Pedagogical and Ethical Implications of Artificial Intelligence in EFL Context: A Review Study
Bibliographic record
Abstract
In the contemporary world, technology is advancing and being integrated in various sectors, impacting human lives in many ways. When the conversation on technological advancements emerges, one of the most prevalent topics is artificial intelligence (AI). AI has gradually developed into an integral part of human lives, with its application being common in finance, healthcare, security, and education. In education, AI can be integrated into English as a Foreign Language (EFL), leading to the introduction of a dynamic realm with profound ethical and pedagogical dimensions. The current study focuses on the interaction between EFL education and AI technologies by analyzing the obstacles and opportunities that might emerge. Pedagogically, AI has multiple advantages to any EFL education setting, which include targeted feedback, automated grading, and personalization of learning experiences, especially for learners with disabilities. However, the use of AI leads to some concerns, including the exclusion of the chance for learning to engage in creative and critical thinking. It is also associated with the possible dehumanization of the learning process and biases that might result from the use of AI software. Also, using AI in an EFL setting raises various ethical concerns, including personal data privacy, academic dishonesty, and a decline in job security for teachers. When teachers do not feel that their jobs are safe, their motivation is likely to decline. Also, using AI in an EFL setting raises concerns such as the loss of cultural nuances and an unhealthy reliance on technology. Thus, there needs to be a balance whenever AI is used in an EFL education setting for the sake of protecting educational objectives and adhering to the ethical standards expected of such settings. This paper highlights the use and impact of AI on EFL pedagogically and the risks and ethical concerns associated with such adoptions. The study is based on the understanding that the multiple benefits associated with the use of AI in education come with challenges that necessitate a balanced approach to implementation.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".