Earticle

본 논문은 생성형 인공지능(Generative AI) 기술의 발전과 이를 역사학 연구에서 어떻게 활용하고 있는지에 대해 초점을 맞춰 탐구를 진행하 였다. 생성형 인공지능의 등장과 중요 발전 과정을 살펴보고, 이러한 기 술이 역사학의 다양한 분야에서 어떻게 활용되고 있는지 구체적인 사 례를 통해 조명하였다. 생성형 인공지능의 주요 활용 사례로 방대한 역 사자료를 디지털화하고 분석했던 막스 플랑크 과학사 연구소의 “스피 어(The Sphere) 프로젝트”, 역사자료의 번역에 활용한 한국고전번역원의 “고전문헌 자동번역 시스템”, 고대 그리스 비문의 복원에 활용한 구글 딥마인드의 “이타카(Ithaka) 프로젝트” 등을 소개하였다. 아울러 이로 인 한 학문적 접근의 변화와 새로운 연구 방향도 함께 언급하였다. 또한, 생성형 인공지능을 사용할 시에 고려해야 할 윤리적 문제와 기술적 한 계점들을 논의하며, 이 분야의 미래 연구 방향과 발전 가능성에 대해서 간단히 전망하였다.

This paper explores the advancements in Generative AI technology and its application in historical research. It examines the emergence and significant developments of Generative AI, highlighting how this technology is being utilized in various historical fields through specific examples. Major use cases include the digitization and analysis of vast historical data by the Max Planck Institute for the History of Science’s “The Sphere Project,” the application in translating historical documents by Institute for the Translation of Korean Classics “Automatic Translation System for Classical Literature,” and the restoration of ancient Greek inscriptions by Google DeepMind’s “Ithaka Project.” The paper also discusses changes in academic approaches and new research directions, as well as the ethical considerations and technical limitations of using Generative AI, briefly forecasting the future research directions and potential developments in the field.

인공지능(AI)의 발달로 우리는 기술의 눈부신 발전 속에 급격한 변화를 겪으며 살 아가고 있다. 본 연구는 생성형 AI를 정서교육에 활용하는 방안에 대하여 연구하였으 며, 정서교육의 영역은 자기감정인식, 자기감정조절, 자기감정표현, 정서관계기술로 구 분하여 제시하였다. 생성형 AI를 통한 자기정서인식을 위한 방법들로 구글에서 개발 한 인공지능 기반의 그림그리기인 오토드로우(AutoDraw)프로그램과 애니메이티드 드 로잉(Animated Drawing) 생성형 AI를 활용을 제안하였다. 생성형 AI를 통한 자기정 서조절을 위한 방법들로는 AI 대화 시뮬레이션을 활용과 Suno AI의 텍스트 프롬프트 를 통해 다양한 장르, 스타일의 음악을 작곡, 작사하는 활동을 제안하였다. 생성형 AI 를 통한 자기정서표현을 위한 방법들로는 AI 기반 감정 일기 작성 및 큐티 마음일기 작성을 제시하였다. 마지막으로 생성형 AI를 통한 정서관계기술을 위한 방법들로는 GPT Online 감정 코칭/공감 훈련, 갈등해결 코칭, 감정표현/코칭, 용서/관계 회복코칭 을 통해 정서교육을 중심으로 한 관계기술향상 방안을 제안하였다. 위 프로그램들은 신앙기반의 활용이 가능한 것으로 파악되어 기독교교육현장에서 사용에 적합한 것으로 볼 수 있다. 본 연구에서 주요 발견 결과는 다음과 같다. 먼저, 생성형 AI는 청소 년들이 자신의 감정을 보다 정확하게 인식하고, 긍정적이고 신앙적 가치관에 기반 하 여 감정을 표현하도록 돕는 데 기여할 수 있었다. AI는 비판 없이 감정을 수용해주는 대화 파트너 역할을 수행함으로써, 청소년들이 자신을 안전하게 드러내고 내면을 탐 색할 수 있는 환경을 제공하였다. 또한, 성경적 원리에 기반 한 용서, 공감, 갈등 해결 훈련을 AI 코칭을 통해 반복 연습할 수 있다는 것이 고무적이라 할 수 있다. 본 연구 의 교육적 함의에서, 기독교교육은 단순한 지식 전달을 넘어 신앙적, 인격적, 관계적 성숙을 지향한다. 생성형 AI의 활용은 정서적, 관계적 측면에서 청소년들의 인격 형성 에 긍정적인 영향을 미칠 수 있다. 특히 감정 표현을 어려워하는 청소년들에게 AI 기 반 감정 일기, 공감 대화 시뮬레이션, 용서 연습 등의 프로그램은 실질적 교육 도구가 될 수 있다. 교사는 이를 보조적 도구로 활용하여, 개별 학생의 정서 상태를 파악하 고, 더 깊은 상담과 신앙 지도를 진행할 수 있다. 생성형 AI는 기독교 청소년교육에 있어 정서교육의 새로운 가능성을 열어주는 유익한 도구가 될 수 있다. 그러나 기술 자체에 의존하기보다는, 기독교적 가치관과 인격교육의 본질을 분명히 하면서 AI를 선별적으로 활용하는 지혜가 요구된다.

