Field-theoretical analysis of AI-enabled social support and teacher professional identity in preschool teacher education

Strengthening the professional identity of preservice teachers with effective social support is an important approach for tackling teacher turnover in the field of early childhood education. This study explores the predictive effect of AI-enabled social support on the formation of TPI from the perspective of sociological field theory, investigating its underlying psychological mechanisms. A large sample size of 1553 final-year preservice preschool teachers from China was used in an online survey for the purpose of generating empirical evidence for the research. Variance-based Partial Least Squares Structural Equation Modelling (PLS-SEM) revealed a significant direct association between AI-enabled social support and TPI. Furthermore, the analysis confirmed robust indirect predictive relationships operating sequentially through self-efficacy and pedagogical beliefs. Theoretically, the research reconceptualises the notion of AI-enabled social support as a convertible form of field capital, which is positively related to the attenuation of habitus hysteresis in the process of the critical transition from student to novice educator. Practically, the research suggests that teacher education programmes should be designed in a way that moves beyond the mere provision of technology in aiding preservice teachers in navigating the pedagogical affordances and ethical complexities of artificial intelligence in a critical manner. Ultimately, such structural interventions are expected to positively predict the cultivation of resilient professional identities and the improvement of educator retention rates.