Indonesian preservice teachers’ attitudes toward using ChatGPT: a structural equation model

Abstract

This study investigates 232 pre-service physics teachers' attitudes toward using ChatGPT, an artificial intelligence-based conversational agent, in educational settings. With the increasing integration of technology in education, understanding pre-service teachers' perceptions of AI-driven tools is crucial for effective implementation. The research employs Partial Least Squares-Structural Equation Modelling (PLS-SEM) to analyze pre-service teachers' attitudes, including evaluating and checking, instructional design, multiple information, problem-solving, and time efficiency. The findings provide valuable insights into the validity and reliability of the measurement model and shed light on the structural relationships among the constructs under investigation. These insights have implications for theory and practice in educational research and instructional design. The implications for teacher education programs and the future integration of AI tools in pedagogical practices are also discussed.

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