본 연구는 스타 발명가의 지식 스필오버가 다국적기업 내에서 민족적 경계에 따라 어떻게 구조화 되는지를 분석한다. 미국 특허 데이터를 활용하여 약 660만 발명가-특허 관측치를 구축하고, 스타 밀도를 동일 민족과 이민족으로 분해하여 분석하였다. 통합 표본에서 두 채널 모두 동료 발명가의 혁신 성과를 유의하게 개선하며, 이민족 효과가 동일 민족 효과보다 유의하게 크나(Wald p = 0.006), 이 집계 패턴은 체계적 이질성을 은폐한다: 동일 민족 채널링은 소수 민족 그룹과 동질적 혁신 허브에서 두드러지는 반면, 이질적 허브에서는 두 효과가 균형을 이룬다. 기업 내 분해 분석 과 인용 흐름 분석은 구성적 선별을 넘어선 직접적 지식 스필오버성분의 존재와 정합적인 증거를 제공한다. 이러한 결과는 다양한 고정효과 구조 및 강건성 검증에 안정적이다. 본 연구는 다국적기 업의 인재 배치와 다양성 관리가 허브별 사회적 맥락을 고려해야 하며, 민족적 근접성이 비공식 지 식 채널의 접근성을 구조화하는 중요한 요인임을 시사한다.

This study investigated the visualization accuracy and educational applicability of generative artificial intelligence (AI) tools in fashion design education by comparing images generated from the same blouse sketch using GPT-based tools, LOOK AI, and Stable Diffusion under identical prompt conditions. Thirty-two professional fashion designers evaluated the generated outputs using a structured 10-item assessment scale, focusing on silhouette accuracy, detail representation, structural clarity, and overall visual completeness. Statistical differences among the tools were analyzed using one-way analysis of variance followed by post-hoc comparisons. The results revealed significant differences (p<.05) in key evaluation criteria: silhouette accuracy, detail implementation, structural interpretability, and overall completeness. LOOK AI excelled in representing structural elements such as seams, pleats, and pattern logic, indicating its strength in design-oriented applications and technical visualization tasks. In contrast, Stable Diffusion received higher ratings for overall visual balance and aesthetic coherence, despite showing relatively lower structural fidelity. GPT-based outputs received lower ratings for structural accuracy but were seen as valuable for promoting critical AI literacy via prompt-based exploration, iterative refinement, and reflective evaluation. These findings suggest that differences among AI tools should not be interpreted in terms of absolute superiority but as distinct educational affordances. Accordingly, this study proposes a three-axis instructional framework that integrates structure-oriented learning, creative visualization, and critical inquiry-based learning.

Various kinds of clothing characterized the fashion style during the Joseon Dynasty period. Cheollik, the oldest among traditional Korean costumes, was worn for diverse purposes by various classes and has immense historical value for its unique structure and form. This study reveals the aesthetic aspects of cheollik from the Joseon period, which upheld the spirit of consideration. It looks into the possibility of cheollik’s re-creation in light of the social/cultural background of that period. It also reviews the literature and compiles images of artifacts and relics of cheollik excavated from the tombs of its wearers during the Joseon period. The aesthetic aspects of cheollik reflect the mindset of Joseon people, as demonstrated by its diverse proportions and forms, qualities that exhibit “thinking outside the box” flexibility, and the ability to transcend conventions through free thinking and creative imagination in daily life. These are consistent with contemporary fashion trends, thus pointing to the modernity of cheollik, as exemplified by a number of design cases thus inspired. Cheollik represents the kind of beauty that resonates with contemporary individuals, showcasing the potential for historical elements to continuously transform into futuristic forms. In other words, the aesthetic value of cheollik begins with consideration and ends with creation.

This study examines group differences in AI dependence, self-efficacy, and design fixation based on learners’ AI utilization experience, and further explores the relationships between AI dependence and perception-related variables within an AI-based design education context. To this end, I surveyed 42 learners who participated in an AI-based design class. I then performed data analysis in IBM SPSS Statistics 26, using one-way analysis of variance (ANOVA) and Pearson correlation. The ANOVA revealed statistically significant group differences in AI dependence by AI usage level, but no significant differences in self-efficacy or design fixation. Furthermore, age and professional experience showed no significant influence on most variables. Meanwhile, the correlation analysis revealed that AI dependence was significantly positively associated with design fixation, but not with self-efficacy. These results suggest that, in AI-based design education, learners’ perceptions and attitudes are not strongly differentiated by personal background factors such as age or professional experience, but are instead associated with their experience with AI use and perceived dependence on AI. By distinguishing and analyzing AI utilization and AI dependence, this study provides empirical evidence that contributes to a more nuanced understanding of learners’ cognitive perceptions in AI-based design education in practice.

본 연구에서는 최근 국내 절화 시장에서 증가하는 소형·특이 화형 수요와 생산 현장의 노동력 절감 요구에 대응하여 개발된 소형 겹꽃 절화용 거베라 신품종 ‘페더핑크(Feather Pink)’의 육성 경위와 주요 특성을 보고하고자 한다. ‘페더핑크’는 2011 년 초세가 강건한 ‘08B3-3’ 계통을 모본으로, 화색과 화형이 우수한 ‘07B3-14’ 계통을 부본으로 교배한 실생 집단에서 선발되었다. 2011년 교배 후 조직배양을 이용한 계대 증식과 2012- 2020년 연차별 생육 및 절화 특성 평가를 통해 유전적 안정성과 균일성을 확보하였다. 주요 특성으로 두상화 직경은 평균 7.5cm이며, 절화장은 56.2cm로 유통에 적합하였고, 연간 채 화량은 75.3본/주/년으로 안정적인 생산성을 보였다. 절화수명 은 8.7일로 상업적 활용에 충분하였으며, 소형 계열이라 꽃목 처짐이 없어 철심 삽입·테이핑·캡 씌우기 등 꽃목보강작업이 필요하지 않아 재배 및 취급 과정의 노동력 절감에 유리하였다. 또한 기호도 평가에서 화색, 화형, 균형미 등에서 높은 선호도를 나타냈다. 이러한 특성으로 ‘페더핑크’는 최근 확대되고 있는 소형 절화 시장의 소비 경향에 부합하며, 국내 절화 시장의 품종 다양성 확대와 농가의 재배 효율성 향상에 기여할 것으로 기대 된다.