Leveraging Generative Artificial Intelligence with Transparency: Enhancing Academic Integrity in Higher Education
Bibliographic record
Abstract
Generative artificial intelligence (GenAI) tools have brought substantial changes to the education system, in particular to the writing process. Students must learn how to use these tools correctly and with integrity. It is the role of institutions and instructors to convey to students the importance of transparency, and how to display it when using GenAI tools to write assignments. In this paper, a theoretical model of academic assignment writing is presented, explaining at which phases of the writing process GenAI can be used with integrity and which phases must absolutely be done by the student. The appropriate use of GenAI tools when deploying informational, writing and referencing competencies to write an assignment is discussed. Advice on modifying existing institutional academic integrity policies, giving clear guidelines and permissions are presented as well as various methods of declaring GenAI usage.
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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 | 0.030 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| 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".