Predicting EFL Students’ Use of Artificial Intelligence Tool in Advancing Their Writing Skills - Amal Mohammad Husein Alrishan (Emerging Science Journal, 2025)
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.