<![CDATA[The low level of social awareness among elementary school students, marked by limited empathy, social care, and social responsibility, remains a critical issue in Social Studies (IPS) learning amid the ongoing transformation of education that demands meaningful and high-quality learning experiences. This article aims to describe the implementation of a social phenomena interpretation strategy in IPS learning and to examine how artificial intelligence-based data analysis can strengthen the accuracy of understanding the process by which students' social awareness develops. The study employed a qualitative approach with a single case study design involving one teacher and thirteen fourth-grade students at SDN Jatisari. Data were collected through observation, interviews, and documentation, then analyzed using the interactive Miles, Huberman, and Saldana model, assisted by NVivo 12 Plus software for coding and data visualization through Project Maps and Hierarchy Charts. The results show that the interpretation strategy was implemented through six interrelated stages: observing social phenomena, identifying social facts, understanding social problems, analyzing causes, interpreting social values, and formulating attitudes and solutions. This strategy supported the development of empathy, social care, and social responsibility among students, although progress varied across individuals. Artificial intelligence-based data visualization proved effective in revealing patterns and the dominance of particular learning stages more systematically and objectively than manual analysis. These findings affirm that the use of artificial intelligence in educational data analysis is relevant not only as a research tool but also as a means of strengthening the quality of learning assessment in elementary schools, in line with efforts to support quality education and the formation of a socially aware society.]]>