With the development of artificial intelligence (AI), we are living through rapid changes in the remarkable development of technology. This study studied how to use Generative AI for emotional education, and the areas of emotional education were divided into self-emotional recognition, self-emotional control, self-emotional expression, and emotional relationship technology. As a method for self-emotional recognition through Generative AI, it was proposed to use the AutoDraw program, an artificial intelligence-based drawing, and an animated drawing Generative AI developed by Google. As a method for self-emotional control through Generative AI, it was proposed to use AI conversation simulation and write various genres and styles of music through Suno AI's text prompt. As methods for self-emotional expression through Generative AI, AI-based emotional diary and cutie heart diary were presented. Finally, as methods for emotional relationship skills through Generative AI, a plan to improve relationship skills centered on emotional education through GPT Online emotional coaching/emotion training, conflict resolution coaching, emotional expression/coaching, and forgiveness/relationship recovery coaching was proposed. These programs can be seen as suitable for use in the Christian education field as they are understood to be capable of utilizing faith-based. The main findings in this study are as follows. First, the Generative AI could contribute to helping adolescents more accurately recognize their emotions and express their emotions based on positive and religious values. AI provided an environment where adolescents could safely reveal themselves and explore their inner self by playing the role of a conversation partner who accepts emotions without criticism. In addition, it can be said that it is encouraging to be able to repeatedly practice forgiveness, empathy, and conflict resolution training based on biblical principles through AI coaching. In the educational implication of this study, Christian education aims for religious, personal, and relational maturity beyond simple knowledge transfer. The use of Generative AI can have a positive effect on the character formation of adolescents in terms of emotional and relational aspects. In particular, for adolescents who have difficulty expressing their emotions, programs such as AI-based emotional diary, empathy conversation simulation, and forgiveness practice can be a practical educational tool. Teachers can use this as an auxiliary tool to understand the emotional state of individual students and conduct deeper counseling and faith guidance. Generative AI can be a beneficial tool that opens up new possibilities for emotional education in Christian youth education. However, rather than relying on the technology itself, wisdom is required to selectively use AI while clarifying Christian values and the essence of personal education.

