FROM AUTOMATION TO SOCIO-AFFECTIVE LEARNING: A BIBLIOMETRIC ANALYSIS OF ARTIFICIAL INTELLIGENCE IN ENGLISH AS A FOREIGN LANGUAGE EDUCATION
FROM AUTOMATION TO SOCIO-AFFECTIVE LEARNING: A BIBLIOMETRIC ANALYSIS OF ARTIFICIAL INTELLIGENCE IN ENGLISH AS A FOREIGN LANGUAGE EDUCATION

Summary/Abstract: Artificial intelligence (AI) has become a driving force in education. Within English as a Foreign Language (EFL) pedagogy, it is reconfiguring instructional approaches by linking automation with learner-centered and affective learning design. Nevertheless, empirical research remains fragmented across cognitive, affective, and social domains, necessitating a comprehensive approach. Drawing on data from the Web of Science (WoS) Core Collection (2021–2025), this study examines the bibliometric landscape of the field. Following PRISMA-based screening, 281 peer-reviewed publications were analyzed with VOSviewer and CiteSpace. Results demonstrate a sharp rise in publications and a conceptual evolution from performance-driven to socio-affective perspectives. Co-citation analysis delineates three major research domains–conversational AI in pedagogy, AI-based assessment tools, and affective–motivational learning–while co-occurrence mapping identifies four thematic clusters: chatbots and emotions, generative AI and critical thinking, automated writing evaluation and feedback, and technology adoption and motivation. Burst detection indicates a growing focus on positive psychology and communication skills. Building on these results, the study suggests the Affective–Cognitive–Social Integration Model (ACSIM), which extends existing acceptance frameworks to encompass motivation, emotion, and ethics. The model bridges automation-oriented AI research with socio-affective pedagogy and informs emotionally intelligent instruction, AI literacy among teachers, and institutional practices for responsible and sustainable use.

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