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The purpose of this study is to analyze the domestic research trends of Youth workers from 2008 to 2017. To achieve this, as a result of searching the keywords(Youth workers, licenced youth workers, youth counselors) through the thesis search site, analysis was conducted according to year, research analysis method, and research topic targeting 52 thesis and 44 papers published in academic journals that meets the purpose of this study. When looking at the analysis results, by degree, it has shown 44 master's thesis(44.8%) and 9 doctoral thesis(9.4%), and in relation to the inclusion in journals, 34 papers(35.4%) were included, 2 papers(2.1%) were nominated, and 8 papers(8.3%) belonged to other journal articles. By research analysis method, it was shown in the order of 57 papers(59.4%) on quantitative research, 15 papers(15.6%) on quantitative and qualitative research, 12 papers(12.5%) on qualitative research, and 12 papers(12.5%) on literature review, in which the quantitative research was dominant. For analysis method, it was shown in the order of frequency analysis followed by correlation analysis, ANOVA, regression analysis, and t-test. For the number of researchers, it was shown in the order of 71 single-person papers(74.0%) including thesis, 17 joint research papers(17.7%), and 8 three-man research papers(8.3%), where the results have shown that interdisciplinarity was good but collaboration was low. Next, research topics were organized by types and analyzed. The discussion of research was concluded based on these results and has proposed the significance of the research and its limitations.

Patent analysis has been performed in various areas of technology management to understand technology such as research and development planning, new product development, technology innovation, sustainable technology etc. In the patent analysis, the analysis of patent keywords with technological information is very popular and meaningful. So we study on a method for patent keyword analysis, and use the cognitive diagnosis model (CDM) to construct the proposed method. We call our method technological cognitive diagnosis model (TCDM), this provides significant technology structure for understanding target technology using the result of patent keyword analysis by TCDM. We illustrate the performance of the TCDM by the experiments using the patent documents related to artificial intelligence (AI) technology. The final goal of these experiments is to find the relationship between core technologies for AI.

Purpose: This study aimed to examine research trends and thematic keyword networks in disaster nursing competency literature published up to September 15, 2024. Methods: A retrospective descriptive design was used to analyze English-language articles retrieved from five databases (CINAHL, Cochrane, Embase, PubMed, and Web of Science) through September 15, 2024. Following PRISMA guidelines, 256 articles and 1,318 keywords were extracted. NetMiner 4.0 was used for keyword preprocessing and analysis. Word clouds and text network analyses were performed. Degree and betweenness centralities were calculated to determine keyword prominence and network roles. Results: Annual publications increased notably after 2020, coinciding with the COVID-19 pandemic. Studies originated from 32 countries, with the United States, China, and Iran leading. Cross-sectional quantitative (34.0%) and qualitative studies (26.6%) were the most commonly used study designs. From 44 high-frequency keywords, “nurses,” “preparedness,” “competency,” and “disaster nursing” ranked highest in both centrality measures. “Education” and “management” also showed strong centralities. Cluster analysis revealed that preparedness and response phases were emphasized more than mitigation and recovery. Conclusion: This study provides a comprehensive visualization of disaster nursing competency research. Findings highlight the dominance of preparedness-focused studies, limited exploration of mitigation and recovery. Future research should prioritize broader disaster phases, standardize terminology, and conduct evidence-based intervention studies to strengthen disaster nursing practice and education.

The present study is based on a self-built Maritime English Law Corpus compared with BNC Baby as a reference corpus to explore some homogeneous features of four different maritime legal genres through the comparison of two different keyword analyses: corpus frequency-based keyword analysis and text dispersion-based keyword analysis. A comparison of keyword lists of four legal genres by using a cross-validation is also conducted to explore unique characteristics of each genre. The results show that two keyword methods generated both shared words and unshared words. According to the two criteria of keywords, we concluded that text dispersion-based keyword analysis is much better than traditional corpus frequency-based keyword analysis because the former meets both the content-distinctiveness of maritime-related keywords and the content-generalisability of law content keywords as well as showing more homogeneous maritime legal features than the latter.

