Rethinking teacher education in the context of AI-powered social-emotional learning: A systematic mapping review

Abstract

Education is undergoing changes linked to the digital transformation, the evolution of technologies, such as Artificial Intelligence (AI), and the implementation of methodologies such as Social Emotional Learning (SEL), which significantly contribute to redefining teacher training. The aim of this study focused on analysing and interpreting the relationship between technology, specifically AI and SEL, in the context of teacher training in the period 2015 and 2024. For this purpose, a systematic review was carried out following the PRISMA method. A total of 70 studies were analysed through frequency analysis, content analysis, and Pearson correlation analysis to determine key trends. The results reveal that a) SEL significantly enhances teacher effectiveness, emotional well-being, and classroom management, b) AI-driven tools improve personalised SEL training and predictive student engagement analytics, and c) Institutional resistance and lack of structured training remain major obstacles to SEL implementation. This review offers a threefold contribution: (a) it provides a conceptual lens to understand the dialectical relationship between AI, SEL, and teacher agency; (b) it employs a systematic mapping methodology to classify and visualize trends in a fragmented field; and (c) it offers practical insights for designing context-sensitive and ethically grounded teacher education programs. The study concludes by identifying critical research gaps and proposing directions for future inquiry and policy development.