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Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F. et al. (2023). ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education. Learning and Individual Differences, 103, Article ID: 102274.
https://doi.org/10.1016/j.lindif.2023.102274

has been cited by the following article:

• TITLE: The Transfer of Junior High School Students’ English Writing in the Context of Generative AI Feedback and Its Relationship with Feedback Literacy and Self-Regulated Learning

  AUTHORS: Xiaoqing Sheng

  KEYWORDS: Generative Artificial Intelligence, English Writing, Writing Transfer, Feedback Literacy, Self-Regulated Learning, Junior High School Student

  JOURNAL NAME: Open Journal of Social Sciences, Vol.14 No.11, November 12, 2026

  ABSTRACT: To investigate the transfer performance of junior high school students’ English writing under the condition of generative artificial intelligence feedback and its relationship with feedback literacy and self-regulated learning, 82 eighth-grade students were selected as the subjects, and the independent writing performance under the two teaching schemes of generative AI feedback and teacher feedback was compared using the unequal pre-test-post-test-delayed calibration experimental design of two natural classes, and the analysis was carried out in combination with questionnaires, feedback judgment tasks, and revised records. The results show that after controlling for pre-test writing scores, the adjusted average delayed-transfer score in the AI feedback class is 5.99 points higher than in the teacher feedback class (P = 0.002). There is a positive correlation between the change in feedback literacy and delayed transfer performance (r = 0.413, P