Harnessing Artificial Intelligence and Its Impact on Pre-Service Teacher’s Technological, Pedagogical, and Content Knowledge Skills: A Meta-Analysis
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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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 | 0.031 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".