• The Journal of Industrial Distribution & Business
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• v.12 no.8
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• pp.21-32
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• 2021

Purpose: With the advent of the era of the 4th industrial revolution, called a new technological revolution, the necessity of fostering future talents equipped with AI utilization capabilities is emerging. However, there is a lack of research on AI education design and competency-based education curriculum as education for business major. The purpose of this study is to design AI education to cultivate competency-oriented AI literacy for business major in universities. Research design, data and methodology: For the design of AI basic education in business major, three expert Delphi surveys were conducted, and a demand analysis and specialization strategy were established, and the reliability of the derived design contents was verified by reflecting the results. Results: As a result, the main competencies for cultivating AI literacy were data literacy, AI understanding and utilization, and the main detailed areas derived from this were data structure understanding and processing, visualization, web scraping, web crawling, public data utilization, and concept of machine learning and application. Conclusions: The educational design content derived through this study is expected to help establish the direction of competency-centered AI education in the future and increase the necessity and value of AI education by utilizing it based on the major field.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.59-67
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• 2021

The development of AI technology has brought about a big change in our lives. As AI's influence grows from life to society to the economy, the importance of education on AI and data is also growing. In particular, the OECD Education Research Report and various domestic information and curriculum studies address data literacy and present it as an essential competency. Looking at domestic and international studies, one can see that the definition of data literacy differs in its specific content and scope from researchers to researchers. Thus, the definition of major research related to data literacy was analyzed from various angles and derived from various angles. In key studies, Word2vec natural language processing methods, along with word frequency analysis used to define data literacy, are used to analyze semantic similarities and nominate them based on content elements of curriculum research to derive the definition of 'understanding and using data to process information'. Based on the definition of data literacy derived from this study, we hope that the contents will be revised and supplemented, and more research will be conducted to provide a good foundation for educational research that develops students' future capabilities.

• ETRI Journal
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• v.43 no.5
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• pp.812-824
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• 2021

Recently, with an increase in Internet usage, users of online social networks (OSNs) have increased. Consequently, privacy leakage has become more serious. However, few studies have investigated the difference between privacy and actual behaviors. In particular, users' desire to change their privacy status is not supported by their privacy literacy. Presenting an accurate measurement of users' privacy status can cultivate the privacy literacy of users. However, the highly interactive nature of interpersonal communication on OSNs has promoted privacy to be viewed as a communal issue. As a large number of redundant users on social networks are unrelated to the user's privacy, existing algorithms are no longer applicable. To solve this problem, we propose a structural similarity measurement method suitable for the characteristics of social networks. The proposed method excludes redundant users and combines the attribute information to measure the privacy status of users. Using this approach, users can intuitively recognize their privacy status on OSNs. Experiments using real data show that our method can effectively and accurately help users improve their privacy disclosures.

• Journal of Engineering Education Research
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• v.25 no.6
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• pp.23-37
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• 2022

This study evaluated the engineering students' ethical sensitivity to an AI emotion recognition robot scenario and explored its characteristics. For data collection, 54 students (27 majoring in Convergence Electronic Engineering and 27 majoring in Computer Software) were asked to list five factors regarding the AI robot scenario. For the analysis of ethical sensitivity, it was checked whether the students acknowledged the AI ethical principles in the AI robot scenario, such as safety, controllability, fairness, accountability, and transparency. We also categorized students' levels as either informed or naive based on whether or not they infer specific situations and diverse outcomes and feel a responsibility to take action as engineers. As a result, 40.0% of students' responses contained the AI ethical principles. These include safety 57.1%, controllability 10.7%, fairness 20.5%, accountability 11.6%, and transparency 0.0%. More students demonstrated ethical sensitivity at a naive level (76.8%) rather than at the informed level (23.2%). This study has implications for presenting an ethical sensitivity evaluation tool that can be utilized professionally in educational fields and applying it to engineering students to illustrate specific cases with varying levels of ethical sensitivity.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.67-73
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• 2023

The Digital Age calls for improvement of information literacy particularly among children and youth who are vulnerable to cybergrooming. Taking an interdisciplinary approach by leveraging our team's expertise including child and adolescent development, data analytics, and cybersecurity, this study proposes an interactive artificial intelligence (AI)-based preventive simulation program that raises youth knowledge and awareness about the risk of cybergrooming as well as increases resilient self-efficacy in their cybersecurity-relevant skills. The primary purpose of this project is to evaluate the effectiveness of the simulation program on preventing cybergrooming. More specifically, this study is designed to examine developmental changes in self-efficacy of cybersecurity-relevant skills among youth participants as a function of the preventive simulation program. Further, this study will identify risk and protective factors that explain interindividual differences in the ability of children and youth either to fall victim to advances from a cyber predator or to recognize and deter such threats. The preliminary data will help improve the effectiveness of the preventive simulation program as well as the methods of implementation to large groups of youth. The findings from the proposed study will contribute to making specific recommendations to parents, educators, practitioners, and policy makers for the prevention of cybergrooming.

