Artificial Intelligence and Academic Integrity: Legislate or Educate?
Bibliographic record
Abstract
This participatory action research intends to bridge the gap between generative artificial intelligence (GenAI) tools and academic integrity in a Midwestern university in the United States. Using mixed methods, researchers analysed participants’ perceptions and strategies for using GenAI tools while upholding academic integrity. The study highlighted the institution’s strengths, weaknesses, opportunities, and challenges in grappling with the issue. The results pointed to the need for the critical use of GenAI tools and guidelines for tackling the multifaceted impacts of technology. The study contributes to the field with strategies to empower educators and learners in navigating the ethical landscape of GenAI in education.
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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 | 0.061 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.046 |
| Scholarly communication | 0.025 | 0.013 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".