With the introduction of generative artificial intelligence(AI) tools such as OpenAI's Sora into the global film industry, including Hollywood, there has been a simultaneous emergence of innovations in film production as well as various crises. These changes are spreading throughout the entire film production process, including scriptwriting, casting, editing, and acting. This study analyzes the impact of AI on the film industry, particularly Hollywood, and explores how this technology might bring about changes in Korean cinema. AI technologies applied in the film industry offer benefits such as reducing production time and costs. However, they also pose threats to many filmmakers and actors who rely on the traditional production methods, leading to ethical and legal issues. In Hollywood blockbuster films, AI technology is used to create realistic visual effects, analyze scripts, and suggest optimal shooting angles. While these applications improve the qualitative level of films, they also reduce the human resources required in traditional film production processes. The impact on the Korean film industry is also noteworthy. Some Korean film production companies are leveraging AI to create films in a more creative and efficient manner. Efforts are being made to analyze audience data using AI and develop storylines that appeal to a larger audience. However, these technological changes are controversial among many Korean filmmakers who prefer traditional production methods. This study provides an in-depth discussion on whether the adoption of AI in the film industry can bring about positive innovation or inevitably lead to crises. It analyzes how AI technology is transforming traditional roles in the film industry and what new opportunities and challenges this change generates within the industry. Additionally. This study highlights the differences in technology adoption between Hollywood and Korean film industry and explores how each industry is embracing these technological changes.

Recent advances of Generative AI (GenAI) tools have transformed information retrieval by offering conversational chatbot interaction and synthesized knowledge access. However, Generative AI systems rely on static, pre-trained data that are often outdated, making them prone to generate hallucinations – fabricated and inaccurate outputs. Retrieval Augmented Generation (RAG) technology is a promising architecture that enhance AI outputs by integrating external, accurate data. While RAG’s technical performance has been widely studied, there are limited studies on user interaction with RAG and its influence on user performance in real-world tasks. This research addresses this gap and assesses the effectiveness of RAG in user outcomes. Grounded in Task-Technology Fit (TTF) theory, we employ a scenario-based experiment design using 2x2 factorial design (AI System Type x Task Complexity). Participants complete tasks of different complexities using either standard LLMs or RAG systems. User performance is assessed through information quality metrics: accuracy, completeness and relevance. Findings are expected to contribute to evaluation of practical utility of RAG tools.

This study proposes a real-time content design pipeline optimized for Unreal Engine, integrating generative AI-based image creation with AI-assisted 3D modeling tools. The pipeline aims to streamline the production of high-quality assets for real-time applications, including games and simulations. Two types of subjects were selected: a bust combining organic character features, and a stone slab characterized by planar and symmetrical structure. Multi-angle image data were first synthesized using advanced generative AI models to simulate diverse viewpoints. These were then processed using AI-enhanced photogrammetry and modeling tools to reconstruct detailed 3D meshes and extract base textures. Post-processing steps, including mesh decimation, UV unwrapping, and texture baking, were performed to ensure compatibility with Physically Based Rendering (PBR) workflows used in Unreal Engine. The final assets were successfully imported into Unreal Engine, demonstrating visual fidelity and performance suitability in a real-time environment. The study confirms the pipeline’s potential for accelerating asset development and suggests promising future directions in AI-driven digital content creation.

This paper examines the story generation capabilities of generative AI, which are increasingly utilized across various industries. The narrative of the classic novel Don Quixote was input into ChatGPT, a generative AI model, and reconstructed through natural language prompts that applied the MacGuffin technique to create new story structures. The AI-generated narratives, produced in the forms of synopses and scene-by-scene treatments, were compared with the original work to evaluate narrative completeness. Furthermore, the reconstructed narratives were transformed into screenplay and game event formats to assess their potential applications in various media content. The results indicate that generative AI achieved meaningful outcomes in restructuring the original story into a reversal-based narrative using the MacGuffin structure. It also demonstrated sufficient capability to adapt stories for character dialogues and scene direction. Therefore, this paper suggests that generative AI can function as a supportive tool for human writers in the idea development and pre-production stages of screenplay and digital content creation.

