Earticle

This study evaluated students' perspectives on the use of technology-based feedback tools. A total of 48 students majoring in English education participated in the study. A set of 30 questions was employed to capture their insights. Closed-ended questions were subjected to Excel analysis for mean scores and standard deviations, while content analysis was applied to open-ended responses. The outcomes revealed that the most valuable aspect of these tools was their grammar checking functionality, and students expressed a general satisfaction with the feedback provided. However, students reported instances where the tools misunderstood their intended meaning or suggested inappropriate corrections. Despite their effectiveness being perceived as equal, there was still a preference for traditional feedback, which was considered more beneficial than technological tools. Addressing concerns about plagiarism, participants expressed a nuanced stance, as reflected in a mean score of 3.65. A significant 85.1% of the participants felt educational institutions need to offer guidelines or training on the effective use of these tools, particularly in the context of plagiarism detection.

Artificial intelligence (AI) is reshaping various industries, including public relations. This study investigates the impact of AI on public relations practices and research within the context of the Organization-Situation-Public-Communication (OSPC) model. Utilizing a mixed-methods approach, the study explores how AI-driven tools are transforming PR practices by enhancing efficiency, personalizing communications, adapting to situational factors, and fostering relationships with publics. The findings reveal that PR professionals must cultivate new skills and competencies, such as strategic thinking, creativity, emotional intelligence, and technical proficiencies related to AI-driven tools, to remain relevant and competitive in the job market. Additionally, the study underscores the importance of embracing AI-driven tools to improve PR campaign efficiency and effectiveness, while being cognizant of ethical considerations, including data privacy, transparency, and algorithmic bias. The study presents a set of future research questions that address the influence of AI on public relations practices within the OSPC model and beyond, with a focus on the balance between human expertise and AI-driven automation, as well as the role of AI in fostering trust and transparency in PR communications. This research contributes to a deeper understanding of AI's impact on PR and offers insights to help practitioners and researchers navigate the challenges and opportunities presented by rapidly evolving AI technologies in the field of public relations.

We explore the effectiveness of AI-driven markerless motion capture (MoCap) tools compared to the traditional marker-based OptiTrack system, known for its high accuracy in capturing precise movements. Through detailed comparative analysis, we assessed various free markerless MoCap tools, including Move One, Radical, Deep Motion, Plask, Rokoko, and Movmi, focusing on critical aspects such as pose accuracy, movement smoothness, and ground detection. Our findings indicate that Move One is the most versatile tool, offering excellent pose accuracy, smooth MoCap, and reliable ground detection, making it a strong contender for various animation tasks. We found that Radical excels in minimizing jitter, making it suitable for projects requiring smooth motion, while Deep Motion performs best in ground detection, which is crucial for accurate foot placement. Although markerless systems still do not fully match the precision of marker-based systems, we suggest that they present viable alternatives depending on the specific needs of a project. As AI technology continues to advance, we expect the gap between markerless and marker-based to narrow, expanding the potential applications of markerless MoCap in the industry.

본 연구는 전통 수작업 방식, 인공지능(AI) 기반 자동화 방식, 하이브리드 방식 등 세 가지 3D 모델링 제작방식을 비교 분석하여 각각의 효율성과 실무 적용 가능성을 실증적으로 검토하는 데 목적을 둔다. 동일한 콘셉트의 모델을 세 가지 방식으로 제작한 후 총 제작시간, 후처리 소요, Unreal Engine 적용 여부 등 정량적 지표와 더불어 게임 및 디지털 콘텐츠 실무자 3인의 평가를 기반으로 한 정성적 분석을 병행하였다. 정형구조를 가진 경비행기 모델에서는 AI방식의 비정상적 비례와 낮은 충실도가 확인되었으며 비정형 자연물 기반의 바위 및 오크통 모델에서는 AI방식과 하이브리드 방식이 실무 수준의 품질을 구현하였다. 실험 결과, AI모델링 도구는 자연물, 반복 배경 오브젝트 등 특정 조건에서 실질적 효율을 제공할 수 있으나 고정밀 구조물 제작에는 여전히 한계가 존재함을 확인하였다. 본 연구는 현재 3D 콘텐츠 제작 환경에서 AI 기반 모델링 도구의 전략적 도입 범위를 제안하며, 향후 더 다양한 AI기술과의 비교 분석을 통한 확장 연구의 기초 자료로 활용될 수 있을 것이다.

This study aims to empirically examine the efficiency and practical applicability of three 3D modeling production methods by conducting a comparative analysis of a traditional manual method, an AI-based automated method, and a hybrid method. After creating models of the same concept using each of the three approaches, a dual analysis was performed, incorporating quantitative metrics such as total production time, post-processing requirements, and Unreal Engine applicability, alongside a qualitative analysis based on evaluations from three industry practitioners in gaming and digital content. For the light aircraft model, characterized by its defined structure, the AI method resulted in abnormal proportions and low fidelity. In contrast, for the rock and oak barrel models, based on atypical natural objects, both the AI and hybrid methods achieved a quality level suitable for practical use. The experimental results confirmed that while AI modeling tools can offer substantial efficiency under specific conditions, such as for natural objects and repetitive background assets, limitations persist in the creation of high-precision structures. This study proposes a strategic scope for the adoption of AI-based modeling tools in the current 3D content production environment and can serve as foundational data for future expanded research involving comparative analyses with more diverse AI technologies.