Academic Integrity and Artificial Intelligence in Higher Education Contexts: A Rapid Scoping Review Protocol
Bibliographic record
Abstract
This paper presents a protocol with methodological considerations for a rapid scoping review on academic integrity and artificial intelligence in higher education. This protocol follows Joanna Brigg Institute’s (JBI) updated manual for scoping reviews and the Preferred Reporting Items for Systematic reviews Meta-Analysis (PRISMA) reporting standards. This rapid scoping review aims to identify the breadth of the literature reflecting the intersection of academic integrity and artificial intelligence in higher education institutions. The included studies in the review will be analyzed for insight concerning this emerging area, particularly its ethical implications. Our findings will be relevant for academic staff, administration, and leadership in higher education and academic integrity researchers.
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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 | 0.228 | 0.295 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.134 | 0.044 |
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".