Interacting with ChatGPT for internal feedback and factors affecting feedback quality

Abstract

Research consistently shows the importance of teacher and peer feedback in enabling students to generate internal feedback for improved learning. However, the utilisation of Generative Pre-Trained Transformer (ChatGPT) to support higher education students in generating internal feedback lacks clarity. This qualitative case study aimed to explore students’ experiences with ChatGPT as they sought assistance in generating internal feedback and to identify factors that influence the quality of feedback provided by ChatGPT. Forty-eight Chinese students with varying levels of writing proficiency participated in an English writing course, and data were collected through self-reflective questions, interviews, and conversation logs with ChatGPT. Qualitative data were analyzed using thematic coding. This qualitative case study aimed to explore students’ experiences with ChatGPT as they sought assistance in generating internal feedback and to identify the factors that influence the quality of feedback provided by ChatGPT. The quality of ChatGPT’s feedback was influenced by factors such as learners’ personal goals, the utilisation of the system’s multilingual capability, and their self-regulation writing strategies, which in turn affect the quality of prompts. These findings offer practical insights into the use of ChatGPT for generating internal feedback in second language (L2) writing.

Keywords:

• Internal feedback
• ChatGPT
• L2 writing
• feedback quality

Acknowledgements

I would like to express my gratitude to Professor Gail Forey and the anonymous reviewers for their valuable feedback in improving this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).