness of feedback for students’ argumentative writing, comparing LLM against expert-generated feedback without receiving information about the feedback source. Participants rated LLM-generated feedback as useful for revision in 59 % of texts (compared to 88 % for expert feedback). 23 % of the time, participants preferred to give LLM-generated feedback to students. Our discussion focuses on the conditions in which AI-generated feedback might be effectively and appropriately used in educational settings.