Faculty Adoption of Generative Artificial Intelligence in a Canadian Higher Education Institution
Bibliographic record
Abstract
The landscape of higher education (HE) continues to change rapidly with the incorporation of new artificial intelligence (AI) applications like generative artificial intelligence (genAI). These transformations can be attributed to the ubiquity, efficacy, and quality of genAI applications. GenAI will necessitate the need for HE instructors to adapt and use these technologies to sustain and enhance student learning. This paper reports quantitative findings influencing instructors’ intentions to adopt genAI into their pedagogies. The Artificial Intelligence Acceptance Measurement Survey (AIAMS) was developed and adapted from the revised Technology Acceptance Model Survey-2 (TAMS-2) that incorporates the main constructs from the Theory of Planned Behavior (TPB). The survey was administered to a sample of instructors from different programs working in a large Canadian urban polytechnic institution (n=87). Multiple regression analysis was conducted to identify the main determinants influencing instructors’ intention to adopt genAI in their teaching. Statistical findings reveal that instructors’ attitudes toward genAI were the only significant factor influencing their intent to adopt it in their teaching practices. It is crucial for those in HE to understand the factors that influence instructors’ intentions to integrate genAI into their teaching practices to support and realize its successful adoption. This understanding is also key for leveraging the full potential and capabilities of genAI to enhance educational outcomes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".