How Learners Detect Revision Occasions in Texts Labeled as Peer-Written or AI-Generated

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 content of this text was in fact identical in both conditions.We found that learners descriptively 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. Revision occasions in peer-written and AI-generated textsA substantial body of research has examined how learners revise their own academic texts and those written by their peers (e.g., Rijlaarsdam et al., 2004).Recently, initial empirical evidence on learners' behaviors when revising AI-generated texts has been presented (e.g., Radtke & Rummel, 2025).However, further research is needed to examine learners' approaches to revising AI-generated content, particularly with regard to whether learners exhibit specific foci and deficits in their revision that should be considered in educational practice and research. Revision of peer-written and AI-generated textsRevision is considered a crucial stage in the writing process.In addition to improving the quality of a text, revision can serve as a 'learning tool' (Rijlaarsdam et al., 2004, p. 199): While revising a text, learners can engage in a comprehensive evaluation and internalization of its content, recall and apply previously learned rules, or even acquire new revision skills through practice.Moreover, when learners revise texts that they have not written in the first place, they can gain insight into new facts and arguments developed by their peers (Fitzgerald, 1987;Rijlaarsdam et al., 2004).Furthermore, empirical evidence suggests that revising peer-written texts can also enhance learners' individual writing and revision skills (e.g., Wichmann & Rummel, 2013).The advent of generative artificial intelligence (AI), particularly large language models (LLMs) such as GPT or BLOOM, in the field of education has major implications for learning practices (e.g., Kasneci et al., 2023).Given the capacity of LLMs to generate texts of a high quality that closely resemble the style of human writers, students worldwide are keen to use this technology, especially in academic writing (e.g., Albayati, 2024).Nevertheless, despite the initial impression of polished and coherent content, a closer examination often reveals that AIgenerated texts require substantial revision.For instance, there is a risk that AI-generated texts contain incorrect information (e.g., hallucinations; Marcus & Davis, 2020) or biased content (OpenAI, 2024).Additionally, it is not implausible that AI-generated texts may contain grammatical mistakes, particularly in non-English texts (Advertext, 2023).However, given the relative novelty of the phenomenon of AI-assisted writing, there is currently a lack of empirical evidence examining how learners engage in the revision of AI-generated academic texts.The processes of revising peer-written and AI-generated texts appear similar in that in both cases, learners are engaged in revising a text that they have not written in the first place.A further rationale for comparing these processes is the growing body of research that draws parallels between collaborative writing and AI-assisted writing in general, with revision being an essential component of both of these learning situations (e.g., Cress & Kimmerle, 2023).In this study, we provided participants with the same text to be revised, varying only the author label (peer vs. AI) between the experimental conditions.This allowed us to determine whether learners exhibit different patterns of detecting revision occasions when revising purportedly peer-written or AI-generated texts. Classification of revision occasionsWhen revising an academic text, learners need to employ two main revision skills: (1) detecting areas that require improvement (revision occasions) and (2) applying the most appropriate improvement strategies (Hayes, 2004).In this paper, we deliberately use the term revision occasions in order to emphasize the following: The revision