Integrating Socio-Emotional Learning in School Counselling Programmes

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Authors: Margaret George (PhD) Kennedy
Cited by: 0

Abstract

The evolving demands of the 21st-century workforce require competencies that extend beyond cognitive intelligence to include emotional regulation, interpersonal competence, adaptability, and resilience. This paper examines the role of emotional intelligence (EI) in shaping a productive workforce and advances a case for the systematic integration of socio-emotional learning (SEL) into school counselling programmes. Anchored within the framework of the Sustainable Development Goals, particularly Sustainable Development Goal 4 (Quality Education) and Sustainable Development Goal 8 (Decent Work and Economic Growth), this study conceptualizes emotional intelligence as a foundational human capital asset. Drawing on empirical literature, the paper synthesizes evidence linking emotional intelligence competencies, self-awareness, self-regulation, motivation, empathy, and social skills to workplace productivity, leadership effectiveness, job satisfaction, conflict management, and organizational commitment. The review further argues that early acquisition of socio-emotional competencies through structured school counselling interventions enhances employability, adaptability, and long-term career sustainability. The paper adopts a position that workforce productivity is not solely an economic function but a developmental outcome shaped by educational systems. It recommends embedding structured SEL frameworks within school counselling programmes to foster emotionally competent graduates capable of contributing meaningfully to sustainable economic growth. By aligning emotional intelligence development with SDG 4 and SDG 8 targets, this study advances a policy-relevant and development-oriented perspective on education to workforce transitions.

Fields of Study & Subjects

Emotional Intelligence and Performance Social Skills and Education Early Childhood Education and Development

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
5 March 2026
Language
EN
Document type
Dataset