This study aims to investigate the stylistic features of maritime fiction written by Louis Becke, one of the best maritime fiction writers in Australia, in terms of keywords extracted from a study corpus compiling his five maritime novels against a reference corpus composed of general literary novels selected from BNC Baby. Additionally, we compare the list of keywords extracted from the study corpus with that of Joseph Conrad’s maritime fiction corpus to ascertain the distinctive stylistic features of Louis Becke’s writings. The results show that Becke’s maritime fiction has comparatively smaller standardized type/token ratio, shorter mean word length, a broader variety of the maritime vocabulary, and more unique function words than that written by Joseph Conrad, a Polish-British author. Interestingly, some function words extracted as keywords represent Becke’s stylistic characteristics: a conjunction coordinator (and), the first person pronouns (I, we, and me), and modal auxiliaries (will and shall). As expected, it was also observed that there are numerous maritime-related content words such as captain, boat(s), mate, ship, brig, crew, deck, board, reef, ashore, etc. in the top 50 keywords.

The International Convention for the Safety of Life at Sea (SOLAS), as the most important international maritime treaty regulating ship safety, establishes minimum safety levels for construction, equipment and operation. Due to the development of technology over time, safety understanding and management, all three safety standards have been undergoing revision. This paper aims to explore and explain the trends of these changing standards by employing keyword analysis of the SOLAS Convention based on the idea that keywords are indicative of changes in writing style, which can ultimately be linked back to social change. To achieve this objective, five consolidated versions of the SOLAS Convention, covering the period from 1974 to 2015, were targeted for data collection. Keywords for three safety standards in each version were extracted respectively using WordSmith Tools 6.0. Statistical measures, including frequency count and type-token ratio, were adopted to analyze the degree of diachronic changes in each safety standard, comparing the lexical distribution and density of each keyword list. The findings suggest that all three safety standards have been revised over time, and the changes vary in terms of degree and content. This study is believed to be useful in understanding the safety concerns in the maritime industry and to contribute to the literature in diachronic research conducted by a keyword-based approach.

사물인터넷은 인터넷이 비즈니스 프로세스를 근본적으로 변화시킨 이후의 기술로 간주되고 있다. 그러나 사물인터넷의 영역이 하드웨어적인 센서 기술로부터 애플리케이션을 통한 서비스까지 광범위하여 아직까지 연구도메인에 대한 구조가 명확하지 않다. 본 연구에서는 기업에 가치를 제공하기 위해서 사물인터넷의 성숙도를 측정하기 위하여 Porter 등 (2014)이 제안한 기술스택 모델을 적용할 것을 제안한다. 스택모델을 이용하여 사회과학, 복합학, 공학 분야에서 발간되는 논문을 대상으로, “사물인터넷 (IoT)”을 키워드로 포함하고 있는 논문의 저자들이 제공한 키워드 분석을 실시하여 사물인터넷 연구의 일반적인 동향을 살펴본다. 결과에 의하면, 클라우드와 빅데이터 분석 기반의 IoT 활용은 활발하지 못하고 결과적으로 IoT로부터의 가치가 충분히 실현되지 못하는 것으로 나타났다. 또한 가치 도출에 중요한 클라우드 프로세스를 적용하는 연구 논문 사례를 발췌하여 사물인터넷의 응용 수준을 측정하였다. 본 연구에서 IT의 가치사슬모형 적용과 유사하게, IoT의 가치를 높이기 위해 스택모델 적용을 제안한 것도 의미가 있다 할 수 있다.

Internet of Things (IoT) is considered to be the next wave of Information Technology transformation after the Internet has changed the process of doing business. Since the domain of IoT ranging from the sensor technology to service to the users is wide, the structure of the research domain is not delineated clearly. To do that we suggest to use the Technology Stack Model proposed by Porter et al.(2014) to measure the maturity level of IoT in organizations. Based on the Stack Model, for the general understandings of IoT, we do keyword analyses on the academic papers whose major research issue is IoT. It is found that the current status of IoT application from the perspectives of cloud and big data analytics is not active, meaning that the real value of IoT has not been realized. We also examine the cases which deal with the part of cloud process which is crucial for value accrual. Based on these findings, we suggest the future direction of IoT research. We also propose that IT is to value chain what IoT is to the Stack Model to derive value in organizations.

