Opportunities, challenges and school strategies for integrating generative AI in education
Bibliographic record
Abstract
The increasing accessibility of Generative Artificial Intelligence (GenAI) tools has led to their exploration and adoption in education. This qualitative study investigates the opportunities and challenges associated with integrating GenAI in education, and the strategies that encourage teachers and students to embrace GenAI in school settings. We recruited 76 educators in Canada to participate in a professional training seminar about GenAI and expressed their views through online surveys. Through written reflections, an optimistic outlook on GenAI's role in education was identified among the teachers, and some discipline-specific ideas were proposed. Thematic analysis reveals three key practices of AI implementation: teaching/learning, administration and assessments. However, three major challenges are also identified: school's readiness, teachers' AI competencies, and students' AI literacy and ethics. Teachers suggest several strategies to motivate GenAI integration, including professional development, clear guidelines, and access to AI software and technical support. Finally, Singh's Teach AI Global Initiative Guidance and Socio-ecological Model are adapted and proposed to support schools in becoming AI-ready by addressing teachers' and students' needs, facilitating organizational learning, and promoting improvement and transformation to foster their literacy development. Recommendations were provided for developing effective strategies to embrace GenAI in 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 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".