Examining AI Guidelines in Canadian Universities: Implications on Academic Integrity in Academic Writing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Academic integrity is a crucial aspect of higher education that fosters intellectual honesty and upholds the principles of fairness and trustworthiness (Stoez & Eaton, 2020; Kang, 2022; Eaton, 2022). As the introduction and integration of artificial intelligence (AI) technologies becomes increasingly prevalent in educational settings, it is imperative to examine how Canadian universities are addressing the implications of AI on academic integrity (Eaton, 2022; UNESCO, 2023). This study aimed to examine the existing AI guidelines and policies developed and implemented by Canadian universities and analyze their alignments and gaps in relation to their academic integrity policies, particularly in the domain of academic writing in Canadian higher education contexts. In this research study, sixteen Canadian universities were selected for document analysis, and through an examination of their existing polices and guidelines on AI, results revealed insights into both challenges and opportunities for faculty, students and stakeholders around teaching academic writing while upholding academic integrity in higher education.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Qualitative | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | high |
| opus | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Qualitative | medium |
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.007 |
| 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 it