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 setting of standards is a critical process in educational evaluation, but it is time-consuming and expensive because it is generally conducted by an education experts group. The purpose of this paper is to find a suitable cluster validity index that considers the futures of item response data for setting cut-off scores. In this study, nine representative cluster validity indexes were used to evaluate the clustering results. Cohen’s kappa coefficient is used to check the conformity between a set cut-off score using four clustering techniques and a cut-off score set by experts. We compared the cut-off scores by each cluster validity index and by a group of experts. The experimental results show that the entropy-based method considers the features of item response data, so it has a realistic possibility of applying a clustering evaluation method to the setting of standards in criterion referenced evaluation.

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.

The rapid advancement of digital technologies and artificial intelligence (AI) is reshaping K-12 education, thereby emphasizing the growing need for AI Literacy among teachers. This study identifies key factors that influence teachers’ self-perceived AI Literacy and evaluates their impact on AI Literacy performance across various teaching phases, using Extreme Gradient Boosting (XGBoost) and Shapley Additive Explanations (SHAP). Data collected from 1172 K-12 teachers in South Korea were preprocessed and then split into an 80:20 training-to-testing ratio. To optimize model performance, Bayesian Optimization was used to fine-tune key hyperparameters, including the learning rate, maximum depth, subsample ratio, and number of boosting rounds. The model’s predictive accuracy was assessed using R², MSE, MAE, and RMSE. The optimized model achieved R² values of 0.8206 (Class Preparation), 0.8007 (Class Implementation), 0.8066 (Class Assessment), and 0.7746 (Utilizing Assessment Results). The results indicate that technical knowledge and AI Literacy skills are the most influential factors in the Class Preparation and Implementation Phases, while educational decision-making and ethical considerations play a crucial role in the Assessment and Utilizing Assessment Results Phases. Further, SHAP analysis highlights that both teachers’ and students’ perceived levels of AI learning significantly impact the adoption of AI Literacy, underscoring the importance of contextual factors in integrating AI within education. These findings emphasize the need for AI Literacy education that integrates technical competencies, pedagogical strategies, and ethical decision-making. This study provides empirical insights to support the development of teacher training programs and AI Literacy policies, ensuring the effective integration of AI in education.

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.

The principles of computer skills have been included in primary and secondary educated since the early 2000s, and the reform of curricula is related to the development of IT. Therefore, curricula should reflect the latest technological trends and needs of society. The development of a curriculum involves the subjective judgment of a few experts or professors to extract knowledge from several similar documents. More objective extraction needs to be based on standardized terminology, and professional terminology can help build content frames for organizing curricula. The purpose of this study is to develop a smart system for extracting terms from the body of computer science (CS) knowledge and organizing knowledge areas. The extracted terms are composed of semantically similar knowledge areas, using the word2vec model. We analyzed a higher-education CS standards document and compiled a dictionary of technical terms with a hierarchical clustering structure. Based on the developed terminology dictionary, a specialized system is proposed to enhance the efficiency and objectivity of terminology extraction. The analysis of high school education courses in India and Israel using the technical term extraction system found that (1) technical terms for Software Development Fundamentals were extracted at a high rate in entry-level courses, (2) in advanced courses, the ratio of technical terms in the areas of Architecture and Organization, Programming Languages, and Software Engineering areas was high, and (3) electives that deal with advanced content had a high percentage of technical terms related to information systems.