Determinants of Behavioral Intention to Use Generative Artificial Intelligence on Economics Teachers in Semarang - Sofyan Setiyawan; Agus Wahyudin; Amir Mahmud (Universitas Negeri Semarang, 2025)
This study aims to analyze the factors influencing economics teachers' behavioral intentions to use Generative Artificial Intelligence (GAI) in teaching, using the Unified Theory of Acceptance and Use of Technology (UTAUT) as the foundational framework. The respondents of the study were high school economics teachers in Semarang City, Central Java, Indonesia. Data were collected from 119 respondents using the accidental sampling technique. A questionnaire developed by previous researchers was used to gather research data. Structural Equation Modeling-Partial Least Squares (SEM-PLS) was employed as the data analysis method. The findings indicate that performance expectancy, effort expectancy, social influence, and facilitating conditions significantly determine the level of teachers' intention to use GAI in teaching. Furthermore, the Technological Pedagogical Content Knowledge (TPACK) variable serves as a moderating variable in the research model. The practical implications of this study suggest that educational institutions, teachers, and policymakers need to enhance understanding, provide training, and implement policies that support the integration of TPACK-based Generative AI. These efforts aim to encourage the effective use of this technology to improve the quality of teaching and learning.