• Informatization Policy
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• v.31 no.3
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• pp.34-47
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• 2024

Artificial intelligence(AI) is regarded as a key tool that can significantly contribute to innovation and improve productivity as digital transformation continues to spread rapidly. Currently, however, there is lack of understanding and empirical research on the factors that influence the adoption of AI by companies. In particular, most studies have been conducted by foreign researchers analyzing data from foreign companies, and domestic studies have limitations in terms of objectivity and timeliness. This study employs econometric methods to identify the determinants of AI adoption at the firm level. To this end, we derive the technological, organizational, and environmental context factors from the perspective of the Technology-Organization-Environment(TOE) framework as a representative theory of technology adoption factors. We then conduct a logistic regression analysis using data from 11,601 Korean firms. This study not only expands the research literature by supplementing the limitations of previous studies in Korea but also provides timely evidence and implications through empirical analysis.

• The Journal of Bigdata
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• v.2 no.2
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• pp.9-16
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• 2017

Global Platform Solution Company (aka Amazon, Google, MS, IBM) who has cloud platform, are driving AI and Big Data service on their cloud platform. It will dramatically change Enterprise business value chain and infrastructures in Supply Chain Management, Enterprise Resource Planning in Customer relationship Management. Enterprise are focusing the channel with customers and Business Partners and also changing their infrastructures to platform by integrating data. It will be Digital Transformation for decision support. AI and Deep learning technology are rapidly combined to their data driven platform, which supports mobile, social and big data. The collaboration of platform service with business partner and the customer will generate new ecosystem market and it will be the new way of enterprise revolution as a part of the 4th industrial revolution.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.41-50
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• 2023

Recently, as the Coronavirus disease 2019 (COVID-19) prolongs along with the development of artificial intelligence technology, a non-contact society has become commonplace. Many companies are promoting digital transformation and the activation of artificial intelligence introduction to respond to this which leads to dramatic increase of demand for Chatbot. In addition, a Chatbot has reached the point of processing business transactions from responding simple inquiries. However, it is necessary to develop an API to interface with the legacy system and there are many difficulties in connecting. To solve this, it is becoming important to establish a hybrid Chatbot environment through RPA interface. Recently, the combination of RPA and Chatbot is considered an effective tool for handling many business processes. But, there are many difficulties due to the lack of interface cases and the difficulty in finding a method to development them. This study suggests a method for building a hybrid Chatbot which is an interface Chatbot(Conversational UX) and RPA(Task Automation) from the perspective of hyper-automation based on actual development cases and review of literature review is presented, so that the interface method can be understood and develop more easily. Therefore, there are implications for actively using AI Chatbot for digital transformation.

• Journal of Information Technology Applications and Management
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• v.28 no.3
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• pp.109-125
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• 2021

For the government of president Moon's AI SW HRD policy, he proclaimed AI democracy that anyone can utilize artificial intelligence technology to spread AI education for the people of the country. Through cognitive map analysis, this study presents expected policy outcomes due to the input of policy factors to overcome crisis factors and utilize opportunity factors. According to the cognitive guidance analysis, first, the opportunity factor is recognized as accelerating the digital transformation to Covid 19 if AI SW HRD is well nurtured. Second, the crisis factor refers to the rapid paradigm shift caused by the intelligence information society, resulting in job losses in the manufacturing sector and deepening imbalance in manpower supply and demand, especially in the artificial intelligence sector. Third, the comprehensive cognitive map shows a circular process for creating an AI SW ecosystem in response to threats caused by untact caused by Corona and a circular process for securing AI talent in response to threats caused by deepening imbalance in manpower supply and demand in the AI sector. Fourth, in order to accelerate the digital circulation that has been accelerated by Corona, we found a circular process to succeed in the Korean version of digital new deal by strengthening national and corporate competitiveness through AI-utilized capacity and industrial and regional AI education. Finally, the AI utilization empowerment strengthening rotation process is the most dominant of the four mechanisms, and we also found a relatively controllable feedback loop to obtain policy outputs.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.11-23
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• 2022

Although artificial intelligence(AI) is a key technology in the digital transformation among the emerging technologies, there are concerns about the use of AI, so many countries have been trying to set up a proper regulation system. This study analyzes the cases of the regulation policies on AI in USA, EU and Korea with the aim to set up and improve proper AI policies and strategies in Korea. In USA, the establishment of the code of ethics for the use of AI is led by private sector. On the other side, Europe is strengthening competitiveness in the AI industry by consolidating regulations that are dispersed by EU members. Korea has also prepared and promoted policies for AI ethics, copyright and privacy protection at the national level and trying to change to a negative regulation system and improve regulations to close the gap between the leading countries and Korea in AI. Moreover, this study analyzed the course of policy changes of AI regulation policy centered on ACF(Advocacy Coalition Framework) model of Sabatier. Through this study, it proposes hyper-scale AI regulation policy recommendations for improving competitiveness and commercialization in Korea. This study is significant in that it can contribute to increasing the predictability of policy makers who have difficulties due to uncertainty and ambiguity in establishing regulatory policies caused by the emergence of hyper-scale artificial intelligence.