본 연구는 코로나19 전후 신문기사에서 다루고 있는 다문화의 정보격차 이슈를 비교분석하였다. 뉴스 빅데이터 시스템인 빅카인즈(BigKinds)와 구글뉴스검색을 통해 다문화 정보격차와 관련된 뉴 스내용을 키워드 빈도, 키워드 네트워크, 키워드 군집 등의 결과를 분석하여 제시하였다. 연구결과, 다문화 정보격차에 대한 사회적 관심은 코로나19 이후 높아졌으며, 다문화 정보격차의 핵심 이슈는 공통적으로 다문화가정과 교육으로 나타났다. 그러나 다문화 정보격차와 관련해 중점적으로 다루고 있는 주제는 시기에 따라 차이가 있는 것으로 나타났다. 코로나19 이전시기에는 학교 밖의 지원을, 코로나19 이후시기에는 학교교육을 중점적으로 보도하고 있으며, 코로나19 이전보다 이후시기에 다문화 집단을 세부적으로 구분하고, 다문화 정보격차를 다각적인 관점에서 접근하였다. 본 연구는 신문기사를 바탕으로 형성된 국내 다문화정보격차 관련 이슈를 분석하였다는 점에서 의의가 있다. 향후 지속적인 연구를 통해 다문화 정보격차 문제 해결을 위한 종합적인 정책방안을 모색할 수 있을 것이라 기대한다.

This study compares the multicultural digital divide issues addressed in newspaper articles before and after COVID-19. This study analyzes of keyword frequency, keyword network and cluster for news contents related ‘multicultural digital divide’ through BigKinds and Google News Search. As a result, social interest in the multicultural digital divide has increased after COVID-19. The core issues of the multicultural digital divide that appeared in all periods before and after COVID-19 were ‘multicultural family’ and ‘education’. However, it found that the topics focused on the multicultural digital divide differ depending on the period. Before COVID-19, the main report was the support programs outside the school. On the other hand, after COVID-19, school education-centered support to solve the multicultural digital divide was mainly reported, and the multicultural group was classified in details. Also the multicultural digital divide was approached from multiple perspectives. This study is significant in that it suggest a new research method by analyzing the multicultural digital divide based on newspaper articles. Through continuos research, it is expected to find the comprehensive policy measures to resolve the multicultural digital divide.

이 연구는 STEM 교육의 연구 동향을 정리하기 위해 대표적 국제 학술지인 International Journal of STEM Education에 게재된 논문을 대상으로 키워드 네트워크 분석을 실시하였다. 출판된 논문들의 저자 키워드를 수집하고, 출현 빈도 5회 이상인 키워드를 대상으로 공출현 네트워크를 구축하였다.분석 결과, STEM이 가장 높은 빈도를 보이는 키워드로 나타났으며, 다음은 STEM education, higher education, gender, motivation 등의 순서로 나타났다. 네트워크 분석을 실시한 결과, 키워드 네트워크는 총 다섯 개의 주제 군집으로 구분되었으며, STEM 및 STEM 교육 핵심 클러스터, 학습자 특성 및 형평성 클러스터, 동기 및 정의적 요인 클러스터, 교수·학습 개입 및 교수설계 클러스터, 교육성과·진로·인간 성장 확장 클러스터로 정리되었다.

This study conducted a keyword network analysis of articles published in the International Journal of STEM Education, a leading international journal focusing on teaching and learning in STEM education, in order to examine research trends in the field of STEM education. Author keywords from articles published were used for the analysis. Specifically, author keywords were collected, and a co-occurrence network was constructed using keywords that appeared at least five times. The results of the frequency analysis indicated that STEM was the most frequently occurring keyword, followed by STEM education, higher education, gender, and motivation. The network analysis further revealed that the keyword network could be classified into five thematic clusters: a core STEM and STEM education cluster, a learner characteristics and equity cluster, a motivation and affective factors cluster, a teaching and learning interventions and instructional design cluster, and an educational outcomes, career development, and human development expansion cluster.

목적: 이 연구는 메이크업과 관련된 논문의 주제어를 키워드 네트워크 분석을 통해 최근 10년 동안 연구된 핵심적 주제를 파악하 고자 하였다. 방법: 한국연구재단에서 제공하는 KCI 학술지 D/B에서 추출한 자료를 키워드 네트워크 분석을 활용하였다. 2013- 2022년 기간의 메이크업 관련 논문 585편을 분석에 사용하였다. 결과: 첫째, 메이크업 관련 키워드의 빈도를 살펴본 결과 '이미지' 이 가장 많은 빈도를 나타냈고 ‘아트메이크업’, ‘디자인’, ‘화장품’, ‘컬러’ , ‘반영구화장’ 같은 주제어의 순으로 빈도가 높았다. 둘 째, 1기(2013년-2017년), 2기(2018년-2022년)로 분리하여 살펴본 결과, 1기는‘ 이미지’, ‘컬러’, ‘디자인’, ‘아트메이크업’ 순이고, 2기는 ‘화장품’, ‘이미지’, ‘아트메이크업’, ‘디자인’, ‘반영구화장’ 순으로 연구가 이루어졌다. 셋째, 중심성 분석을 통해 키워드 간 속성을 알아본 결과 키워드들 간 가장 많은 상호작용을 하고 있는 키워드는 ‘이미지’이며 ‘디자인’, ‘아트메이크업’은 다른 키워드 들과 연결의 중심성이 높은 키워드로 메이크업 관련 연구에서 중요한 주제임을 알 수 있다. 결론: 메이크업과 관련된 연구는 이미 지, 화장품, 컬러, 예술, 행동, 서비스, 교육 등 다양하게 이루어지고 있지만, 제 4차 산업과 인공지능(AI) 등 융합 연구가 이루어지 고 이와 관련된 키워드가 향후 많이 등장해야 할 것으로 사료된다.

