Artificial Intelligence Integration in Teacher Education: Navigating Benefits, Challenges, and Transformative Pedagogy
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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