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

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

Advancing learning-oriented assessment (LOA): mapping the role of self-assessment, academic resilience, academic motivation in students’ test-taking skills, and test anxiety management in Telegram-assisted-language learning

Abstract Some impediments in language learning may have a detrimental impact on learners’ actual performance on the test and lead to anxiety and demotivation. Language achievement is influenced by self-assessment (SA), academic resilience (AR), academic motivation (AM), and test-taking skills (T-TS) among other factors. Considering the relevance of these factors in language achievement, the current investigation aims to delve into the probable interactions of SA, AR, AM, T-TS, and test anxiety (TA) management among English as a foreign language (EFL) learners. A model was devised and evaluated using confirmatory factor analysis (CFA) and structural equation modeling (SEM) to achieve this objective. This research collected 512 by distributing online questionnaires to fifteen approved private institutions which applied Telegram-based language learning. The study findings reflected that SA, AR, and AM could predict EFL learners’ T-TS. It was also confirmed that SA, AR, and AM modulated EFL learners’ TA. The implications of the study are presented and accompanied by some future research proposals as well as instructional consequences

Artificial Intelligence for Arabic Lessons Will IT Helps Teachers?

This study aims to determine whether artificial intelligence for learning Arabic can help teachers. The method used by the researcher is a survey method from previous research, which also discusses artificial intelligence. Artificial intelligence can help teachers, especially in Arabic subjects. However, although it can help teachers learn Arabic, it still cannot replace the role of a teacher who teaches directly or face to face. Because Arabic is one of the subjects that teach all or everything related to language, experts are needed to teach it. So with the existence of artificial intelligence, teachers can be helped but not even though they are not optimal. No matter how well the program is designed, it cannot replace the teacher's position as an expert in science. The nature of the computer program is only as a tool, not as a transmitter of knowledge as a whole like a teacher. Technological advances are beneficial or can warn the teacher's work in certain areas. It is hoped that future researchers can use this research as reference material with this research