Artificial intelligence in the context of teacher education: emerging themes and critical issues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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 | 0.001 | 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 it