Amal Mohammad Husein Alrishan
Department of Education, College of Arts and Humanities, A’Sharqiyah University, Ibra 400

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Predicting EFL Students’ Use of Artificial Intelligence Tool in Advancing Their Writing Skills Amal Mohammad Husein Alrishan
Emerging Science Journal Vol. 9 (2025): Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-SIED1-018

Abstract

This study examines the factors influencing the adoption and use of artificial intelligence (AI) tools to enhance writing skills among English as a Foreign Language (EFL) learners in Oman, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT). The objectives were to assess the impact of performance expectancy, effort expectancy, social influence, and facilitating conditions on students’ behavioral intention and actual AI usage, and to test the moderating role of prior AI experience. A cross-sectional quantitative design was employed, with data collected from 255 undergraduate female EFL students through a validated questionnaire. Structural equation modeling (SEM) and confirmatory factor analysis were used to validate the measurement model and test hypothesized relationships. Findings indicate that behavioral intention and facilitating conditions significantly predicted actual AI tool use, while performance expectancy, effort expectancy, and social influence strongly shaped behavioral intention. Mediation tests confirmed that behavioral intention served as a key pathway linking UTAUT constructs to actual adoption, and moderation analysis showed that prior AI experience strengthened the intention–usage relationship. This research contributes to a context-specific, evidence-based framework for AI adoption in EFL writing, offering novel insights for educators, institutions, and technology designers to integrate AI ethically and effectively in language learning.
Modelling Pre-Service English Teachers' Readiness for AI Integration: A TPACK–TAM Mixed-Methods Study Amal Mohammad Husein Alrishan
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-020

Abstract

Artificial Intelligence (AI), particularly large language models such as ChatGPT, has advanced rapidly recently, revolutionizing English Language Teaching (ELT); nonetheless, its pedagogically meaningful integration remains uneven and contingent on teacher preparation. Emerging research indicates that AI adoption is shaped more by teachers’ professional knowledge and acceptance views than by technological hurdles. However, empirical information on their interaction, particularly in underexplored contexts, remains scarce. Using an integrated Technological Pedagogical Content Knowledge (TPACK) and Technology Acceptance Model (TAM) framework, this study investigates pre-service English teachers' preparedness for AI integration, conceptualizing readiness as competence-informed acceptance, a novel construct that differs from traditional readiness frameworks by emphasizing the cognitive professional interplay between knowledge and beliefs rather than mere willingness or attitude. An explanatory sequential mixed-methods single-case design was utilized, with survey data (n = 78) analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and qualitative responses examined through reflexive thematic analysis. The results demonstrated that pedagogical knowledge was the strongest predictor of reported usefulness (β = 0.607, p < 0.001) and perceived ease of use (β = 0.546, p < 0.001). Prior AI experience directly predicted intention (β = 0.208, p < 0.001) and moderated the usefulness–intention link (β = 0.061, p = .044), although perceived ease of use had a greater impact on planned future use (β = 0.299, p < 0.001) than perceived usefulness (β = 0.192, p = 0.003). The qualitative results identified the importance of pedagogical rationale and context limitations. The research contributes to the theory, as it combines TPACK and TAM and offers context-related evidence in the MENA region, which supports the preparation of AI in ELT with pedagogy as a priority. Qualitative findings highlighted the role of pedagogical reasoning and contextual constraints. The study advances theory by integrating TPACK and TAM, demonstrating that professional knowledge operates indirectly through acceptance beliefs, and provides context-sensitive evidence from the Middle East and North Africa (MENA) region, supporting pedagogy-first AI preparation in ELT.