• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.115-123
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• 2024

The purpose of this study is to contribute to how the advantages of artificial intelligence (AI) services and the associated limitations can be simultaneously overcome, using the keywords AI and risk management. To achieve this, two cases were introduced: (1) presenting a risk monitoring process utilizing AI and (2) introducing an operational toolkit to minimize the emerging limitations in the development and operation of AI services. Through case analysis, the following implications are proposed. First, as AI services deeply influence our lives, the process are needed to minimize the emerging limitations. Second, for effective risk management monitoring using AI, priority should be given to obtaining suitable and reliable data. Third, to overcome the limitations arising in the development and operation of AI services, the application of a risk management process at each stage of the workflow, requiring continuous monitoring, is essential. This study is a research effort on approaches to minimize limitations provided by advancing artificial intelligence (AI). It can contribute to research on risk management in the future growth and development of the related market, examining ways to mitigate limitations posed by evolving AI technologies.

• Journal of Practical Engineering Education
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• v.16 no.4
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• pp.567-575
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• 2024

This study was conducted to provide basic data on how to use Generative AI and education to strengthen Generative AI competency in vocational counseling by confirming the experience and perception of job counselors' use of Generative AI. A questionnaire was produced based on literature research and FGI preliminary surveys, and the main contents of the questionnaire were 'experience in using Generative AI (whether to have experience, type of tool, job, educational experience, etc.) and Generative AI recognition (recognition level, usefulness, availability, educational needs, etc.). An online survey was conducted for vocational counselors, and a total of 293 data were analyzed. As a result of major research, first, there were many counselors who had no experience in using Generative AI(60%), and the response that the reason for not using it was because they did not feel the need(28%). Second, the 'degree of recognition' in the Generative AI was somewhat low (M=2.77), and 'Generative AI usefulness' was found to be at a normal level (M=3.32), and it was recognized that it would be necessary mainly for jobs related to 'vocational information'. Third, 'tool (computer use, etc.) competency' (26%) was the highest as the competency required for future vocational counselors, and 'how to use Generative AI' (57%) accounted for a high proportion of the educational content necessary to improve these competencies.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.305-310
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• 2024

With the emergence of generative AI, a new era of coexistence with humanity has begun. The vast data-driven learning capabilities of AI are being utilized in various industries to achieve a level of productivity distinct from human learning. However, AI also manifests societal phenomena such as technophobia. This study aims to analyze collaborative AI models based on an understanding of AI and identify areas within the jewelry industry where these models can be applied. The utilization of collaborative AI models can lead to the acceleration of idea development, enhancement of design capabilities, increased productivity, and the internalization of multimodal functions. Ultimately, AI should be used as a collaborative tool from a utilitarian perspective, which requires a proactive, human-centric mindset. This research proposes collaborative AI strategies for the jewelry business, hoping to enhance the industry's competitiveness.

• International Journal of Computer Science & Network Security
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• v.24 no.8
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• pp.1-13
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• 2024

Artificial intelligence (AI) describes a variety of approaches in computer applications to mimic human learning. As this technology becomes increasingly prevalent, it is inevitable that it will enter the educational environment, as both an educational tool and topic of learning. STEM education, which deals with science, technology, engineering, and math, is perhaps the most appropriate educational field in which to introduce students to this new and rapidly growing technology. In recent years, educators, AI engineers, and educational researchers have published trial results of experimental curricula implementing AI technology in student and teacher education. This systematic literature review analyzed a sample of seven such publications to identify key trends in suggested best practices for the usage of AI in STEM classrooms. The sample was analyzed for keywords using MaxQDA. The results indicated three key trends among suggested best practices. The first was that AI is best taught to students when the technology itself is the topic of education. Another trend was that simulating real world applications of AI technology was most impactful in showing students the potential, limits, and ethical implications of AI. Finally, it was found that educator's familiarity with AI is an important factor in their ability to employ it in the classroom.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.315-320
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• 2021

In order to prepare for future social changes, SW·AI education is essential. In this paper, after conducting non-face-to-face SW·AI education for pre-service teachers, the effectiveness of SW education before and after education was measured using the measurement tool on the software educational effectiveness. As a result of the analysis, the overall average and the average of the 'computational thinking' and 'SW literacy' domains increased significantly, and the difference between the averages before and after education was statistically significant in decomposition, pattern recognition, abstraction, and algorithm, which are sub domains of 'computational thinking'. Through SW·AI education, students not only recognize the necessity of SW education and the importance of computational thinking, but also understand the process of decomposing information, recognizing and extracting patterns, and expressing problem-solving processes. It can be seen that non-face-to-face SW·AI education has the effect of improving computational thinking and SW literacy beyond recognizing the importance of SW.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.227-239
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• 2023

Online marketing is a rapidly growing industry that heavily depends on digital technologies and data analysis to effectively reach and engage consumers. For that, artificial intelligence (AI) has emerged as a crucial tool for online marketers, enabling marketers to analyze extensive consumer data and automate decision-making processes. The purpose of this study was to investigate the ethical implications of using AI in online marketing, focusing on its impact on consumer privacy and decision-making. AI has created new possibilities for personalized marketing but raises concerns about the collection and use of consumer data, transparency and accountability of decision-making, and the impact on consumer autonomy and privacy. In this study, we reviewed the relevant literature and case studies to assess the potential risks and make recommendations for improving consumer protection. The findings provide insights into ethical considerations and offer a roadmap for balancing the advantages of AI in online marketing with the protection of consumer rights. Companies should consider these ethical issues when implementing AI in their marketing strategies. In this study, we explored the concerns and provided insights into the challenges posed by AI in online marketing, such as the collection and use of consumer data, transparency, and accountability of decision-making, and the impact on consumer autonomy and privacy.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.21-29
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• 2024

Fueled by international efforts towards AI standardization, including those by the European Commission, the United States, and international organizations, this study introduces a AI-driven framework for analyzing advancements in drone technology. Utilizing project data retrieved from the NTIS DB via the "drone" keyword, the framework employs a diverse toolkit of supervised learning methods (Keras MLP, XGboost, LightGBM, and CatBoost) enhanced by BERTopic (natural language analysis tool). This multifaceted approach ensures both comprehensive data quality evaluation and in-depth structural analysis of documents. Furthermore, a 6T-based classification method refines non-applicable data for year-on-year AI analysis, demonstrably improving accuracy as measured by accuracy metric. Utilizing AI's power, including GPT-4, this research unveils year-on-year trends in emerging keywords and employs them to generate detailed summaries, enabling efficient processing of large text datasets and offering an AI analysis system applicable to policy domains. Notably, this study not only advances methodologies aligned with AI Act standards but also lays the groundwork for responsible AI implementation through analysis of government research and development investments.

• Health Policy and Management
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• v.34 no.2
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• pp.103-105
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• 2024

Artificial intelligence (AI) technology is rapidly spreading across various industries. Accordingly, interest in ethical issues arising from the use of AI is also increasing. This is particularly true in the healthcare sector, where AI-related ethical issues are a significant topic due to its focus on health and life. Hence, this issue aims to examine the ethical concerns when using AI tools during research and publication processes. One of the key concerns is the potential for unintended plagiarism when researchers use AI tools for tasks such as translation, citation, and editing. Currently, as AI is not given authorship, the researcher is held accountable for any ethical problems arising from using AI tools. Researchers are advised to specify which AI tools were used and how they were employed in their research papers. As more cases of ethical issues related to AI tools accumulate, it is expected that various guidelines will be developed. Therefore, researchers should stay informed about global consensus and guidelines regarding the use of AI tools in the research and publication process.

• 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.