As generative AI becomes increasingly integrated into various applications, its role in enhancing smart home IoT systems has gained significant attention. This study explores the impact of prompt engineering on the performance of generative AI within smart home IoT environments. By optimizing input prompts, we investigate how AI can more effectively control smart home devices, improving performance metrics such as task execution and system efficiency. Using an experimental method, we compare the outcomes of different prompt designs, focusing on key metrics: generation accuracy, completion time, and task performance. Our findings provide insights into how prompt engineering can maximize the utility of generative AI in smart home settings, contributing to advancements in smart home automation and personalized services. This research adds to the growing body of knowledge on AI-IoT integration, highlighting the potential of prompt optimization in real-world applications.

본 연구는 디지털 영상 제작 프로세스에서 점점 중요한 역할을 하고 있는 생성형 AI의 발전과 그 영향을 탐구한다. GPT, GAN 및 기타 생성 알고리즘과 같은 모델의 개발에서 AI 기술의 급속한 발전으로 영상 제작 환경이 큰 변화를 겪고 있다. ChatGPT, Runway, DALL·E, MidJourney, SunoAI 등 생성형 AI 모델의 발전으로 영상 제작 단계에서 적용 가능성이 크게 확장되었다. 생성형 AI는 아이디어 기획에서부터 최종 편집 프로세스에 이르기까지 다양한 제 작 단계를 간소화할 수 있는 잠재력이 있다. 예를 들어, AI는 플롯 아이디어나 대화를 생성하 여 대본 작성을 지원할 수 있으며, 후반 작업에서는 시각 효과를 향상시키고, 사실적인 환경을 만들거나, 반복적인 편집 작업을 자동화할 수 있다. 또한, AI 기반의 사운드 디자인 도구는 영 상 분위기에 맞춘 음악과 사운드 효과를 자동으로 생성할 수 있다. 본 연구는 현재 사용되고 있는 생성형 AI 기술을 조사하고, 특히 런웨이 AI 영화제에서 소개된 사례들을 통해 그 장점 과 한계를 분석한다. 연구 결과, 생성형 AI는 영상 제작에 드는 시간과 비용을 획기적으로 줄 일 수 있는 잠재력을 지닌 반면, 저작권 문제와 딥페이크와 같은 기술적, 윤리적 문제는 신중 한 고려가 필요함을 제시한다. 향후 연구 과제는 AI와 인간의 예술적 창의성 간의 균형을 유 지하는 방법에 관한 것이다.

This paper is to study the role of generative AI in transforming the digital video production process. As AI technologies, particularly models like GPT, GANs, and other generative algorithms, continue to advance rapidly, the video production landscape is undergoing significant change. Generative AI can streamline various stages of production, from the initial ideation phase to the final editing process. For instance, AI can assist in scriptwriting by generating plot ideas or dialogue, and in post-production, it can enhance visual effects, create realistic environments, or automate repetitive editing tasks. Moreover, AI-driven sound design tools can compose music or generate sound effects that dynamically adapt to the content of the video. This study examines the current applications of these technologies in real-world video production, providing insights through case studies and analysis of existing tools. Ultimately, the study concludes that while generative AI holds immense potential to revolutionize the video production industry by reducing time and cost, it also presents new challenges that require thoughtful consideration of its limitations and ethical boundaries.

The rapid growth of generative AI has raised concerns about content authenticity on user-generated platforms, particularly in online reviews. This study proposes an interpretable, feature-based machine learning approach to detect AI-generated reviews, focusing on transparency and efficiency. By integrating linguistic feature analysis (LIWC), textual pattern recognition (TF-IDF), and Large Language Model (LLM)-based interpretation, Random Forest and XGBoost classifiers were applied to achieve robust predictive performance. SHAP value analysis was used to enhance interpretability by identifying key linguistic and structural patterns distinguishing AI-generated content from human-written reviews. The findings reveal that AI-generated reviews tend to exhibit structured grammar, formulaic conclusions, exaggerated sentiment, and broader aspect coverage compared to the nuanced and informal style of human reviews. This study contributes to the field by offering (1) an effective feature-based detection framework, (2) empirical validation of linguistic distinctions between AI and human content, and (3) practical guidance for developing lightweight, trustworthy AI-content detection tools.

