Social emotional learning in artificial intelligence-driven education: a bibliometric and strengths, weaknesses, opportunities, and threats analysis

Abstract

The rapid integration of artificial intelligence (AI) into education has transformed how social emotional learning (SEL) is conceptualized, yet a comprehensive understanding of it is research trajectory and strategic implications remains limited. This study addresses this gap by examining influential SEL research within AI-driven educational contexts. A mixed analytical approach was employed, combining bibliometric performance analysis and strengths, weaknesses, opportunities, threats (SWOT) analysis. Using the Scopus database, publications from 2016-2025 were systematically identified according to PRISMA procedures, and the 10 most-cited articles were selected for in-depth evaluation. The findings reveal three dominant research orientations: critical policy discourse, pedagogical integration of SEL, and emerging AI-driven innovations. The SWOT analysis highlights strengths in AI-enabled personalization and engagement, as well as opportunities for adaptive emotional learning environments. However, weaknesses related to limited empirical validation and threats concerning ethical risks, emotional surveillance, and algorithmic bias persist. The study concludes that while AI presents transformative potential for SEL, its implementation must remain human-centered and ethically grounded. These findings offer important implications for educators, policymakers, and future research in developing responsible AI-supported SEL frameworks.