Purpose: This study aims to identify research on makeup-related topics from the past decade using keyword network analysis. Methods: This study used keyword network analysis to extract data from the Korea Citation Index journal database of the National Research Foundation of Korea. A total of 585 papers related to makeup, published between 2013 and 2022, were used for the analysis. Results: An examination of the frequency of makeup-related keywords indicated that “image” displayed the highest frequency, followed by “art makeup,” “design,” “cosmetics,” “color,” and “semi-permanent makeup.” The study then classified the year of publication into the first (2013–2017) and second (2018–2022) periods. The terms “image,” “color,” “design,” and “art makeup” and “cosmetics,” “image,” “art makeup,” “design,” and “semi-permanent makeup” were studied in these orders during the first and second periods, respectively. Third, the study explored the properties of keywords using centrality analysis. The results indicated that the keyword with the most interaction with other keywords is “image,” whereas “design” and “art makeup” are keywords with a high centrality of connection with other keywords used in makeup-related research. Thus, the result implies that this topic is important. Conclusion: Research related to makeup is being conducted on various topics, including image, cosmetics, color, art, behavior, service, and education. However, convergence research, such as the Fourth Industrial Revolution and artificial intelligence (AI), is being conducted, and keywords related to this are expected to appear in several cases in the future.

본 연구는 근골격계 질환으로 입원한 환자의 의무기록지 키워드 네트워크 분석을 통해 근골격계와 관련된 표준 의료용어를 유추하여 보건의료현장의 비정형화된 데이터 활용 방안을 제시하기 위함이다. 분석 대상은 2010년부터 2019년까지 근골격계 질환 환자의 입퇴원요약지 145부로, 더아이엠씨(The IMC)에서 개발한 빅데이터 분석 솔루션인 TEXTOM을 활용하여 분석하였다. 1차·2차 정제과정을 통해 도출된 177개의 근골격계 관련 용어를 최종 분석하였다. 연구결과 다빈도 용어는 ‘Metastasis’, 의료용어 체계별 분석 결과에서 임상소견은 ‘Metastasis’, 증상은 ‘Weakness’, 진단은 ‘Hepatitis’, 처치는 ‘Remove’, 신체구조는 ‘Spine’, 약물은 ‘Oxycodone’이 가장 많이 사용되었다. 이러한 결과를 바탕으로 정형화되지 않은 의료데이터의 분석과 활용 및 관리 방안에 대한 시사점을 제안하고자 한다.

The purpose of this study is to suggest a plan to utilize atypical data in the health care field by inferring standard medical terms related to the musculoskeletal system through keyword network analysis of medical records of patients hospitalized for musculoskeletal disorders. The analysis target was 145 summaries of discharge with musculoskeletal disorders from 2015 to 2019, and was analyzed using TEXTOM, a big data analysis solution developed by The IMC. The 177 musculoskeletal related terms derived through the primary and secondary refining processes were finally analyzed. As a result of the study, the frequent term was 'Metastasis', the clinical findings were 'Metastasis', the symptoms were 'Weakness', the diagnosis was 'Hepatitis', the treatment was 'Remove', and the body structure was 'Spine' in the analysis results for each medical terminology system. 'Oxycodone' was used the most. Based on these results, we would like to suggest implications for the analysis, utilization, and management of unstructured medical data.

