Exploring the impact of AI and metaverse on student academic and personal growth through social–emotional learning: a SEM-ANN approach

Purpose This study aims to examine the impact of artificial intelligence (AI) technology and metaverse on academic and personal growth of students, moderated by the components of social–emotional learning (SEL), that is, emotional intelligence and peer relationships. Design/methodology/approach The study uses a two-stage hybrid structural equation modeling (SEM) artificial neural network (ANN) methodology. The initial phase uses SmartPLS4 to perform partial least squares SEM for exploratory investigations. Further, to explore the non-linear interaction between variables, an ANN model is used. Findings Findings suggest that perceived usefulness significantly impacts academic growth. The relationship is enhanced by moderators such as peer relationships and emotional intelligence, where higher perceived usefulness weakens the contribution of peer relationships to personal growth, while emotional intelligence strengthens the contribution of perceived usefulness to personal growth. Moreover, the study identifies social–emotional variables as key predictors of enhanced personal and academic growth. Research limitations/implications This study offers major implications for higher education by demonstrating how integrating the Technology Acceptance Model and SEL frameworks can enhance students’ academic and personal growth. Practically, it urges institutions to prioritize emotional intelligence and peer relationships alongside technological adoption, promoting a more inclusive, sustainable and student-centered learning environment. Originality/value This study examines the use of emerging technologies, AI and metaverse applications in higher education. Its contribution lies in integrating them with the SEL framework. Rather than treating learner characteristics as predictors, this study positions emotional intelligence and peer relationships as moderators that influence whether technology engagement leads to academic and personal growth. By further modeling non-linear relationships among variables, this study refines the predictive understanding of technology-enhanced learning and informs the andragogical implementation of these tools.