• The Journal of Economics, Marketing and Management
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• v.11 no.6
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• pp.1-8
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• 2023

Purpose: In the era of the Fourth Industrial Revolution, individuals equipped with fundamental understanding and practical skills in artificial intelligence (AI) are essential. This study aimed to validate the effectiveness of AI education for enhancing AI literacy among business major student. Research design, data and methodology: Data for analyzing the effectiveness of the AI Fundamental Education Program for business major students were collected through surveys conducted at the beginning and end of the semester. Structural equation modeling was employed to perform basic statistical analyses regarding gender, grade, and prior software (SW) education duration. To validate the effectiveness of AI education, seven variables - AI interest, AI perception, data analysis/utilization, AI projects, AI literacy, AI self-efficacy, and AI learning persistence - were defined and derived. Results: All seven operationally defined variables showed statistically significant positive changes. The average differences were observed as follows: 0.47 for AI interest, 0.32 for AI perception, 0.37 for data analysis/utilization, 0.27 for AI projects, 0.25 for AI literacy, 0.39 for AI self-efficacy, and 0.41 for AI learning persistence. Statistically, AI interest exhibited the most substantial average difference. Conclusions: Through this study, the applied AI education was confirmed to enhance learners' overall competencies in AI, proving its utility and effectiveness in AI literacy education for business major students. Future research endeavors should build upon these results, focusing on ongoing studies related to AI education programs tailored to learners from diverse academic backgrounds and conducting continuous efficacy evaluations.

• Journal of The Korean Association of Information Education
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• v.24 no.5
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• pp.463-471
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• 2020

Humanity faces the fourth industrial revolution, a time of technological revolution by the collaboration of various industries including the fields of artificial intelligence(AI) and big data. Many countries are focused on fostering AI talent to prevail in the coming technological revolution. While Korea also provides some strategies to enhance the cultivation of AI talent, it is still difficult for Korean undergraduate students to get involved in AI studies. Through on the implementation of 'Big data analysis based AI education', which allows an easier approach to AI education, this paper examined the changes in the attitudes of undergraduate students regarding general AI education. 'Big data analysis based AI education' was provided at undergraduate level for 5.5 weeks (15 hours). The attitudes of undergraduate students were analyzed by pre-postmortem. The results showed there was a significant improvement in confidence and self-directed in regard to receiving AI education. With these results, further active research to develop basic AI education that also increases confidence and self-initiative can be expected.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.201-207
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• 2021

As artificial intelligence gradually expands into jobs, iIt is necessary to nurture talents with AI literacy capabilities required for non-majors. Therefore, in this study, based on the necessity and current status of AI education, AI literacy competency improvement education was conducted for non-majors so that AI learning could be sustainable in relation to future majors. For non-majors at University D, problem-solving solutions through project-based data analysis and visualization were applied over 15 weeks, and the AI ability improvement and effectiveness of learners before and after education were analyzed and verified. As a result, it was possible to confirm a statistically significant level of positive change in the learners' data analysis and utilization ability, AI literacy ability, and AI self-efficacy. In particular, it not only improved the learners' ability to directly utilize public data to analyze and visualize it, but also improved their self-efficacy to solve problems by linking this with the use of AI.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.45-52
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• 2023

With the advent of deep learning, Artificial Intelligence (AI) technology has experienced rapid advancements, extending its application across various industrial sectors. However, the focus has shifted from the independent use of AI technology to its dispersion and proliferation through the open AI ecosystem. This shift signifies the transition from a phase of research and development to an era where AI technology is becoming widely accessible to the general public. However, as this dispersion continues, there is an increasing demand for the verification of outcomes derived from AI technologies. Causal AI applies the traditional concept of causal inference to AI, allowing not only the analysis of data correlations but also the derivation of the causes of the results, thereby obtaining the optimal output values. Causal AI technology addresses these limitations by applying the theory of causal inference to machine learning and deep learning to derive the basis of the analysis results. This paper analyzes recent cases of causal AI technology and presents the major tasks and directions of causal AI, extracting patterns between data using the correlation between them and presenting the results of the analysis.