As the tourism sector has become one of the fastest areas of growth in the contemporary business environment, tourism has been increasingly regarded as an important impetus to promoting export trade and economic growth in many countries. However, the tourism research field has not been reviewed systematically or comprehensively. Therefore, this study analyzes existing tourism literature by constructing keyword networks to identify the key concepts and issues that researchers are most interested in and to show changes in research trends. We collected 1,053 academic publications published between 2008 and 2019 in five leading journals to empirically show how keywords of paper are connected and form a network. Based on the degree of network centrality of keywords, we present the important keywords by year, by journal, and by time period to show similarities and differences in important concepts and issues prioritized by researchers. As a common result, it was found that service quality, customer satisfaction, and hotels/restaurants have been treated as important issues in the keyword network. It has also been shown that the introduction of information technology in the tourism industry is emerging as a recent research trend.

예술 심리치료 분야에서의 문헌분석은 이전부터 수행되어왔지만, 일반적으로 그러한 연 구는 특정 주제를 대상으로 한 것이다. 이에 본 연구의 목적은 광범위한 키워드 네트워크 분석을 통하여 예술 심리치료에 대한 연구동향을 파악하고자 하였다. 이를 위해 1980년부터 2017년까지 ‘The Arts in Psychotherapy' 저널에 게재된 1,256편의 논문을 분석하였다. 키 워드는 연대별로 구분하여 추출하였으며 키워드 간의 관계는 NetMiner 4.3 프로그램을 사 용하여 시각화하였다. 본 연구의 결과로, 특정 연대에 따른 추출된 키워드의 추세와 키워드 간 연결패턴의 변화가 구조적으로 조사되었다. 그러한 결과는 사회정치적 변화 및 정신건강 정책과 밀접하게 관련되어 있음을 나타냈다. 본 연구는 예술심리치료 분야의 연구동향을 하 나의 출처로 정리하여 기술함으로써 지난 37년 동안 예술 심리치료 분야의 가장 많이 논의 된 주제에 관한 자세한 설명을 제공한다.

Although literature reviews in the field of arts psychotherapy have previously been conducted, such studies generally only targeted specific topics. Therefore, the aim of this study was to identify research trends in arts psychotherapy through extensive keyword network analysis. To this end, a total of 1,256 articles published in Journal of ‘The Arts in Psychotherapy’ from 1980 to 2017 were analyzed. Keywords were extracted depending on period of decade and the relationship between keywords was visualized using the NetMiner 4.3 program. As a result of this study, trends of appeared keywords and changing patterns of linkages between keywords were structurally examined in a specific decade. Findings indicated that trends were closely related to sociopolitical changes and mental health policy. This study delineates research trends in this field, collating them into a single source. Further, it relates to articles published in the journal over the previous 37 years, providing a detailed description of the most-discussed topics.

Older adults with disability might have been increasing due to the rapid aging of society. Many studies showed that physical activity is an es-sential part for improving quality of life in later lives. Regular physical activity is an efficient means that has roles of primary prevention and secondary prevention. However, there were few studies regarding old-er adults with disability and physical activity participation. The purpose of this current study was to investigate restriction factors to regularly participate older adults with disability in physical activity by employing keyword network analysis. Two hundred twenty-nine older adults with disability who were over 65 including aging with disability and disability with aging in type of physical disability and brain lesions defined by dis-abled person welfare law partook in the open questionnaire assessing barriers to participate in physical activity. The results showed that the keyword the most often used was ‘Traffic’ which was total of 21 times (3.47%) and the same proportion as in the ‘personal’ and ‘economical’. Exercise was considered the most central keyword for participating in physical activity and keywords such as facility, physical activity, dis-abled, program, transportation, gym, discomfort, opportunity, and leisure activity were associated with exercise. In conclusion, it is necessary to educate older persons with disability about a true meaning of physical activity and providing more physical activity opportunities and decreas-ing inconvenience should be systematically structured in Korea.

본 연구의 목적은 메가 스포츠이벤트 시설의 사후 활용 방안의 성공적인 사례로 평가받는 강원 청소년동계올림픽 대회에 대한 대중의 의견과 생각을 체계적으로 분석하여 주요 관심 이슈를 도출하고, 이에 대한 보완점을 제안하는 것이 다. 또한, 본 연구의 결과는 향후 스포츠 이벤트 개최와 관련된 이해관계자들에게 필요한 정보를 제공하고, 관련 분야에 중요한 시사점을 제시할 것이다.　연구목적을 달성을 위하여 빅데이터 프로그램 TextoM을 활용하여 대회기간 14일 포함 총 42일(2024. 1. 5 – 2. 15)로 설정하여 인터넷 포털사이트(네이버, 다음, 구글에서 키워드를 수집하였다. 연구결과를 얻기 위하여 키워드 정제작업과 텍스트마이닝, 감성분석, 의미결망분석, CONCOR분석을 실시하였다. 본 연구결과 긍정 적인 감성이 높게 나타났으며, 4가지 군집(강원 동계청소년올림픽 대회 정보, 지역 노출, 경기 및 선수 이슈, 무료관람 이슈)이 나타났다. 종합해 보면 올림픽 개최로 건립된 시설의 유지보수, 사후 활용 방안에 대한 문제는 끊임없이 제기되고 있었다. 그러나 강원 동계청소년올림픽대회는 기존의 건립된 시설을 활용한 아시아 최초의 성공적인 동계청소년올림픽 대회로 인식되고 있으며 순위와 메달 보다는 청소년의 교육과 문화교류라는 대회의 추진목표에 맞게 사회적 담론이 형성 되었다.

