Gendered Syntax in AI-Assisted Academic Writing in Nigerian Universities
Bibliographic record
Abstract
This study examined gendered syntactic patterns in AI-assisted academic writing among students in the Department of English and Literature, Alvan Ikoku Federal University of Education, Owerri. This study was motivated by growing concerns that AI-assisted writing tools, while supporting academic literacy, may unconsciously reproduce sociocultural biases through sentence structure. Anchored on Halliday’s Systemic Functional Linguistics (SFL) theory, the analysis focused on transitivity patterns, agency, and participant roles in selected academic texts. The data comprised a purposively selected corpus of AI-assisted and human-written academic texts. These texts were analyzed to determine how material, relational, and mental processes, as well as active and passive constructions, were used to represent gendered subjects. The findings indicated that AI-assisted texts frequently reproduced conventional syntactic patterns that foregrounded masculine agency while backgrounding feminine roles through passivation and relational clauses. These tendencies reflected broader sociocultural ideologies embedded in language use rather than deliberate technological bias. The study concluded that although AI-assisted academic writing enhanced textual organization and linguistic accuracy, it can subtly reproduce gendered discursive patterns. This study, therefore, recommended integrating critical language awareness and responsible AI literacy into academic writing instruction to promote more gender equitable language practices in Nigerian universities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".