A Movement towards Language Learning by Artificial Intelligence: A PLS-SEM Approach to Instrument Development and Validation

This study aimed to design and validate a comprehensive questionnaire to assess the impact of AI on language learning, focusing on key factors such as personalization, adaptive feedback, AI dependence, perceived usefulness, perceived ease of use, teacher enthusiasm, and language comprehension. A 27-item survey was initially developed based on the Technology Acceptance Model and elements of smart learning environments. Through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), the instrument was refined to 17 items across seven components. The study surveyed 150 Iraqi students, revealing that personalization, adaptive feedback, and teacher enthusiasm significantly enhance AI-assisted language learning outcomes. Additionally, gamified features and bias-free access to AI technologies were found to improve engagement and equity. Structural equation modeling confirmed the reliability and validity of the proposed model, emphasizing AI’s effectiveness in addressing linguistic and idiomatic challenges.