While generative AI's Video-to-Video technology has rapidly advanced, challenges remain in video production requiring complex camera movements and precise angle control. This study proposes a workflow that validates precise camera control by inputting reference videos created with Unity game engine into Video-to-Video generative AI. We designed five camera control missions (120⁰ rotation, vertical crane shot, compound dolly zoom, spiral orbit, and multi-angle transition) and created reference videos using Unity for each mission, then generated final videos using Runway Gen-3's Video-to-Video functionality. Focus group interviews with 5 video production experts showed that Unity references accurately conveyed camera movements across all missions (average 4.6/5 points), maintaining high accuracy (4.5 points) even in complex spiral orbit missions. Experts identified frame-level precise control, ease of iterative modification, and predictable results as key advantages, with all participants expressing intention to apply this in professional practice. This study empirically demonstrates that game engine-based reference generation is a practical solution to camera control challenges in generative AI video production.

본 연구는 생성형 인공지능(Generative Artificial Intelligence, 이하 Generative AI)이 스마트 물류 센터의 계획(Planning)과 예측(Forecasting) 시스템에 미치는 구조적 변화를 탐구한다. 기존 공급 망관리(SCM) 연구가 주로 예측 분석과 자동화에 집중한 반면, 생성형 AI는 반응적 예측을 넘 어 선제적 시나리오 창출(Proactive Scenario Creation)을 가능하게 한다. 본 연구는 파괴적 혁신 이론, 동태적 역량이론, 사회기술시스템 관점을 통합하여, 조직이 기술 통합(Technology Integration) 과 조직 적응(Organizational Adaptation)을 조율하는 과정을 설명하는 DAIM(Disruptive AI Integration Model)을 제안한다. DAIM은 생성형 AI가 물류시스템의 의사결정 구조와 역량체계를 재구성하는 메커니즘을 이론 적으로 제시하며, 향후 실증 검증의 기초 틀을 마련한다. 특히, 생성형 AI 도입은 운영비용 절 감(20~40%), 처리량 향상(25%), 공간 효율 개선(30%) 등 산업적 성과와 연계된다. 본 연구는 이러한 성과가 한국의 무역금융 및 물류정책, 특히 K-SURE(한국무역보험공사)의 AI 기반 디 지털 전환 전략과 밀접하게 관련됨을 제시하며, 물류금융(Logistics Finance)과 위험관리(Risk Management) 분야에서의 향후 실증 연구 방향을 제안한다.

Purpose : This study examines the disruptive influence of Generative Artificial Intelligence (Generative AI)on planning and forecasting systems in smart logistics centers, proposing the Disruptive AI Integration Model (DAIM)to explain how Generative AI reshapes decision-making logic and organizational readiness. Research design, data, methodology : Using a conceptual approach, the study integrates theories of disruptive innovation, dynamic capabilities, and socio-technical systems, supported by industry cases from Amazon Robotics, DHL Resilience360, and K-SURE’s Smart Logistics Finance initiative. Results : Generative AI-enabled logistics achieve 20-40% cost reduction, 25% throughput improvement, and 30% spatial efficiency through autonomous simulation and adaptive planning. Conclusions : Generative AI functions as a disruptive general-purpose technology, transforming logistics operations and offering a conceptual foundation for future empirical research in logistics finance and trade insurance.

