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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.035 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.015 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".