How Learners Detect Revision Occasions in Texts Labeled as Peer-Written or AI-Generated
Radtke, Anna Ruan, Qian Gurevych, Iryna Rummel, Nikol
Radtke, Anna
Ruan, Qian
Gurevych, Iryna
Rummel, Nikol
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Natural Language Processing
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Conference proceeding
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2025
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English
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Abstract
Although the potential of large language models in education has been widely recognized, research on how learners revise AI-generated content remains limited. The aim of this study was to determine whether learners detect different revision occasions when revising texts labeled as written by different authors (peer vs. AI). In a controlled study, learners (N = 152) revised a text that was labeled as either peer-written or AI-generated, while the con-tent of this text was in fact identical in both conditions. We found that learners descrip-tively detected fewer surface-level revision occasions in a text labeled as AI-generated, although this difference was not statistically significant. No notable difference was found for semantic (i.e., meaning-changing) revision occasions.
Citation
A. Radtke, Q. Ruan, I. Gurevych, and N. Rummel, “How Learners Detect Revision Occasions in Texts Labeled as Peer-Written or AI-Generated,” Proceedings of the 19th International Conference of the Learning Sciences - ICLS 2025, pp. 1168–1172, Jun. 2025, doi: 10.22318/ICLS2025.473453
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Proceedings of the 19th International Conference of the Learning Sciences, 2025
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19th International Conference of the Learning Sciences - ICLS 2025
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19th International Conference of the Learning Sciences - ICLS 2025
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International Society of the Learning Sciences