Bibliographic record
Abstract
Since its launch in November 2022, ChatGPT-3 has generated substantial discussion on its potential impact on various domains, including higher education. Canadian universities have responded to the growing use of ChatGPT by updating their policies and guidelines. However, these policies are dominantly geared toward the general university communities and sparse discussions are focusing on ChatGPT-3 policy making in relation to international students in Anglophone universities. This chapter draws on findings from document analysis of ChatGPT policies and guidelines as published on Ontarian universities and identifies critical issues prioritized by the institutions in terms of academic integrity challenges, authorship and data privacy, digital equity and inclusion, and educational values and innovations. The results reveal the institutions’ contrasted attitudes toward ChatGPT usage in the educational process inspired by creativity and innovations and in the educational outcome measured by assessments. Informed by deep learning and plurilingualism theories, we argue that international students’ language needs remain as a “blind spot” in current university policies and universities must recognize and support diverse language needs to foster a more inclusive learning environment. We further discuss strategies to navigate these challenges and opportunities and accommodate international students’ language needs in an AI-infused plurilingual classroom and call for a re-imaging of HE not only inspired by plurilingualism but also enriched with human–technology intra-actions.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".