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The field of artificial intelligence (AI) is permeating education worldwide, reflecting societal changes driven by advancements in computing technology and the data revolution. Herein, we analyze the alignment between core AI educational curricula and textbooks to provide guidance on structuring AI knowledge. Text mining techniques using Python 3.10.3 and frame-based content analysis tailored to the computing field are employed to examine a substantial amount of text data within educational curriculum textbooks. We comprehensively examine the frequency of knowledge incorporated in AI curricula, topic structure, and practical tool utilization. The degree to which keywords are reflected in curriculum textbooks and in the textbook characteristics are determined using Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) analysis, respectively. The topic structure distribution is derived by Latent Dirichlet Allocation (LDA) topic modeling and the trained model is visualized using PyLDAvis. Furthermore, the variation in vertical content range or level is investigated by content analysis, considering the tools used to teach similar AI knowledge. Lastly, the implications for AI curriculum structure are discussed in terms of curriculum composition, knowledge construction, practical application, and curriculum utilization. This study provides practical guidance for structuring curricula that effectively foster AI competency based on a systematic research methodology.

The advancement of computing technology has led to many changes in a variety of fields, and the importance of programming education has been emphasized in many countries worldwide. Despite the importance of programming education, the cognitive burden of text programming for beginners has not been reduced. The goal of this study was to implement an environment where a text programming language is used in a block-based programming environment and to determine at which school level this learning environment affects positive perceptions of programming. To achieve this goal, we conducted programming classes targeting 128 middle school, high school, and university students for 14 weeks and analyzed the effects of the factors of “understanding of programming instructions”, “usage confidence”, and “usefulness” on “positive perceptions of programming”. The results of the analysis by school level show that “usefulness” influenced positivity toward programming for middle school students, “usefulness” and “understanding of programming instruction” for high school students, and “understanding of programming instruction” and “usage confidence” for university students. Therefore, the significance of this study confirms the need to construct the learning environment differently depending on school level, even for beginners.

Artificial intelligence (AI) is bringing about enormous changes in everyday life and today’s society. Interest in AI is continuously increasing as many countries are creating new AI-related degrees, short-term intensive courses, and secondary school programs. This study was conducted with the aim of identifying the interrelationships among topics based on the understanding of various bodies of knowledge and to provide a foundation for topic compositions to construct an academic body of knowledge of AI. To this end, machine learning-based sentence similarity measurement models used in machine translation, chatbots, and document summarization were applied to the body of knowledge of AI. Consequently, several similar topics related to agent designing in AI, such as algorithm complexity, discrete structures, fundamentals of software development, and parallel and distributed computing were identified. The results of this study provide the knowledge necessary to cultivate talent by identifying relationships with other fields in the edutech field.