Interactional Metadiscourse Markers in the Era of Artificial Intelligence: An Analysis of Human and GPT-Generated Academic Writing

Document Type : Research Paper

Authors

1 English Department, Faculty of Letters & Humanities, Shahrekord University, Iran

2 English Department, Faculty of Letters & Humanities, Shahrekord University, Iran

Abstract

As the boundaries between human and machine-authored academic texts continue to blur, there is a compelling need to evaluate how artificial intelligence (AI)-generated content navigates the landscape of interpersonal engagement and persuasion in research articles. The present investigation aimed to examine the functional role of interactional metadiscourse markers in academic texts authored by humans versus those generated by ChatGPT. Relying on Hyland’s (2005) metadiscourse framework, this research study compared the frequency, rhetorical function, and contextual use of five key interactional markers in the conclusion and discussion sections of 40 applied linguistics research articles. The results, derived from a mixed-methods approach of corpus-based discourse analysis, revealed significant differences in rhetorical style and communicative intent. The human-authored texts employed more hedges, engagement markers, and attitude markers, indicating a more cautious, evaluative, and reader-aware stance. In contrast, the AI-generated texts overused self-mentions and lack contextual subtlety, often resulting in a more expository and impersonal tone. These findings highlight the current limitations of AI in replicating the rhetorical sensitivity required in academic writing and suggest the continued need for critical human oversight in AI-assisted scholarly communication.

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