Artificial Intelligence Integration in Teacher Education: Navigating Benefits, Challenges, and Transformative Pedagogy
Bibliographic record
Abstract
This article explores the potential uses, benefits, and challenges of artificial intelligence (AI) tools for teacher educators and their teacher candidates. It begins with a brief introduction to the topic, followed by a discussion of existing literature concerning the impact of AI on K–12 education; the importance of preparing AI-literate teachers; and specific issues related to AI’s use in teacher education, which includes studies on perceptions about AI utilization in education. The author also examines the challenges of AI implementation in the preparation of teachers; poses critical questions and ethical, pedagogical, and philosophical concerns faculty and students must consider; and provides ideas on using AI tools in courses through exemplar activities. The various activities illustrate the integration of AI- and non-AI-based activities to promote AI literacy among teachers. The goal of these activities is to support candidates’ competency development to become effective teachers while understanding the potential of AI tools for assisting in planning instruction and creating curricula. The exemplars also focus on developing critical thinking, creativity, and collaboration in carrying out pedagogical tasks. The author advocates for embracing AI tools in education cautiously and strategically by recognizing their power to transform teaching and learning for teachers and students.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".