Teachers' Behavioral Intention Toward Using Generative Artificial Intelligence (GAI): The Role of TPACK and Self-Efficacy

Abstract

Generative Artificial Intelligence (GAI), representing a new phase in AI development, is profoundly transforming the field of education, presenting novel opportunities for educational innovation and reform. This study aimed to investigate the key predictors of the behavioral intentions of primary and secondary school teachers in adopting GAI for instructional support. A combination of convenience sampling and snowball sampling was employed to collect questionnaire data from 561 K-12 teachers across multiple provinces in China. Based on the Technology Acceptance Model (TAM) and integrating core variables such as teachers' Technological Pedagogical and Content Knowledge (TPACK) and self-efficacy, the data were analyzed using structural equation modeling.The results indicated that teachers' TPACK, self-efficacy, and perceived usefulness of GAI all significantly and positively predicted their behavioral intention to use it. However, no direct correlation was found between perceived ease of use and behavioral intention. This study provides new empirical evidence for understanding teachers' acceptance mechanisms toward intelligent educational technology and offers practical implications for designing more targeted teacher professional development programs. The conclusions of this study are limited by its cross-sectional design and specific sample scope; future research could employ longitudinal designs and broader sampling to verify and deepen these findings.