Towards Adaptive Resilience: Generative AI Integration in English Departments in Canada and Beyond
Bibliographic record
Abstract
This study examines how English departments can navigate the integration of generative AI technologies while preserving their core educational mission of developing authentic student voices and critical thinking. Through intentional case analysis of departmental characteristics and policy frameworks, we identify fundamental tensions between Maton’s knower code dynamics that define humanities education and the horizontal knowledge structures necessary for technological adaptation. Drawing on Bernstein’s discourse theory and contemporary educational research, we argue that current responses to AI, ranging from uncritical embrace to rigid resistance, fail to address the deeper identity challenges these technologies pose for humanities disciplines. Our interpretive policy analysis reveals that English departments, seen as exemplars for humanities departments world-wide, exist as complex institutional environments where preconditions for both AI resistance and acceptance coexist simultaneously. The study maps the acceptance of AI technology on a continuum from AI resistance to AI acceptance. The study introduces adaptive resilience as a framework that honors authentic voices while accommodating pedagogical diversity required for technological adaptation. The findings of our case study and policy analysis suggest that successful AI integration requires moving beyond polarized positions toward relational stances that embrace both traditional expertise based and contemporary multidisciplinary approaches that preserve our commitment to both culturally responsive critical analysis while adapting to evolving technological contexts.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.013 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".