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

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Bibliographic Details
Title: Social-Emotional Learning and Generative AI: A Critical Literature Review and Framework for Teacher Education
Language: English
Authors: Danah Henriksen (ORCID 0000-0001-5109-6960), Edwin Creely (ORCID 0000-0002-5009-4047), Natalie Gruber (ORCID 0000-0001-7256-7610), Sean Leahy (ORCID 0000-0001-6840-371X)
Source: Journal of Teacher Education. 2025 76(3):312-328.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 17
Publication Date: 2025
Document Type: Journal Articles
Information Analyses
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Social Emotional Learning, Artificial Intelligence, Computer Software, Risk, Preservice Teachers, Teacher Education Programs, Technology Integration, Technological Literacy, Pedagogical Content Knowledge, Privacy, Guidelines, Ethics, Bias, Individualized Instruction, Faculty Development, Cultural Awareness, Guidance, Teaching Methods, Research Reports
DOI: 10.1177/00224871251325058
ISSN: 0022-4871
1552-7816
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.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1469503
Database: ERIC
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