• Journal of Integrative Natural Science
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• v.14 no.4
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• pp.176-182
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• 2021

We live in the age of artificial intelligence, and big data and artificial intelligence education are no longer just for majors, but are required to be able to handle non-majors as well. Software and artificial intelligence education for non-majors is not just a general education, it creates talents who can understand and utilize them, and the quality of education is increasingly important. Through such education, we can nurture creative talents who can create and use new values by fusion with various fields of computing technology. Since 2015, many universities have been implementing software-oriented colleges and AI-oriented colleges to foster software-oriented human resources. However, it is not easy to provide AI basic statistics education of big data analysis deception to non-majors. Therefore, we would like to present a big data education model for non-majors in big data analysis so that big data analysis can be directly applied.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.212-218
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• 2022

In this paper, the necessity of AI video interview arises when conducting an interview for acquisition of excellent talent in a non-face-to-face situation due to similar situations such as Covid-19. As a matter to be supplemented in general AI interviews, it is difficult to evaluate the reliability and qualitative factors. In addition, the AI interview is conducted not in a two-way Q&A, rather in a one-sided Q&A process. This paper intends to fuse the advantages of existing AI interviews and video interviews. When conducting an interview using AI image analysis technology, it supplements subjective information that evaluates interview management and provides quantitative analysis data and HR expert data. In this paper, image-based multi-modal AI image analysis technology, bioanalysis-based HR analysis technology, and web RTC-based P2P image communication technology are applied. The goal of applying this technology is to propose a method in which biological analysis results (gaze, posture, voice, gesture, landmark) and HR information (opinions or features based on user propensity) can be processed on a single screen to select the right person for the hire.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.378-384
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• 2023

In this study, we explore social awareness, interest, and acceptance of generative AI, including chatGPT, which has revolutionized web search, 30 years after web search was released. For this purpose, we performed a machine learning-based topic modeling analysis based on Korean news big data collected from November 30, 2022, when chatGPT was released, to August 31, 2023. As a result of our research, we have identified seven topics related to chatGPT and generative AI; (1)growth of the high-performance hardware market, (2)service contents using generative AI, (3)technology development competition, (4)human resource development, (5)instructions for use, (6)revitalizing the domestic ecosystem, (7)expectations and concerns. We also explored monthly frequency changes in topics to explore social interest related to chatGPT and Generative AI. Based on our exploration results, we discussed the high social interest and issues regarding generative AI. We expect that the results of this study can be used as a precursor to research that analyzes and predicts the diffusion of innovation in generative AI.

• 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.

• Education of Primary School Mathematics
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• v.26 no.4
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• pp.333-348
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• 2023

Clear analysis and diagnosis of various characteristic factors of individual students is the most important in order to realize individual customized teaching and learning, which is considered the most essential function of math artificial intelligence-based digital textbooks. In this study, analysis factors and tools for individual customized learning diagnosis and construction models for data collection and analysis were derived from mathematical AI digital textbooks. To this end, according to the Ministry of Education's recent plan to apply AI digital textbooks, the demand for AI digital textbooks in mathematics, personalized learning and prior research on data for it, and factors for learner analysis in mathematics digital platforms were reviewed. As a result of the study, the researcher summarized the factors for learning analysis as factors for learning readiness, process and performance, achievement, weakness, and propensity analysis as factors for learning duration, problem solving time, concentration, math learning habits, and emotional analysis as factors for confidence, interest, anxiety, learning motivation, value perception, and attitude analysis as factors for learning analysis. In addition, the researcher proposed noon data on the problem, learning progress rate, screen recording data on student activities, event data, eye tracking device, and self-response questionnaires as data collection tools for these factors. Finally, a data collection model was proposed that time-series these factors before, during, and after learning.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.317-325
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• 2021

In this paper, the degree of reflection of SW·AI elements and CT elements was investigated and analyzed for a total of 44 textbooks of Korean, social, moral, mathematics and science textbooks based on the 2015 revised curriculum. As a result of the analysis, most of the activities of data collection, data analysis, and data presentation, which are ICT elements, were not reflected, and algorithm and programming elements were not reflected among SW·AI content elements, and there were no abstraction, automation, and generalization elements among CT elements. Therefore, in order to effectively implement SW·AI convergence education in elementary school subjects, we will expand ICT utilization activities to SW·AI utilization activities. Training on the understanding of SW·AI convergence education and improvement of teaching and learning methods using SW·AI is needed for teachers. In addition, it is necessary to establish an information curriculum and secure separate class hours for substantial SW·AI education.