Artificial Intelligence and English as a Foreign Language (EFL) Teachers’ Competencies: A Systematic Review
Bibliographic record
Abstract
The integration of Artificial Intelligence (AI) into English as a Foreign Language (EFL) education has transformed teaching practices, necessitating a re-evaluation of teacher competencies in the digital age. This systematic review examines the intersection of AI technologies and EFL teachers' competencies, exploring how AI tools influence pedagogical skills, technological proficiency, and professional development. Through an analysis of recent literature, the study identifies key competencies required for EFL teachers to effectively leverage AI, including adaptive teaching strategies, data literacy, and ethical considerations in AI usage. The review also highlights challenges such as resistance to technological adoption, the digital divide, and the need for continuous upskilling. Findings suggest that while AI offers significant opportunities for personalized learning and efficiency, EFL teachers must develop a balanced skill set that integrates traditional teaching expertise with emerging technological demands. The results of the research showed that 1) EFL teachers’ competencies in artificial intelligence field consist of 10 competencies, namely: 1) AI-Assisted Lesson Planning, 2) AI-Powered Language Practice & Feedback, 3) Speech Recognition & Pronunciation Tools, 4) AI for Differentiated Instruction, 5) Automated Assessment & Grading, 6) Data-Driven Student Insights, 7) Ethical & Critical Use of AI, 8) AI for Content Creation & Gamification, 9) AI-Powered Translation & Comprehension Support, and 10) AI and Virtual/Augmented Reality in EFL. The paper concludes with recommendations for teacher training programs and policy frameworks to support the evolving role of EFL educators in an AI-driven educational landscape.
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.
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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".