Using genre analysis to detect AIGenerated academic texts
| dc.contributor.author | Melliti, Mimoun | |
| dc.date.accessioned | 2024-11-18T21:06:52Z | |
| dc.date.available | 2024-11-18T21:06:52Z | |
| dc.date.issued | 2024-07 | |
| dc.description.abstract | This study investigates the distinguishing characteristics between human-written and AI-generated abstracts through genre analysis techniques. The research examined mini-memoir abstracts authored by MA2 students at Faculty of Arts and Humanities, University of Kairouan, Tunisia and compared them to AI-generated abstracts created specifically for this study using ChatGPT. The analysis focused on text function recurrence, specifically the frequency and quality of elements such as purpose statements, methodology, results, and contextualization. Findings revealed that human-written abstracts exhibit a more comprehensive and detailed presentation, emphasizing contextualization and thorough results, while AI-generated abstracts tend to prioritize clear and explicit purpose statements with less depth in results and contextual information. The study highlights the need for targeted teacher training and rigorous assessment criteria to uphold academic integrity and address the challenges posed by AI in scholarly writing. | |
| dc.format | 19 p. | |
| dc.identifier.citation | Melliti, M. (2024). Using genre analysis to detect AI-Generated academic texts. Diá-Logos, 16(29), 09–27. | |
| dc.identifier.doi | https://doi.org/10.61604/dl.v16i29.377 | |
| dc.identifier.issn | 2958-9754 | |
| dc.identifier.uri | http://hdl.handle.net/11715/2742 | |
| dc.language.iso | en | |
| dc.publisher | Editorial Universidad Don Bosco | |
| dc.subject | Genre Analysis | |
| dc.subject | AI-generated Texts | |
| dc.subject | Academic Abstracts | |
| dc.subject | Human-AI Comparison | |
| dc.title | Using genre analysis to detect AIGenerated academic texts | |
| dc.type | Article |