Pre-service teachers' technology acceptance of artificial intelligence (AI) applications in education - Isidro Max V. Alejandro; Joje Mar P. Sanchez; Gino G. Sumalinog; Janet A. Mananay; Charess E. Goles; Chery B. Fernandez (STEM Education)
We verified a pre-service teachers' Extended Technology Acceptance Model (ETAM) for AI application use in education. Partial least squares structural equation modeling (PLS-SEM) examined data from 400 pre-service teachers in Central Visayas, Philippines. Perceived usefulness and attitudes, usefulness and attitudes, ease of use and attitudes, and intention to use AI apps were significantly correlated. However, subjective norms, experience, and voluntariness did not affect how valuable AI was viewed or intended to be used. Attitudes toward AI mediated specific correlations use. These findings improve the ETAM model and highlight the significance of user-friendly AI interfaces, educational activities highlighting AI's benefits, and institutional support to enhance pre-service teachers' adoption of AI applications in education. Despite its limitations, this study establishes the foundation for further research on AI adoption in educational settings.