Drivers of AI Tool Adoption in Multilingual English Classrooms: A TAM-Based Structural Equation Model

Abstract

This study examines the drivers of unsolicited artificial intelligence use and the factors shaping its adoption in multilingual English classrooms without institutional direction. Grounded in the Technology Acceptance Model, it addresses a gap in existing research that has focused on policy driven AI integration while overlooking independent use by teachers and students and its implications for equity, academic integrity, and pedagogical alignment. Using a quantitative design, survey data from 321 participants were analyzed through structural equation modeling with perceived ease of use, perceived usefulness, attitude, behavioral intention, and actual usage. Findings show that students exhibit stronger links between perceived ease of use and usefulness, whereas teachers emphasize the relationship between attitude and actual usage. Model fit indices indicate a reasonable explanatory fit for AI adoption behavior. The study recommends developing AI competency frameworks, ensuring equitable access, and adopting context sensitive guidelines for ethical and effective AI use in English education.