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

Rangga Firdaus; Akhmad Habibi; Robi Hendra; Mohd Sofian Omar  Fauzee; Sheren Dwi Oktaria; Muhammad Sofwan; Turki Mesfer Alqahtani

doi:10.1590/1983-3652.2025.57135

Autores/as

Rangga Firdaus Universitas Lampung, Program Studi Magister Teknologi Pendidikan, FKIP, Indonesia https://orcid.org/0000-0003-4139-9946
Akhmad Habibi Korea University, Graduate School of Education, Seoul, Republic of Korea / Universitas Jambi, Fakultas Keguruan dan Ilmu Pendidikan, Jambi, Indonesia https://orcid.org/0000-0001-7687-2858
Robi Hendra Universitas Jambi, Fakultas Keguruan dan Ilmu Pendidikan, Jambi, Indonesia https://orcid.org/0000-0002-2471-3107
Mohd Sofian Omar Fauzee INTI International University, Faculty of Education and Liberal Arts, Nilai, Malaysia https://orcid.org/0000-0002-6841-9647
Sheren Dwi Oktaria Universitas Lampung, Program Studi Magister Teknologi Pendidikan, FKIP, Indonesia https://orcid.org/0000-0002-7075-5762
Muhammad Sofwan Universitas Jambi, Fakultas Keguruan dan Ilmu Pendidikan, Jambi, Indonesiai https://orcid.org/0000-0001-6936-1267
Turki Mesfer Alqahtani Jazan University, College of Arts and Humanities, e-Learning Centre and Department of Educational Sciences, Jazan, Saudi Arabia https://orcid.org/0000-0002-7226-9602

DOI:

https://doi.org/10.1590/1983-3652.2025.57135

Palabras clave:

Artificial Intelligence, Pre-service EFL teacher, Survey, TAM, TPACK

Resumen

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).

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