Artificial intelligence in the context of teacher education: emerging themes and critical issues
Bibliographic record
Abstract
The integration of Artificial Intelligence (AI) into teacher education (TE) is said to change, or at least challenge, traditional pedagogical approaches and ways of preparing teachers to teach in an increasingly technological world. This paper identifies emerging themes and critical issues arising from a cross-analysis of the papers in this special issue. It offers insights that should be considered by both teacher educators and TE institutions. The following themes are discussed: AI tools and technologies in TE: Use cases, affordances and constraints; AI literacy in TE; (Teacher) educators’ identities; Pedagogical transformation and innovation through AI; and Ethical and political issues of AI in TE. Moving beyond adoption with optimism or scepticism, this paper highlights what AI enables and limits. We conclude by reflecting on how AI in the context of TE might be used without losing sight of ethical considerations, human interaction, and the broader social, pedagogical, and political aspects.
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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 | 0.045 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.016 | 0.053 |
| Scholarly communication | 0.042 | 0.030 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.020 |
| 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".