The Role of Artificial Intelligence in Supporting Canadian Plurilingual Graduate Students
Bibliographic record
Abstract
The evolution of Artificial Intelligence (AI) in education offers new possibilities for supporting plurilingual students within Canada's varied linguistic landscape, where learners balance official and community languages while developing academic proficiency and negotiating identities. AI tools, such as adaptive learning platforms, natural language processing technologies, and real-time translation software, provide inclusive, personalized environments. Grounded in plurilingualism and inclusive education, this chapter outlines AI's potential to support language acquisition, foster cultural inclusivity, and promote identity negotiation. It also explores systemic barriers, presenting case studies from Canadian classrooms that illustrate AI's impact on student engagement and learning. Although AI offers innovative opportunities, concerns regarding equitable access, algorithmic bias, and data privacy must be addressed. Strategies for ethical application are proposed, along with context-sensitive plurilingual practices to foster metalinguistic awareness and equitable learning spaces.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".