Generative or Degenerative?! Implications of AI Tools in Pre-Service Teacher Education and Reflections on Instructors’ Professional Development
Bibliographic record
Abstract
Despite existing research on AI applications in education (AIEd), the release of ChatGPT has disrupted the status quo in the educational landscape. Although this technology can personalize learning, decrease teacher workload, and offer access to a wealth of information, concerns around generative AI (GenAI) tools have emerged, including academic integrity, data accuracy, and bias in information. Given research highlights and acknowledging educators’ varied levels of awareness and conflicting views toward AIEd, two teacher educators (also authors of this paper) in the Faculty of Education at Brock University facilitated three workshops among different groups of teacher educators. The workshops focused on the emerging nature of GenAI tools, their affordances, and their implications for educators’ practices. Adopting a narrative inquiry approach, the authors describe the details of these workshops and present their reflections on the process of preparing for and facilitating them. Implications for teacher education research and practice are also presented and discussed.Keywords: artificial intelligence (AI), artificial intelligence in education (AIEd), teacher education, professional development, generative AI (GenAI)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".