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 purpose of this study is to use Squeak etoys and Arduino to design education activities so that middle school students can learn the object-oriented paradigm, and assess how usable the education activities are at the level of middle school students. As for education activities, learning methods, such as imitating, modifying and creating, were introduced. Usefulness assessment was divided into a cognitive domain and a definitional domain before the assessment tools were developed. An object-based programming class for 11 middle school students was then conducted and the results analysed. Positive results were confirmed in both the cognitive domain and the definitional domain. The results of this study are meaningful in that the possibility of object-oriented education was discovered for middle school students as well. Also, considering the sequence of object-oriented education, it was found that it is necessary to develop object-oriented programming education at the level of both elementary and high schools.

Since 2010 countries around the world have been emphasizing the importance of informatics education based on computer science rather than ICT use. Korea devised an informatics curriculum that emphasizes SW education as part of its revised curriculum of 2015. The purpose of this study is to examine the factors affecting SW education achievement before implementation of the curriculum so that the revised curriculum could be efficiently established in schools. SW education was provided for 4221 elementary school students, and the factors affecting academic achievement were extracted. The results of the study showed that the achievement level of female students was higher than that of male students, and the level of understanding increased in higher grades. It was found that satisfaction with overall SW education influenced academic achievement. That is, the more satisfied students were with the key factors, such as the infrastructure required for providing SW education and interaction among teachers and students during class time, the higher the level of satisfaction. This study intended to find the key factors necessary for helping SW education take root in schools.

Many artificial intelligence studies focus on designing new neural network models or optimizing hyperparameters to improve model accuracy. To develop a reliable model, appropriate data are required, and data preprocessing is an essential part of acquiring the data. Although various studies regard data preprocessing as part of the data exploration process, those studies lack awareness about the need for separate technologies and solutions for preprocessing. Therefore, this study evaluated combinations of preprocessing types in a text-processing neural network model. Better performance was observed when two preprocessing types were used than when three or more preprocessing types were used for data purification. More specifically, using lemmatization and punctuation splitting together, lemmatization and lowering together, and lowering and punctuation splitting together showed positive effects on accuracy. This study is significant because the results allow better decisions to be made about the selection of the preprocessing types in various research fields, including neural network research.