Moral Diversity in Institutional Policies Governing the Student Usage of Generative <scp>AI</scp> : An International Comparison
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT This paper explores from an ethical standpoint how higher education institutions in three different countries (Canada, UK, and USA) have framed their policies containing guidelines in regard to the student usage of generative artificial intelligence (GenAI). An inductive thematic analysis of the online GenAI policy sources of 36 universities has revealed the existence of different national patterns with regard to two major moral aspects. Firstly, institutions follow different moral frames to justify their critical or accepting stance towards GenAI, specifically conceptualised in this paper as: (a) moral consistency and (b) responsible futureproofing. Secondly, institutions assign different levels of moral authority to faculty as ultimate decision‐makers, identified in the paper as (a) absolute, (b) restrained, and (c) hybrid. Through an international comparative discussion of these findings, the paper wishes to inform current and future policy (re)formations on the topic, as institutional work in the area is currently rapidly unfolding.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it