Adapting the AI Ecological Education Policy Framework to the Canadian Context
Bibliographic record
Abstract
The rapid emergence of generative artificial intelligence (hereafter referred to as AI) tools such as ChatGPT, Claude, and Gemini is fundamentally reshaping higher education, necessitating the development of comprehensive, equity-centred institutional policy frameworks. This study focuses on the quantitative and qualitative survey results from a larger mixed-methods study of faculty at a Canadian university, evaluating the applicability of AI Ecological Education Policy Framework, comprising Pedagogical, Governance, and Operational dimensions, to the Canadian context. The study reveals that while faculty recognize the potential of AI to enhance digital literacy and learning, significant concerns persist regarding academic integrity, equity, ethical governance, and the emotional labour associated with AI integration. Faculty responses highlight the need for policies that are not only adaptive and transparent but also attentive to relational and affective dynamics, such as trust, emotional well-being, and the preservation of authentic pedagogical relationships. Based on these findings, the paper proposes an expanded four-dimensional policy development framework that adds a Relational and Affective dimension, emphasizing the importance of emotional support, participatory governance, and affect-sensitive design.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".