A comparison of rhetorical move analysis by GPT-4 and humans in abstracts of scopus-indexed tourism research articles

AI advancements have made ChatGPT a remarkable and versatile tool in education and linguistics, showcasing its potential to mimic human conversation and comprehend language. Scholars are intrigued by ChatGPT’s text data handling, yet its application in rhetorical move analysis remains largely unexplored. Therefore, the objective of this study is to investigate the ability of GPT-4 in the identification of rhetorical moves employed in the abstracts of tourism research articles indexed in Scopus. The essentiality of moves was also reported. Additionally, this research seeks to compare the accuracy of GPT-4’s analysis with that of humans. Adopting Hyland’s (2000) fivemove model, the results indicated that GPT-4 analyzes moves more quickly but less accurately than human experts, and the four principal types of errors committed by GPT-4 include redundancy/over-count, unmatched categorization, incorrect sequence, and vague identification. The findings also revealed that Move 2 (Purpose) and Move 4 (Findings) are obligatory with a 100% essentiality rate through both GPT-4 and human analysis. Differences arise in certain steps of Move 1 (Introduction), Move 3 (Methods), and Move 5 (Conclusion), where GPT-4 often sees higher essentiality rates. This study shed light on the testament to AI’s current capabilities in move analysis in academic discourse.

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