Applying AI For English Language Instruction and Material Development in Schools: A PLS-SEM Approach

Abstract

Despite growing recognition of the value of artificial intelligence (AI) in English as a Foreign Language (EFL) instruction, adoption at the school level remains limited due to a lack of understanding about the complex factors influencing teachers’ post-training acceptance. This study examined the interrelationships among subjective norms, technologist roles, student influence, process facilitation, compatibility, perceived attitudes, and behavioral intentions in Indonesian senior high school EFL teachers following a professional development workshop on AI integration. Using validated survey instruments, data from 146 teachers were analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance-Performance Matrix Analysis (IPMA). Quantitative results showed that subjective norms significantly affected process facilitation and compatibility, while student influence strongly predicted technologist roles, compatibility, and process facilitation. Technologist roles and compatibility were pivotal in shaping positive attitudes and intentions to adopt AI. IPMA identified compatibility as a key area for targeted improvement. The findings stress the need for ongoing, context-sensitive professional development to promote effective and sustainable AI integration in EFL teaching.

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