Rise of Generative Artificial Intelligence: Insights from Secondary School Leaders
Bibliographic record
Abstract
With the rise of generative artificial intelligence (GenAI), there has been limited research on this technology in the context of Ontario secondary school leadership. This study aims to fill this gap by utilizing a basic qualitative research design, where twenty secondary school leaders from various locations in Ontario engaged in semi-structured interviews. The data were analysed utilizing reflexive thematic analysis and e-leadership, pedagogical beliefs, and educational change as the conceptual framework. The GenAI technologies used and approved across Ontario secondary schools and parental and community perspectives were reported by school leaders. Overall, five themes emerged in which Ontario secondary school leaders expressed a range of perspectives—both for and against the integration of GenAI—grounding their views in their professional responsibilities and the needs of their students. School leaders indicated several opportunities and challenges of integrating this technology, including varying equity perspectives and different supports in their schools and the system. School leaders propose several recommendations regarding GenAI in Ontario secondary schools. Implications of this exploratory study are discussed.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".