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This study is based on the necessity of a new recognition of multicultural families in the context of cultural coexistence and integration in the era of artificial intelligence, as well as responding to the challenges of low birth rates and an aging society. The aim is to analyze the issues of perception toward multicultural families and the current state of education, and to suggest the need for policy reform. First, this study empirically analyzes the school life, psychological and emotional traits, and socio-cultural traits of multicultural youth, comparing them with the mainstream youth in multicultural dense areas. Second, based on the research findings, the study emphasizes the need for changes in the education system to address biases and discrimination against multicultural youth, especially in preparation for the era of artificial intelligence. Third, the study calls for the development of educational methods that allow students to experience and understand various cultures through cultural experiences using virtual reality(VR) or augmented reality(AR), and interactive cultural education content. Finally, the study suggests that the development of AI-based multicultural education content could serve as a new alternative for securing labor force in the era of low birth rates.

This study examines how AI tools can be integrated into college education to promote active and efficient learning for students in a Quantitative Business Analysis course. The main focus of the course is to teach statistics and data processing with R to a class of 50 first-year International Business Administration students. With the variety of topics and assignments, it is a challenge to provide personalized feedback for a class of this size. To resolve these issues, AI tools are used along with traditional lab sessions and supplementary video lectures. A mid-semester survey was conducted to evaluate students' experiences with this new methodology. Its effectiveness in improving their skills and comprehension, and their preferences of future AI integration were examined to determine the optimal level of AI integration to improve outcomes in the course. The survey shows that students evaluate AI and video lectures as highly effective in learning R coding and completing assignments. However, many still prefer to retain in-person interactions such as lab sessions. We need to find an optimized mixture that combines traditional teaching methods with AI tools to improve students’ satisfaction and their learning outcomes. It is worth noting students without prior coding experience showed almost the same responses regarding AI-assisted course with students with prior experience. This proves AI-integrated method can satisfy both groups. Only small differences between two groups were observed in students’ confidence and their support for further AI integration. This issue can be resolved with additional help for no experience group such as orientation sessions at the beginning of the semester. The originality of this study lies in its empirical evaluation of AI as a new educational tool that can make personalized learning possible. This new method allows students, even in large classes, to progress at their own pace and skill level. This research can contribute to finding a new educational framework adaptable to diverse learning contexts.

The purpose of this study is to explore the elderly care policies and ICT utilization of Japan and Sweden, which entered the super-aged society earlier than Korea, and to suggest ways to promote the use of ICT in elderly care in Korea. The literature research method was conducted by referring to literature such as research reports, theses, and policies on changes in each country's elderly care policies and the use of ICT, as well as statistical data from the relevant countries and Internet articles. The research results show that, first, each country is facing government financial burdens and a shortage of care providers due to the super-aging, and they are trying to realize the will of the elderly who wish to continue living in their local communities. Second, South Korea provides services such as safety support and daily life support in the customized care service for the elderly before they enter long-term care services. Third, South Korea is using ICT-based equipment in emergency safety and security services and home health care services for the elderly living alone, but there is no sharing of information, so the personnel and technology are being operated in a redundant and fragmented state. Fourth, Korea's ICT utilization in elderly care policies and care robots is repeated through small-scale pilot projects. The policy recommendations of this study are to establish an integrated system for the elderly customized care service, emergency safety and security service for the elderly living alone, health center visit health management project, and long-term care service project. This should alleviate the shortage of care workers and financial burdens and prepare for an ultra-aged society.

The implementation of generative AI into the film industry marks a critical juncture in the evolution of cinema, providing opportunities to redefine storytelling, visual aesthetics, and audience experiences. This study explores the emergence of AI film festivals, which serve as platforms for technological and artistic innovation, by analyzing their organizational structures, operational frameworks, and creative outputs. Employing a data-driven approach using web crawling and quantitative analysis, this research examines how AI film festivals redefine the traditional paradigms in filmmaking while addressing ethical, legal, and industrial challenges. AI film festivals are characterized by their unique ability to democratize the filmmaking process, leveraging cost-effective AI tools that reduce barriers for creators from diverse cultural and economic backgrounds. These festivals not only enable innovative narrative forms and visual effects but also promote global cultural exchange through online streaming platforms and immersive technologies. Case studies, including Runway AI Film Festival and Korea’s AI International Film Festival, demonstrate how AI-driven storytelling methodologies contribute to expanding genre boundaries and fostering convergence between technology and art. For instance, AI film festivals utilize virtual reality and real-time audience feedback mechanisms to create interactive, personalized cinematic experiences that were previously unattainable. Moreover, AI film festivals address critical issues such as copyright, intellectual property, and sustainability. By optimizing production efficiency and minimizing environmental impacts, these festivals set new benchmarks for responsible filmmaking. They also facilitate discussions on the societal implications of AI in creative industries, promoting ethical frameworks for integrating AI technologies into cinema. These events highlight the potential of generative AI to act as both a creative collaborator and a technical enabler, fundamentally reshaping the relationship between creators and audiences. The findings of this study reveal that AI film festivals are not merely extensions of traditional cinema but independent platforms with distinct aesthetics and operational models. They complement conventional film festivals by introducing new paradigms of storytelling and production while offering solutions to challenges faced by the film industry. As AI film festivals continue to grow, their role as transformative agents in the cinematic landscape becomes increasingly evident, blending the creative possibilities of art with the efficiency of technology. This research concludes that AI film festivals represent a sustainable and inclusive future for cinema, where innovation and tradition coexist to redefine the boundaries of creative expression.

To investigate how training data bias impacts recommendation quality, this study experimented with simulated data to observe changes in recommendation performance and result bias across varying levels of data bias. The experiment revealed that while imbalances in popularity and preference distributions within the training data did not significantly affect recommendation accuracy, they did strongly influence recommendation result bias, leading to amplification effects. Specifically, increased preference distribution imbalance intensified recommendation result bias, whereas a certain level of popularity imbalance had no effect on recommendation result bias nor caused amplification. These findings challenge previous research suggesting that data bias degrades recommendation accuracy but support claims that data bias amplifies recommendation result bias. This study is notable for providing insights into how data bias mechanisms impact recommendation quality, offering valuable guidance for future research focused on bias mitigation.