Article
Analysis of International Research Trends in Social Empathy Education Using Topic Modeling
| e-ISSN | 2982-6845 |
| p-ISSN | 1226-4474 |
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Abstract
This study analyzes international research trends in social empathy education by collecting academic articles published from 2015 to 2025 and applying Latent Dirichlet Allocation (LDA) and semantic network analysis to the abstracts of 458 selected articles. As a result, eight topics were identified: empathy education enacted through practices of mutual respect; empathy education addressing social, cultural, and historical inequalities; evaluation of emotional intervention effects in empathy-based interventions; artificial intelligence (AI)- and digital tool-based development of social empathy; empathy-based education for training healthcare professionals; impact of teacher empathy on learner growth; empathy education promoting inclusion of socially vulnerable groups; exploration of the mechanisms underlying the development of empathy. In addition, semantic network analysis revealed relationships among keywords within each topic, supporting a much richer interpretation of the identified themes. Based on these findings, this study suggests implications for social empathy education in South Korea, including the need for practice-oriented and action-focused empathy education design, the expansion of AI and digital tool use, and the enhancement of empathy education for professional development. This study is significant in that it systematically analyzes international research trends and major topics in social empathy education based on large-scale bibliographic data.