Ti and colleagues employed a cross-sectional survey design with hierarchical regression and moderation analyses to examine how in-service teachers’ teaching emotions and teaching ability mindset predict their AI readiness, and whether mindset moderates the relationship between emotions and AI readiness. The study included 424 in-service teachers in China (mean age = 38.76 years) from both primary and secondary schools. AI readiness was measured using Wang et al.’s (2023) four-dimensional framework, including cognition, ability, vision, and ethics. Teaching emotions were categorized into positive and negative emotions, and teaching mindset was classified as growth or fixed. Gender and social desirability bias were controlled in the analyses, and interaction effects were tested using the PROCESS macro.
The results showed that positive teaching emotions significantly and positively predicted all four dimensions of AI readiness (B ≥ .48, p < .001), whereas negative emotions did not significantly predict any dimension (|B| ≤ .06, p ≥ .309). Regarding mindset, a growth teaching mindset had significant positive effects on cognition, ability, vision, and ethics (B ≥ .18, p < .01), indicating that teachers who view teaching ability as developable are better prepared to respond to AI-related educational change. Interestingly, a fixed teaching mindset did not uniformly produce negative effects; instead, it positively predicted the cognition and ability dimensions (B ≥ .17, p < .01), although it was not significant for vision and ethics. Overall, the inclusion of emotions and mindset in the models yielded medium to large effect sizes (.28 ≤ f² ≤ .36), suggesting substantial explanatory power.
Moderation analyses further revealed that a growth teaching mindset strengthened the positive relationship between positive emotions and AI cognitive readiness (B = .11, p < .05). In other words, teachers with both high positive emotions and a strong growth mindset demonstrated higher levels of understanding regarding AI roles and functions. In contrast, a fixed teaching mindset moderated the relationship between negative emotions and the cognitive dimension (B = .10, p < .05). When fixed mindset was low, negative emotions significantly reduced cognitive readiness (B = –.18, p < .05), whereas this effect was not significant when fixed mindset was high. Notably, moderation effects were observed only for the cognition dimension, suggesting that the cognitive aspect of AI readiness is particularly sensitive to the interaction between emotional and mindset resources.
Overall, this study indicates that teachers’ readiness for AI integration in education is influenced not only by technical training or institutional support but also by their emotional experiences and beliefs about the malleability of teaching ability. Positive emotions and a growth teaching mindset serve as important psychological resources that enhance AI readiness, especially in shaping teachers’ cognitive understanding of AI. The authors recommend that AI-related professional development initiatives incorporate emotional regulation support and mindset cultivation to foster more comprehensive and sustainable AI readiness among teachers.
Source (Open Access): Ti, Y., Sun, Y., & Li, X. (2026). Predicting in-service teachers’ AI readiness from emotions in teaching and mindsets about teaching ability: Testing the direct and moderating effects. Teaching and Teacher Education, 175, 105433.