본 연구의 목적은 생성형 AI를 활용한 대학생 주도 약물 예방 프로젝트에 참여한 학생들의 경험을 탐색하고, 디지털 예방 활동에서 AI의 교육적 기능을 규명하는 데 있다. 연구 대상은 8주간 SNS 기반 예방 캠페인에 참여한 대 학생 8명이며, 반구조화된 심층면담과 함께 카드뉴스, 해시태그, 영상 등 캠페인 산출물을 보조자료로 수집하였다. 수 집된 자료는 질적 내용 분석 절차에 따라 개방코딩, 범주화, 주제 도출 과정을 거쳐 분석하였다. 연구 결과, 생성형 AI 는 아이디어 생성과 초안 작성 과정에서 콘텐츠 제작에 대한 부담을 완화하고, 사실 검증과 재작성 과정에서는 학생 들의 비판적 디지털 역량을 강화하는 역할을 수행하였다. 또한 또래를 대상으로 한 예방 메시지 전달 경험은 약물 위 험 인식과 예방 동기 향상으로 이어졌다. 본 연구는 생성형 AI 기반 학생 주도 약물 예방 교육의 실천적 가능성과 교 육적 활용 가치를 시사한다.

This study explored the experiences of university students who participated in a generative AI supported, student-led drug prevention project and examined the educational roles of AI in digital prevention activities. The participants were eight university students who took part in an eight-week social media–based prevention campaign. Data were collected through semi-structured, in-depth interviews, along with supplementary materials including campaign outputs such as card-news posts, hashtag content, and videos. The collected data were analyzed using qualitative content analysis, following procedures of open coding, categorization, and theme development. The findings indicated that generative AI reduced the burden of content production by supporting idea generation and initial drafting. Additionally, students’ strengthened their critical digital competencies through fact-checking and rewriting processes applied to AI-generated content. Furthermore, delivering prevention messages to peers increased students’ awareness of drug-related risks and enhanced their motivation for drug prevention. Overall, this study highlights the practical potential and educational value of generative AI–based, student-led drug prevention education in digital environments.

This study proposes a method for achieving more intuitive and efficient in-game character facial customization using Generative AI. Conventional avatar customization in MMORPGs is generally limited by fixed options, making it difficult to accurately recreate a desired appearance and requiring extensive work time. To address these issues, we employ Stable Diffusion to generate character facial images from text prompts, and then map the landmark data extracted by a facial recognition algorithm to the customization variables. Consequently, we confirmed that primary facial features eyes, nose, mouth, and so on could be integrated without the need for additional 3D modeling, allowing for faster and more creative customization than traditional manual adjustment. Moreover, by mapping the generated images onto a character’s appearance, we preserved the game’s narrative framework and polygon structure while maintaining a high degree of similarity in key facial elements such as eye shape, lips, and jawline. In future work, we intend to fine-tune the model using game-specific datasets and extend the customization scope to include clothing, items, and environments. This study not only demonstrates the potential of automated avatar customization through Generative AI but also serves as a foundational reference for novel approaches in subsequent game development.

대규모 언어 모델의 발전으로 언어와 텍스트 기반 상호작용에 생성형 AI의 사용이 증가하고 있으며, 특히 교육 분야에서 맞춤형 학습 경험을 제공하며 큰 잠재력을 보여주고 있다. 그러나 생성형 AI의 교육적 효과를 입증하는 실증적 연구는 부족한 실정이다. 본 연구에서는 인간 에이전트와 생성형 AI 에이전트의 효과를 비교 분석하기 위해 참가자들을 두 그룹으로 나누어, 통제그룹은 인간 에이전트와, 실험그룹은 생성형 AI와 4주간 영어 말하기를 학습하는 실험을 진행했다. 결과적으로, 인간 에이전트와 생성형 AI의 학습성과에 유의미한 차이는 없었으나, 초기 영어 레벨이 낮은 학습자들의 경우, 생성형 AI가 더 큰 학습 성과를 보였다. 이는 생성형 AI가 자신감이 낮은 학습자들의 참여를 촉진하는데 효과적임을 시사한다. 또한 생성형 AI 사용이 학습자의 평가받는 두려움을 감소시킴을 확인하였다. 이러한 발견은 생성형 AI가 언어 학습을 향상시킬 수 있는 방법과 그 메커니즘을 이해하는 데 중요한 이론적 기여를 하며, 실제 교육 환경에서의 적용 전략에 실질적인 시사점을 제공한다.

