Keep the Ball Rolling in AI-Assisted Language Teaching: Illuminating the Links Between Productive Immunity, Work Passion, Job Satisfaction, Occupational Success, and Psychological Well-Being Among EFL Teachers
Bibliographic record
Abstract
Artificial intelligence (AI) revolutionizes education by fundamentally altering the methods of teaching and processes of learning. Given such circumstances, it is essential to take into account the mental and psychological well-being of teachers as the architects of education. This research investigated the links between teacher immunity (TI), work passion (WP), job satisfaction (JS), occupational well-being (OW-B) and psychological well-being (PW-B) in the context of AI-assisted language learning. In order to achieve this objective, 392 Iranian teachers of English as a foreign language (EFL) were given the Language Teacher Immunity Instrument, the Work Passion Scale, the Job Satisfaction Questionnaire, the Occupational Well-Being Scale, and the Psychological Well-Being at Work Scale. By using confirmatory factor analysis and structural equation modeling, the study identified and quantified the impacts of TI, WP, JS, OW-B, and PW-B via data screening. The findings emphasize the crucial role that TI and WP play in providing a balance in teachers’ JS, OW-B, and PW-B while applying AI in their language instruction. The broad ramifications of this research are explored.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".