• Human Ecology Research
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• v.61 no.1
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• pp.15-28
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• 2023

The aim of this study was to develop, implement, and evaluate the use of Artificial Intelligence in the prevention of violence among middle-school students. The sample for this study consisted of 20 first-year middle-school students who participated in theme selection activities in a free semester program as part of their home economics studies. The data for the study consisted of nine class observation logs, four group activity outputs, 30 class results, an online survey, and in-depth interviews with three students. A program called "R.U.OK" was developed by setting problematic situation for school violence prevention linked to the contents of the Home Economics Education(HEE) curriculum. After the program was implemented, the survey on the students' class satisfaction content elements, with AI-based learning activities and PBL and interest, displayed high points, with an average of 4.0 or higher. Our qualitative analysis produced four significant results. First, students' concerns about school violence had increased and they showed a change in attitude, having more empathy with friends and more interest in their surroundings. Second, digital and AI literacy had improved, and students' interest in digital media learning had increased. Third, there had been an improvement in problem-solving ability in terms of being able to think more critically and independently. Fourth, the results also demonstrated that there had been a positive effect on self-direction and an improved capacity for teamwork. This study was significant in demonstrating the effectiveness of a program for the prevention of school violence based on the use of digital technology in the educational environment.

• The Mathematical Education
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• v.62 no.2
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• pp.289-302
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• 2023

In today's digital information society, student knowledge and skills to analyze big data and make informed decisions have become an important goal of school mathematics. Integrating big data statistical projects with digital technologies in high school <Artificial Intelligence> mathematics courses has the potential to provide students with a learning experience of high impact that can develop these essential skills. This paper proposes a set of guidelines for designing effective big data statistical project-based tasks and evaluates the tasks in the artificial intelligence mathematics textbook against these criteria. The proposed guidelines recommend that projects should: (1) align knowledge and skills with the national school mathematics curriculum; (2) use preprocessed massive datasets; (3) employ data scientists' problem-solving methods; (4) encourage decision-making; (5) leverage technological tools; and (6) promote collaborative learning. The findings indicate that few textbooks fully align with these guidelines, with most failing to incorporate elements corresponding to Guideline 2 in their project tasks. In addition, most tasks in the textbooks overlook or omit data preprocessing, either by using smaller datasets or by using big data without any form of preprocessing. This can potentially result in misconceptions among students regarding the nature of big data. Furthermore, this paper discusses the relevant mathematical knowledge and skills necessary for artificial intelligence, as well as the potential benefits and pedagogical considerations associated with integrating technology into big data tasks. This research sheds light on teaching mathematical concepts with machine learning algorithms and the effective use of technology tools in big data education.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.295-301
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• 2021

This study tried to examine the necessity of AI literacy education to increase the effectiveness of artificial intelligence education ahead of the revision of the next revised curriculum. To this end, AI modeling classes were conducted for high school students and the necessity, content, and training period of AI literacy perceived by students in AI education were investigated through a questionnaire. The results showed that they generally agreed on the need for data utilization and data preprocessing in the AI class, and in the course of the AI class, there were many cases of difficulties due to lack of basic competencies for database use. In particular, it was observed that the understanding of the file structure for data analysis was insufficient and the understanding of the data storage format for data analysis was low. In order to overcome this part, the necessity of prior education for data processing was recognized, and there were many opinions that it is generally appropriate to go to high school at that time. As for the content elements of AI literacy, it was found that there were high demands on the content of data visualization along with data transformation, including data creation and deletion.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.1-16
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• 2024

The purpose of this study is to develop a machine learning-based AI convergence class model and class design principles that can foster data literacy in high school students, and to develop detailed guidelines accordingly. We developed a machine learning-based teaching model, design principles, and detailed guidelines through research on prior literature, and applied them to 15 students at a specialized high school in Seoul. As a result of the study, students' data literacy improved statistically significantly (p<.001), so we confirmed that the model of this study has a positive effect on improving learners' data literacy, and it is expected that it will lead to related research in the future.

• The Journal of Korean Association of Computer Education
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• v.23 no.2
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• pp.1-11
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• 2020

As software and artificial intelligence education became more and more important, in December 2019, the Ministry of Science and ICT announced plans to expand software and AI education to mandatory education in elementary and secondary schools by 2022. In addition to elementary and secondary schools, most universities are actively engaged in software education for computer non-majors, but research on coding education for computer non-majors is insufficient. The purpose of this paper is to find an efficient teaching and learning method for coding education for computer non-majors. Nowadays, college students, called Millennial and Generation Z, prefer visual information and are familiar with computers as digital natives. Based on these characteristics, this study examined the visual literacy and thinking styles of college students and then examined whether the students' visual literacy and thinking styles influenced coding-based problem solving in coding subjects. Based on this, this paper proposes an alternative to do programming education more efficiently for students who are new to coding.