This study investigates how non-experts learn to use generative AI image tools by comparing outcome-oriented tools (e.g., Midjourney, DALL·E) with process-oriented tools (e.g., ComfyUI). Outcome-oriented tools offer intuitive interfaces and immediate feedback, lowering initial cognitive load, while process-oriented tools provide advanced control but require higher effort to master. Using surveys with 15 participants and in-depth interviews with 6 users, this exploratory study examined cognitive load, sense of control, and motivation. Results show that outcome-oriented tools effectively engage beginners, whereas process-oriented tools foster sustained learning once early barriers are overcome. Based on these findings, a three-stage curriculum—Basic Exploration, Advanced Control, and Creative Application—is proposed to gradually reduce cognitive barriers and support long-term creative growth.

Generative AI is increasingly shaping human experiences across creative, social, and professional domains. While its digital capabilities offer new opportunities for enhancing user engagement, personalization, and productivity, its dual impact on users’ psychological needs remains underexplored. Grounded in self-determination theory, this study investigates how generative AI both satisfies and frustrates users’ psychological needs and examines the broader implications of this dynamic. Employing a netnographic methodology, the research draws on over 1.5-year of naturalistic online AI Community observation, resulting in a rich dataset comprising 2,062 pages of data. This research uncovers paradoxical relationships between generative AI features and users’ needs – autonomy, competence, and relatedness – highlighting the simultaneous fulfillment and frustration they can provoke. These findings offer critical insights for the user-centered design of generative AI systems, and have implications for developers, business professionals, and policymakers aiming to balance innovation with psychological well-being.

본 연구는 텍스트 마이닝과 생성형 인공지능을 활용해 2000년대 이후 기업가정신 연구 동향을 분석하여 기업가정신 분야의 연구 동향과 주제, 향후 연구 주제를 파악하고자 한다. 토픽 모델링은 문서 집합에서 숨겨진 토픽을 발견하고, 문서 내 단어 패턴을 분석해 토픽을 추론하며, 각 토픽을 대표하는 단어를 식별하는 비지도 학습 방식이며, 생성형 AI는 기존 데이터 를 학습하여 새로운 데이터를 생성하는 AI 모델로 이미지, 텍스트, 음악 등 다양한 유형의 데이터를 생성한다. 토픽 모델링 결 과 기업가정신, 기업가, 소셜, 비즈니스, 연구 등이 중요한 토픽으로 나타났다. 생성형 AI를 활용한 키워드 분석 결과 이벤트, 복지, 성 역할, 가상, 스타트업, 교육, 소셜 미디어, 여성 기업가정신이 주요 연구 테마로 나타났다. 본 연구결과는 기업가뿐만 아니라 일반 대중에게도 기업가정신의 중요성을 인식할 수 있는 기반을 제공할 수 있을 것으로 보인다.

This study uses text mining and generative AI to analyze entrepreneurship research trends since the 2000s to identify research trends, topics, and future research topics in entrepreneurship. Topic modeling is an unsupervised learning method that discovers hidden topics in a set of documents, analyzes word patterns in documents to infer topics, and identifies words that represent each topic. Generative AI is an AI model that generates new data by learning from existing data and generates various types of data such as images, text, and music. The results of topic modeling showed that entrepreneurship, entrepreneurs, social, business, and research are important topics. Keyword analysis using generative AI revealed the following key research themes: events, welfare, gender roles, virtual, startup, education, social media, and female entrepreneurship. The results of this study can be used can provide a basis for recognizing the importance of entrepreneurship not only for entrepreneurs but also for the general public.