• Journal of Information Technology Applications and Management
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• v.31 no.5
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• pp.1-16
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• 2024

Generative AI technology, which represents the artificial intelligence era, is developing faster than any other field in image production and is being applied to practice. The purpose of this study is to explore various ways to use Generative AI by deriving the characteristics and types of Generative AI use cases of book cover design among visual design fields. The research method investigated the concept of Generative AI and the current status of use in the field of visual design through literature. Based on this, the book cover design cases were analyzed using the Generative AI currently published, and design characteristics and types were derived. As a result of the study, Generative AI book cover design could be divided into two characteristics: 'Indirect Expression' and 'Direct Expression' and three types: 'Image Transfer', 'Image Transformation', and 'Image Expansion'. Future uses of generative AI in book cover design will need to understand the format of the book, be able to describe a lot of knowledge and experience for prompting, and generate a lot of images for inspiration. Numerous Generative AI tools exist today. In the future, it should be the basis for designers to effectively handle Generative AI by analyzing commercial outcomes created by Generative AI in various design fields to create creative outcomes that can be used in practice beyond the use of tools.

• Journal of Information Technology Services
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• v.20 no.5
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• pp.43-54
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• 2021

Currently, citizens' expectations and concerns on utilizing artificial intelligence (AI) technologies in the public sector are widening with the rapid digital transformation. Furthermore the level of global acceptance on the AI and other intelligent digital technologies is augmenting with the needs of non-face-to-face types of public services more than ever due to the unforeseen and unpredictable pandemic, COVID-19. Thus, this study intended to empirically examine what policy directions for the public should be considered to provide well-designed services as well as to promote the evidence-based public policies in terms of Al speaker technology as a non-contact smart city service. Based on the survey of senior citizens' perceptions on AI (AI Speaker technology), this study conducted structure equation modeling analyses to identify whether technology acceptance models on to the varied dependent variables such as actual use, perception, attitude, and brand royalty. The Results of the empirical analyses showed that AI increased the positive level of citizens' perception, attitude and brand royalty on non-contact public services (smart city services) which are becoming more crucial for developing AI oriented government and providing intelligent public services effectively. In addition, theoretical and practical implications are discussed for understanding the changes of public service in the post-corona.

• The Mathematical Education
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• v.63 no.2
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• pp.233-254
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• 2024

This paper delves into the symbiotic integration of coding and mathematics education, aimed at cultivating computational thinking and enriching mathematical problem-solving proficiencies. We have identified a corpus of scholarly articles (n=38) disseminated within the preceding two decades, subsequently culling a portion thereof, ultimately engendering a contemplative analysis of the extant remnants. In a swiftly evolving society driven by the Fourth Industrial Revolution and the ascendancy of Artificial Intelligence (AI), understanding the synergy between these domains has become paramount. Mathematics education stands at the crossroads of this transformation, witnessing a profound influence of AI. This paper explores the evolving landscape of mathematical cognition propelled by AI, accentuating how AI empowers advanced analytical and problem-solving capabilities, particularly in the realm of big data-driven scenarios. Given this shifting paradigm, it becomes imperative to investigate and assess AI's impact on mathematics education, a pivotal endeavor in forging an education system aligned with the future. The symbiosis of AI and human cognition doesn't merely amplify AI-centric thinking but also fosters personalized cognitive processes by facilitating interaction with AI and encouraging critical contemplation of AI's algorithmic underpinnings. This necessitates a broader conception of educational tools, encompassing AI as a catalyst for mathematical cognition, transcending conventional linguistic and symbolic instruments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.37-45
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• 2024

This study aims to collect and systematize indicators for each stage of digital transformation at the infrastructure level to accurately diagnose the current status of digital transformation in Korea and to serve as a reference for establishing a balanced digital strategy. In order to establish a framework for digital transformation of infrastructure, 19 indicators in three categories(tangible/intangible, data) were identified across three stages of digital transformation: computerization, digitization, and digital transformation, and 19 indicators in three categories were identified to study the changes in digital infrastructure. The main findings are: First, the digital transformation of infrastructure is at a high level, moving from digitization to digital transformation. Second, the scope of digital transformation policies is expanding as digital transformation is triggered, and additional policies on inclusion and social disparities should be prepared. It is also important to improve the regulatory environment, which is relatively undervalued. Third, as data becomes more important, it is important to develop indicators and measurements to strengthen digital competitiveness in terms of data infrastructure. This study is an exploratory study of the existing indicators, which can be used to conduct specialized research on the differences in the level of digital transformation by industry, sector, company size, age, gender, region, and group, and to study indicators for the expansion of digital transformation to social and industrial sectors. The expected effect is to deepen the process of understanding the interaction between each indicator, so that future digital transformation policies can be organized and promoted, and policy outcomes can be predicted and responded to in advance.

• Journal of Information Technology Services
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• v.20 no.1
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• pp.93-115
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• 2021

Various policy declarations and institutional experiments involving artificial intelligence are being made in most countries. Depending on how the artificial intelligence policy changes, the role of the government, the scope of the policy, and the policy means used may vary, which can lead to the success or failure of the policy. This study proposed a perspective on AI(Artificial Intelligence) in policy research, investigated the theory of path change, and derived the characteristics of path change in AI policy. Since AI policy is related to a wide range of policy areas and the policy making is at the start points, this study is based on the neo-institutional path theory about the types of institutional changes. As a result of this study, AI policy showed the characteristics of path creation, and in detail presented the conflict relationship between institutional design elements, the scalability of policy areas, policy stratification and policy mix, the top policy characteristics transcending the law, and the experiment for regulatory innovation. Since AI can also be used as a key tool for policy innovation in the future, research on the path and characteristics of AI policy will provide a new direction and approach to government policy or institutional innovation seeking digital transformation.