Bibliographic record
Abstract
This paper examines the ethical implications of Generative AI (GenAI), such as ChatGPT, in higher education, focusing on how these technologies can be harnessed to enhance student learning while upholding academic integrity. It begins by addressing traditional ethical principles in education, including fairness, transparency, accountability, privacy, and equality, and emphasizes the need for a shift from academic integrity to professional ethical competence (PEC) in response to the challenges posed by AI. The paper then explores the specific ethical concerns associated with GenAI, including algorithmic bias, transparency, data privacy, and the potential for misuse in academic assessments. A tiered framework for policymaking and implementation is proposed, structured at the university, faculty, and course levels, to ensure ethical GenAI use while promoting flexibility and stakeholder adoption. The paper concludes with a set of technical recommendations for AI developers, focusing on embedding ethical design, enhancing transparency through summaries and explanatory output, conducting bias audits, and prioritizing data privacy and security. By fostering collaboration among all stakeholders— universities, developers, educators, and students—the paper advocates for a thoughtful and responsible approach to integrating GenAI in education that supports both innovation and ethical responsibility.
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 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 | 0.053 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.081 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.010 | 0.021 |
| 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".