Research Agenda and Directions for Social Studies AI Digital Textbooks (AIDT) through the Case Analysis of Math and English AIDT Implementation

This study aims to critically examine the implementation cases of AI Digital Textbooks (AIDT) in mathematics and English education and to propose theoretical directions and practical research agendas for designing social studies AIDT. While AIDT is emerging as a future-oriented educational platform offering personalized learning, real-time feedback, and inquiry-based functions, early implementations reveal critical limitations such as repetitive algorithms in math and lack of cultural contextualization in English. Based on a text coding analysis of 784 responses from Korea’s largest elementary teacher community, this study identifies key pedagogical and technological concerns that must be addressed. Unlike subjects with clear-cut answers or linguistic performance goals, social studies require context-sensitive, value-laden, and multi-perspective learning. Accordingly, this paper proposes four core directions: (1) domain-specific algorithm branching based on disciplinary cognitive structures; (2) implementation of explainable and ethically-guided AI; (3) scenario-based inquiry models emphasizing locality and participation; and (4) collaborative learning environments encouraging civic engagement. A four-stage AIDT-based instructional design model and a lesson scenario applied to the elementary unit “Changes in Our Region” are presented to demonstrate both theoretical coherence and practical feasibility.