Secondary school teachers’ perspectives on GenAI proliferation: generating advanced insights
Bibliographic record
Abstract
The proliferation of generative artificial intelligence (GenAI) technologies has significantly impacted the educational sector, prompting a re-evaluation of teaching, learning, and assessment practices. This study explores the perceptions of Ontario secondary school teachers regarding the challenges and opportunities presented by GenAI. Using a qualitative research method, 17 high school teachers were interviewed to understand their views on GenAI integration and its implications for academic integrity. The findings reveal three critical areas for integrating GenAI in education: generating people through professional development and ethical training for educators, generating programs by designing transparent and purpose-driven initiatives, and generating policies through the creation of clear, adaptable governance frameworks. Together, these pillars highlight the collaborative work needed to harness GenAI’s potential while ensuring ethical and equitable practices in secondary education. These themes are a subset of invitational education and highlight the need for comprehensive training for teachers, the development of transparent guidelines and ethical practices, and the establishment of robust policies to support the integration of GenAI in education. The study emphasizes the importance of collaboration among educators, administrators, and other stakeholders to effectively navigate the evolving landscape of GenAI-driven educational environments effectively. By addressing these pillars, academic institutions can harness the transformative potential of GenAI while maintaining the integrity and quality of education. This research provides valuable insights into the evolving role of teachers and the necessity for strategic planning, professional development, and policy frameworks to optimize the benefits of GenAI in secondary education.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".