Academic Fraud in the Use of Generative Artificial Intelligence (GenAI) for Faculty Promotion and Tenure
Bibliographic record
Abstract
Since its emergence in 2022 through OpenAI, generative artificial Intelligence (GenAI) has represented a major technological breakthrough with the potential to revolutionize higher education systems. However, in addition to being a potentially helpful work tool, GenAI can also enable academic fraud. The purpose of this manuscript is to propose the foundations of a legal framework for addressing academic fraud in university faculty promotion and tenure that is facilitated by the use of GenAI. This manuscript begins with an introduction outlining how current GenAI capabilities could be used to engage in academic fraud. The manuscript then examines the underlying ethical systems in higher education that underpin decisions to utilize GenAI broadly, as well as more specifically in the creative process of scholarship. This discussion is followed by a section that explains the incentives to engage in academic fraud caused by national policies and university systems governing the promotion and tenure of faculty members in the United States (U.S.) and Spain/Europe. The legal framework at the end of this manuscript provides policymakers in government and university administration with interrelated concepts to guide the drafting of new policies that would govern the use of GenAI in academic scholarship. As stated in the concluding section, the authors have designed an empirical study to test the response to their proposed legal framework among faculty researchers, considering their systems of ethics and incentives that undergird temptations to engage in academic fraud. The authors present this manuscript as a primer for that future study.
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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 | 0.059 | 0.201 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".