Performance motivation and emotion regulation as drivers of academic competence and problem‐solving skills in AI‐enhanced preschool education: A SEM study

Abstract The swift adoption of artificial intelligence (AI) in preschool education has sparked widespread interest, with emerging evidence suggesting that tools powered by AI can significantly improve early learning experiences by personalising instruction and supporting cognitive growth. Despite these advancements, the interplay between psychological factors (e.g., performance motivation and emotion regulation) and crucial educational outcomes (e.g., academic competence and problem‐solving skills) has remained relatively unexamined in AI‐enhanced preschool contexts. To bridge this gap, this study investigated the relationships among performance motivation, emotion regulation, academic competence and problem‐solving skills within AI‐enhanced preschool education in China. Adopting a quantitative design, the study included 464 preschool‐aged children (4–6 years), specifically selected within this developmental range. Data were gathered using age‐appropriate and validated tools and analysed through structural equation modelling (SEM). The findings indicated that performance motivation was a robust predictor of both academic competence and problem‐solving skills. Similarly, emotion regulation demonstrated significant correlations with academic competence and problem‐solving skills. The study proposes that incorporating strategies to bolster performance motivation and emotion regulation into AI‐enhanced preschool programmes can substantially elevate educational outcomes. These insights have practical implications for curriculum designers, instructors and technology developers seeking to harness AI's potential in early childhood education.