Harnessing Artificial Intelligence and Its Impact on Pre-Service Teacher’s Technological, Pedagogical, and Content Knowledge Skills: A Meta-Analysis
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
As education shifts into the digital age, technology, especially artificial intelligence (AI), plays an increasingly vital role in improving teaching and learning. This meta-analysis investigates how AI impacts the development of Technological Pedagogical and Content Knowledge (TPACK) skills among pre-service teachers. By reviewing data from various studies published between 2010 and 2023, we explore how AI can help integrate technology into educational settings. Our findings reveal that AI interventions generally positively affect TPACK skills, although the extent of these effects varies significantly across studies. Factors such as sample size, duration of the intervention, and the quality of AI tools used all contribute to these differences. Importantly, larger sample sizes and well-designed AI applications lead to more significant improvements in TPACK skills. However, challenges still exist, particularly the need for adequate training and institutional support for pre-service teachers. This research highlights the necessity of establishing the best practices for incorporating AI into teacher training programs. By addressing the gaps in current literature, this study offers valuable insights into the effective use of AI in enhancing TPACK skills and advocates for further investigation in this critical area of educational technology.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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