Teaching and learning at the intersection of artificial intelligence, academic integrity, and assessment innovation: A rapid scoping review protocol
Bibliographic record
Abstract
For three years, teachers and teacher educators have been struggling to respond to the advent of generative artificial intelligence (GenAI) and its implications for teaching and learning. Building on DeLuca et al.’s (2025) AI3 framework, this paper is situated at the intersections of artificial intelligence, academic integrity, and assessment innovation: three critical ‘AIs’ for the current moment. Owing to a dearth of literature reviews at this intersection, this paper presents a rapid scoping review protocol for AI3 in preservice teacher education. The protocol follows the Joanna Briggs Institute’s updated manual for scoping reviews and the Preferred Reporting Itemsfor Systematic Reviews Meta-Analysis (PRISMA) reporting standards (Aromataris & Munn, 2020). The studies included in the review will be analyzed for insights on these topics, particularly the innovative practices possible in an age of GenAI. Our findings will be relevant to teacher educators in particular and more broadly to educational researchers and practitioners interested in integrity, innovation, and defensible practice in ever-shifting GenAI spaces.
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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 | 0.202 | 0.233 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.085 | 0.024 |
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".