Bibliographic record
Abstract
Amidst the persistent and increasingly visible discrimination and injustice against racially minoritized groups during and after the global COVID-19 pandemic, Canadian higher education has stressed the incorporation of equity, diversity, and inclusion (EDI) into curricular planning and practice. In parallel to the EDI emphasis at the institutional level, of importance is an urgent understanding of the role of artificial intelligence (AI), specifically relating to its impact and possibilities for meaningful academic engagement and advancement of critical scholarship in academia. In light of this, the paper discusses the pedagogical challenges and possibilities of creating EDI-foregrounded English for academic purposes (EAP) teaching in this evolving context of AI. Drawing on the concepts of ‘super-diversity’ and transnational belonging as characteristics of global citizens , as well as transformative multiliteracies pedagogy, the article introduces multimodal and multiliteracies-engaging instructional design to EAP teaching navigated through action research and demonstrates how the newly developed instructional design has corresponded to the reality of EDI and the emergence of AI in Canadian higher education. This study underscores the necessity for a more nuanced understanding of EDI within the localized context of EAP teaching situated in a large cosmopolitan city in Canada.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".