A Study on AI Digital Textbooks Using Big Data Analysis Techniques: Focusing on Keyword and Perception Analyses

The purpose of this study is to collect and analyze academic papers, media, and social media data to explore academic discussions and social perceptions on AI Digital Textbooks, and to suggest the direction of AI Digital Textbook policy. The research methods and analysis results are as follows. First, a total of 79 domestic academic papers published from January 2018 to December 2024 were selected, and frequency analysis, TF-IDF analysis, N-gram analysis, connection centrality analysis, and CONCOR analysis were conducted. As a result of the analysis, four discussion points were drawn: (1) ‘introduction and execution of AI Digital Textbooks on-site’, (2) ‘learning-centered functional design’, (3) ‘construction of AI-based customized learning environment’, and (4) ‘development of teaching materials using Generative AI’. Second, media and social media data were collected through the first (2023.08∼2024.07) and the second (2024.08∼2025.04) to conduct time series and sentiment analysis. In the first period, expectations for the educational use of AI technology spread, but it was confirmed that negative perceptions increased rapidly in the second period. This suggests that joint consensus is needed in the field application of AI Digital Textbooks, and supplementary measures are needed in the establishment and implementation of policies. Based on these results, policy proposals and implications such as establishing data utilization guidelines and governance systems to promote AI Digital Textbook policies, supplementing digital infrastructure, and strengthening teachers' digital literacy capabilities were presented. Further analysis of documents from schools and offices of education for generalization in the future and follow-up studies at the point of view after active use of AI Digital Textbook in the educational field were proposed.