Algorithms as Language Managers: The Linguistic Penalty and the Rise of Anti-Bot Language, in Algerian EFL Academic Writing
| dc.contributor.author | Mansouri Rayane Moussa | |
| dc.date.accessioned | 2026-09-08T09:46:02Z | |
| dc.date.available | 2026-09-08T09:46:02Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | Universities across the world are deploying Artificial Intelligence (AI) detection tools to manage academic integrity. In Algeria, where English is taught as a foreign language (EFL), this policy has created an unexamined problem: the same writing features that EFL instruction builds into students, formulaic phrasing, standard transitions, and structured argument, are the features that detection software flags as machine-generated. This study investigates that contradiction, examining what the anti-bot language register looks like in student writing, and how Algerian higher education students and educators respond to detection pressures. The research employed a mixed-methods design, collecting data through a 21-item Likert questionnaire completed by 23 EFL students (Cronbach's α = .772), semi-structured interviews with a group of four students and two educators, non-participant observations at three academic sites, and writing sample analysis using aidetector.com. Two groups of student writers were compared: one that produced natural, unmonitored academic writing, and one that wrote knowing their text would be scanned. Results indicate that naturally written EFL essays were classified as AI-generated , despite being fully human-authored. Conversely, texts written under detection pressure, full of deliberate errors and structural disruptions, students reported consciously changing their writing style when scanned, and that writing for a detector feels fundamentally different from writing for a reader. Educators confirmed these patterns from the classroom perspective. Ultimately, this study demonstrates that better EFL academic writing is more likely to be flagged as AI-generated than deliberately degraded writing. This is not a minor technical error. It represents a structural consequence of how detection tools work, disproportionately panelizing the students who have worked hardest to master academic English | |
| dc.identifier.citation | 655 | |
| dc.identifier.uri | https://dspace.univ-sba.dz/handle/123456789/2707 | |
| dc.language.iso | en_US | |
| dc.title | Algorithms as Language Managers: The Linguistic Penalty and the Rise of Anti-Bot Language, in Algerian EFL Academic Writing | |
| dc.type | Thesis |
