Redefining Academic Integrity in the Age of Generative Artificial Intelligence: The Essential Contribution of Artificial Intelligence Ethics
Bibliographic record
Abstract
The widespread adoption of generative artificial intelligence (AI) tools has profoundly impacted higher education, reshaping how knowledge is created, shared, and assessed. While these technologies offer new opportunities for teaching and learning, they also introduce significant challenges, particularly in maintaining academic integrity. This article contends that the traditional focus on preventing plagiarism and misconduct is insufficient in the context of generative AI. Instead, it advocates for a renewed culture of academic integrity that incorporates comprehensive AI ethics training across the academic community. This approach goes beyond simplistic prohibitions to address the broader ethical, social, technical, and normative issues that underpin academic integrity in the use of AI tools. By fostering awareness and competence in AI ethics, it seeks to equip students, educators, and administrators with the skills to critically engage with AI technologies, promote fairness, transparency, and accountability, and ensure responsible and ethical use. The article ultimately supports a pragmatist approach to AI ethics, emphasising reflective, contextual, and autonomous decision-making as essential to navigating the complexities of AI in higher education.
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 | 0.058 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.013 |
| 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".