Unveiling the Drivers of AI Integration Among Language Teachers: Integrating UTAUT and AI-TPACK

Artificial intelligence (AI) offers numerous benefits to the field of language education, making it crucial to understand the factors influencing language teachers' adoption of these technologies. This study investigates the determinants of language teachers' adoption of AI chatbots in educational settings. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technological Pedagogical Content Knowledge (TPACK) framework, a comprehensive model of AI adoption among language teachers is proposed and tested. Data were collected from 276 language teachers in Vietnam through an online survey. Partial Least Square-Structural Equation Modeling (PLS-SEM) was employed to analyze the data. Results indicate that AI adoption intent significantly predicts AI integration, while performance expectancy, effort expectancy, and AI self-efficacy are key determinants of AI adoption intent. AI-TPACK emerges as a crucial factor, strongly influencing AI self-efficacy, performance expectancy, and effort expectancy. Facilitation is found to be a significant predictor of AI-TPACK. These findings enhance the theoretical framework of AI adoption in language education and provide valuable insights for fostering effective AI integration among language teachers.