Bibliographic record
Abstract
This chapter examines how Higher Education Institutions (HEIs) are developing governance frameworks to address ethical and responsible deployment of Artificial Intelligence (AI), particularly Generative AI (GenAI). Through a qualitative study of institutional documents, departmental guidelines, and course-level policies at the University of Lethbridge, it identifies key themes such as academic integrity, transparency, and the balancing of ethical considerations with operational efficiency. The establishment and objectives of an AI Policy Working Group (AIPWG) are detailed, highlighting stakeholder collaboration to develop adaptive policies responsive to rapidly advancing AI technologies. Main findings indicate variability in AI policy across academic disciplines, creating potential confusion regarding ethical AI use among students and faculty. The chapter proposes a multitiered governance framework incorporating institutional guidelines aligned with Responsible Management Education (RME) principles—emphasizing ethics, accountability, and sustainability—alongside discipline-specific and course-level policies. It concludes by advocating for international cooperation and continuous stakeholder engagement to refine AI governance, ensuring responsible and ethical integration that aligns with educational values and promotes critical thinking.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".