GenAI and Academic Integrity: Redefining the Role of Writing Centres in a Shifting Digital Landscape
Bibliographic record
Abstract
This article investigates the transformative effects of generative artificial intelligence (GenAI) on Academic Integrity and the role of writing centres (WCs) in post-secondary institutions. By exploring the scientific, philosophical, and educational perspectives on the current development of GenAI in academia, the article highlights the opportunities for WC administrators and staff to embrace change. Suggestions include becoming familiar with new discourse communities, cultivating new relationships with colleagues, redefining WC pedagogy and culture, incorporating new digital literacies into training, engaging in the ongoing development and update of academic integrity policies, and developing new support networks across WCs. The objective of this article is to offer insights into how WCs can adapt to this new landscape and ensure their continued relevance within the larger educational ecosystem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.007 | 0.028 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.011 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".