Factors affecting Indonesian pre-service EFL teachers’ AI acceptance and use

Abstract

Integrating artificial intelligence in language education, particularly for pre-service English as a Foreign Language (EFL) teachers, presents unique challenges and opportunities. This research seeks to extend the technology acceptance model (TAM) by integrating technological pedagogical and content knowledge (TPACK) to predict behavioral intentions and actual use of AI technologies in an EFL context. Employing partial least squares structural equation modeling, the sample consisted of 436 pre-service EFL teachers. The findings showed that perceived ease of use impacts perceived usefulness (β=0.674) and attitudes (β=0.387). Perceived usefulness affects attitudes (β=0.452) and AI-behavioral intention (β=0.216). The attitudes variable influences AI-behavioral intention (β=0.206). Technological content and technological pedagogical knowledge contribute to TPACK (β=0.278, β=0.311). TPACK impacts AI-behavioral intention (β=0.350) and AI-use (β=0.557). By extending the TAM with TPACK, this study offers insights into optimizing AI adoption among future language educators, thereby fostering innovative teaching practices that enhance language learning experiences for students. The current study covers two areas of Sustainable Development Goals (SDGs): Higher education quality in the EFL area (SDG 4-Quality Education) and digital transformation in education (SDG 17 – Partnerships for the Goals).