In this paper, we investigate the use of Generative AI to enhance and augment datasets within the context of smart factory metaverse platforms. Specifically, we propose a method for generating synthetic abnormal data using Generative Adversarial Networks (GANs) to address the inherent data imbalance issues in PCB (Printed Circuit Board) datasets, where normal data far exceeds abnormal samples. In our study, we demonstrate that generating synthetic data from a minimal set of abnormal samples significantly improves the performance of AI models, such as MobileNet-V3 Large. By augmenting the abnormal dataset from 20 to 500 images, the classification accuracy of the model increased from 74.9% to 97.3%, validating the effectiveness of this approach. This research highlights the potential of combining generative AI with metaverse platforms, enabling real-time guidance and training for users without the limitations of time and space, thus enhancing production efficiency and minimizing human error in smart factory environments.

이 연구는 생성형 AI(Generative AI) 기술을 활용하여 기본 시각디자인 분야에서의 새로운 접근 방법과 가능 성을 탐색한다. Generative AI는 데이터 기반 학습을 통해 창의적인 디자인을 생성하는 인공지능 기술로, 시 각디자인에서 중요한 역할을 하고 있다. 본 연구는 Generative AI가 시각디자인의 기본 요소와 원리에 어떻게 적용되며, 디자인 프로세스를 어떻게 혁신할 수 있는지를 분석한다. 먼저, Generative AI가 색채, 형태, 구성과 같은 디자인의 기본 요소를 어떻게 해석하고 재창조하는지에 대해 연구한다. 이를 통해 AI가 디자인의 창의 성과 예술성을 어떻게 향상시킬 수 있는지를 탐구한다. 또한, AI가 디자인 결정 과정에서 어떻게 인간 디자 이너를 보조할 수 있는지에 대해서도 연구한다. 이 연구는 Generative AI를 실험하고, 이를 통해 얻은 시각디 자인 결과를 통해, AI가 시각디자인의 전통적인 접근 방식에 어떤 새로운 시각과 해석을 제공하는지를 조사 한다. AI 기술의 발전이 디자인의 미래와 디자이너의 역할에 어떤 변화를 가져올지에 대한 통찰과 디자인 분 야의 전문가뿐만 아니라, AI 기술에 관심 있는 학자들에게도 중요한 시사점을 제공한다는 점에서 의의를 찾을 수 있다.

This research explores new approaches and possibilities in the field of basic visual design using Generative AI technology. Generative AI, an artificial intelligence technology that creates creative designs through data-based learning, plays a significant role in visual design. This study analyzes how Generative AI can be applied to the fundamental elements and principles of visual design and how it can innovate the design process. Initially, the research focuses on how Generative AI interprets and reimagines basic design elements such as color, form, and composition. Through this, it explores how AI can enhance the creativity and artistry of design. Additionally, the study examines how AI can assist human designers in the decision-making process. This research involves experimenting with Generative AI and, through the visual design outcomes obtained, investigates how AI provides new perspectives and interpretations to the traditional approaches of visual design. The advancement of AI technology and its implications for the future of design and the role of designers offer significant insights, not only for experts in the design field but also for scholars interested in AI technology, marking the significance of this study.

This study takes the technology acceptance model (TAM) as the theoretical basis, combines the unique technical attributes of generative AI, and constructs an extended model of AI technical and user characteristics, perceived usefulness and ease of use, and continuous use intention. Hypothesis testing confirmed that the Technology Acceptance Model (TAM) applies to generative AI, with Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) being the strongest factors determining continued usage intention. PEOU was positively predicted by user traits (AI literacy, experience) and two technical features (interactivity, creativity), while all four features (personalization, interactivity, creativity, contextual awareness) boosted PU. Importantly, information credibility enhanced both perceptions, lowering acceptance barriers. The study highlighted that while creativity was the strongest driver of PU, the influence of personalization and contextual awareness on PEOU was limited because these features operate implicitly in the system’s background. Interactivity was the most influential factor for ease of use, while personalization and contextual features need clearer interface visibility to be more effective. Lastly, the findings suggest that technological accordance and user readiness jointly influence behavioral intention through the mediating effects of perceived usefulness and ease of use.