The advancement of artificial intelligence is transforming educational practices, including how teachers design lessons. As artificial intelligence tools increasingly support lesson planning, content development, and assessment preparation, it is important to examine the factors that enable preservice teachers to design artificial intelligence -integrated lessons responsibly and pedagogically. Although the Technology Acceptance Model has been widely used to explain technology adoption, it may not fully capture the cognitive, affective, and pedagogical dimensions of artificial intelligence integration in teacher education. This study employed a quantitative explanatory design and collected survey data from 375 preservice teachers in Indonesia. The findings showed that artificial intelligence readiness was significantly associated with artificial intelligence -integrated lesson design and helped explain how technology self-efficacy and perceived usefulness are linked to pedagogical innovation. Emotional and psychological factors partially mediated the relationships of perceived usefulness and technology self-efficacy with artificial intelligence readiness but did not mediate their relationships with artificial intelligence -integrated lesson design, suggesting that cognitive and competence-related factors are more directly associated with the act of lesson design. The proposed model explained 70.8% of the variance in artificial intelligence -integrated lesson design, indicating strong predictive power. These findings suggest that artificial intelligence readiness plays an important role in connecting preservice teachers' beliefs and capabilities with practical pedagogical innovation.
Tusyanah et al. (Tue,) studied this question.