Enhancing Novice EFL Teachers' Competency in AI-Powered Tools Through a TPACK-Based Professional Development Program
Bibliographic record
Abstract
As artificial intelligence (AI) continues to advance rapidly, its application in educational settings is increasingly expanding. However, a substantial gap persists in the number of novice English as a Foreign Language (EFL) teachers who are well-prepared to integrate technology in learning activities. This research created a professional development (PD) program grounded in the technological pedagogical content knowledge (TPACK) framework to tackle this issue and enhance the AI-related teaching skills of novice EFL teachers. The study employed a quasi-experimental design, with 20 participants in the experimental group and 20 in the control group, to assess the impact of the PD program on various aspects of AI teaching competence, including AI-powered tools knowledge test, teaching skills related to AI-powered tools, and AI-powered tools teaching self-efficacy. The research utilized several instruments, such as AI-powered tools self-efficacy scale, a rubric for evaluating AI-powered tools lesson plans, an AI-powered tools knowledge test, and semi-structured interviews. The findings demonstrated that the TPACK-based PD program a) enhanced the AI-powered tools knowledge of novice EFL teachers, b) improved their ability to integrate AI-powered tools into their teaching practices, and c) boosted their self-efficacy in teaching with AI-powered tools. These results underscore the impact of this program for bolstering novice EFL teachers' proficiency in using AI-powered tools and provide valuable insights for the development of effective PD programs for EFL educators.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".