The purpose of this study is to systematize public thoughts and opinions regarding the Gangwon Youth Winter Olympics, which is recognized as a good example of post-use strategies for mega sports event facilities, in order to identify key interest issues and propose improvements. Using the big data program TextoM, keywords were collected from internet portal sites (Naver, Daum, and Google) over a total of 42 days, including the 14-day event period (from January 5 to February 15, 2024).The results of this study revealed that the keywords most frequently associated with the Gangwon Youth Winter Olympics were host region, success, events, and athletes. Additionally, there was a high level of positive sentiment. Four main clusters emerged: information about the Gangwon Youth Winter Olympics, regional exposure, issues related to competitions and athletes, and issues regarding free admission. In summary, the issues regarding the maintenance and post-utilization of facilities constructed for hosting the Olympics have been continuously raised. However, the Winter Youth Olympic Games in Gangwon is recognized as Asia's first successful Winter Youth Olympic Games by utilizing the pre-existing facilities. Rather than focusing on rankings and medals, the social discourse was formed in alignment with the event's goals of promoting youth education and cultural exchange.

본 연구는 2022 개정 교육과정 개발시 기초 자료로 활용할 목적을 갖고 개발된 인공지능교육 보조 교재 5종을 대상으로 주제어 빈도분석, 동시출현 빈도분석, 네트워크 중심성 분석, 수직적 연계성 분석을 넷마이너 프로그램을 활용하여 분석하였다. 학교급 별 주제어 빈도분석 통해 공통으로 출현빈도가 높은 인공지능, 데이터, 인간, 활용 주제어를 제외하고는 학교급별로 중요하게 다루는 주제어에 차이가 있는 것을 확인할 수 있었다. 또한 네트워크 중심성 분석을 통해 연결정도 중심성, 매개 중심성, 근접 중심성이 높게 나타난 핵심 주제어를 중심으로 내용을 집중적으로 다루고 있음을 확인할 수 있었다. 초-중-고 학교급별 주제어 의 연계성을 보면 ‘인공지능의 이해’, ‘인공지능의 원리와 활용’, ‘ 인공지능의 사회적 영향’ 영역 모두 주제어의 양적 확대를 확 인할 수 있었으며 특히 ‘인공지능의 원리와 활용’ 영역에서는 ‘탐색’ 주제어의 질적 심화를 확인할 수 있었다. 인공지능시대를 살 아갈 학생들에게 필요한 역량을 키워주기 위한 교재를 내용분석하는 것은 앞으로 인공지능교육 교재개발에 관한 후속 연구를 위한 기초자료로 예상한다.

In this study, we analyzed the subject word frequency analysis, co-occurrence frequency analysis, network centrality analysis, and vertical linkage analysis were conducted using the Netminer program for 5 types of artificial intelligence education supplementary materials developed with the purpose of using them as basic data in the development of the 2022 revised curriculum. Through the frequency analysis of keywords for each school level, it was confirmed that there were differences in the keywords that were important for each school level, except for AI, data, human, and used keywords, which have high frequency of appearance in common. In addition, through the network centrality analysis, it was confirmed that the content was intensively dealt with by focusing on key keywords that showed high degree of connection centrality, mediation centrality, and proximity centrality. Looking at the linkage of the keywords for each elementary, middle, and high school level, it was possible to confirm the quantitative expansion of the keywords in 'Understanding Artificial Intelligence', 'Principles and Use of Artificial Intelligence', and 'Social Impact of Artificial Intelligence'. In the 'principle and application' area, it was possible to confirm the qualitative deepening of the 'search' keyword. It is expected that the content analysis of textbooks to nurture the necessary competencies for students who will live in the age of artificial intelligence will serve as basic data for subsequent research on the development of artificial intelligence education textbooks.