Understanding AI ethics in education with a LLM-based chatbot: Evidence from PLS-SEM and fsQCA analyses

As Artificial Intelligence (AI) becomes increasingly and widely integrated in education, fostering teachers’ understanding of AI ethics has become critical. This study investigates the acceptance of a chatbot based on a Large Language Model (LLM), designed to enhance teachers’ understanding of AI ethics in education. A mixed-methods approach combining Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) was applied in the current study with 142 participants. Findings show that the content accuracy, system functionality and stability significantly influence perceived ease of use and perceived usefulness. FsQCA further revealed five sufficient configurations associated with high continuance intention. These findings provide practical guidance for designing AI-supported learning environments and offer directions for future research.