Social-Emotional Learning and Generative AI: A Critical Literature Review and Framework for Teacher Education

Abstract

This article provides a critical thematic literature review that explores the intersection of generative artificial intelligence (GenAI) and social-emotional learning (SEL), analyzing its implications for teacher education. GenAI offers promising applications for enhancing SEL competencies such as self-awareness, empathy, and social skills through tools like real-time emotional feedback and personalized learning experiences. However, the integration of GenAI into SEL also presents significant challenges, including risks of depersonalization, algorithmic bias, and privacy concerns. This paper introduces a conceptual framework designed to prepare both pre-service and in-service teachers to navigate these complexities, emphasizing ethical considerations, human oversight, and cultural sensitivity. The framework highlights strategies to operationalize cultural sensitivity within AI systems, recognizing the limitations of current technologies in accounting for diverse social and emotional norms. By addressing both opportunities and risks, we aim to provide a balanced analysis of GenAI’s potential in SEL as well as guidance for teacher education programs.