نجمه مقصودی

The growing integration of artificial intelligence (AI) into English language education has created new opportunities to enhance reading instruction. This study investigated the effect of AI-driven reading instruction on EFL learners’ reading autonomy and explored learners’ perceptions of AI-driven reading environments. A sequential explanatory mixed-methods design including a quasi-experimental pretest–posttest nonequivalent control-group design followed by the qualitative phase using extreme (polar) case sampling and semi-structured interviews, was employed during a five-week intervention. Fifty-four intermediate EFL learners from a Language school in Kerman, Iran were selected through Oxford Placement Test and were randomly assigned to an experimental group (n = 27), which received AI-driven reading instruction through ReadTheory, and a control group (n = 27), which received traditional reading instruction. Quantitative data were collected using pre- and post-tests of reading autonomy questionnaire, while qualitative data were obtained through semi-structured interviews. The quantitative data were analyzed using Analysis of Covariance (ANCOVA) after checking the required assumptions, and the interview data were analyzed through inductive thematic analysis. The findings revealed that AI-driven instruction significantly enhanced learners’ overall reading autonomy as well as its cognitive, metacognitive, affective-motivational, and action-oriented dimensions. However, no statistically significant improvement was found in the social dimension of reading autonomy. Furthermore, qualitative findings indicated that learners perceived ReadTheory as a tutor-like support system, providing personalized and adaptive learning for them. The study concludes that AI-driven platforms can serve as a valuable pedagogical tool for enhancing learners’ reading autonomy when integrated with appropriate instructional support.

A critical characteristic of effective teachers is their ability to manage unforeseen and critical incidents (CIs) defined as disruptive events that undermine teachers’ resilience. While existing research has examined CIs across various educational contexts, their potential for the professional development (PD) of English for Academic Purposes (EAP) teachers has remained under explored. This qualitative study sought to explore the types of CIs encountered by Iranian EAP teachers and to assess how reflection on such incidents contributes to their PD. Twelve EAP teachers participated in this study based on convenience sampling. Data were collected through in-depth, semi-structured interviews conducted individually at an English institute in Kerman, Iran, and the results went through inductive thematic analysis and inter-coder reliability which identified three primary themes of CIs: teacher-related, learner-related, and institution-related. Additionally, the findings indicated that reflection on CIs fostered EAP teachers’ cognitive, affective, and social dimensions of PD. The study implies the necessity of targeted PD initiatives through reflection on CIs in the context of Iran. While complete mitigation of CIs may seem unlikely, reflective practice can lead to positive outcomes for all parties including EAP teachers, students, and administrators.

English for Specific Purposes (ESP) education has gained prominence as a specialized field designed to address the distinct linguistic needs of learners. Although AI-enhanced instruction has been investigated within general English language learning, its implementation in ESP contexts particularly concerning vocabulary recall and retention has remained insufficiently explored. This study sought to evaluate the impact of AI-supported instruction on vocabulary recall and retention among Iranian ESP learners. Based on non-random convenience sampling, a total of 48 undergraduate students in an ESP course from a university in a southeastern province in Iran participated in the present quasi-experimental study. The participants were non-randomly assigned into two different groups: an experimental group (n=24) receiving AI-powered vocabulary instruction through Diffit, and a control group (n=24) receiving traditional print-based vocabulary instruction. Data were collected during a twelve-week intervention through AI-powered instruction of ESP vocabulary. To address the research questions, a univariate repeated measures analysis of covariate (ANCOVA) was employed. Findings revealed that AI-enhanced instruction significantly improved vocabulary recall and retention among ESP learners. The results offer robust evidence supporting the efficacy of AI-based instructional approaches in enhancing vocabulary learning outcomes within ESP settings.