This study explores the design and preliminary application of a Korean language class incorporating a retranslation-comparison activity in an AI-assisted translation environment. Grounded in the AI-in-the-loop (AI²L) principle, whereby learners serve as the primary agents of judgment while AI translation functions as a mediating text rather than an authoritative source, a three-session structure was developed and piloted with 30 multilingual international students at a Korean university. In the first session, learners activated background knowledge and critically reflected on AI translation tools. In the second session, they retranslated a Korean text from AI-translated versions in their mother tongues using only a dictionary, without AI tools. In the third session, they compared their retranslations with the original Korean text and presented their translation choices for peer and instructor discussion. The findings indicate that the activity fostered learner agency, metalinguistic awareness, and cross-linguistic contrastive thinking. Variation in retranslation outcomes across language backgrounds—Chinese, Japanese, Vietnamese, Russian, and English—generated rich opportunities for meaning negotiation and interaction. The study also identified several limitations, including uneven participation by proficiency level, learner resistance to AI-free translation, and time constraints within the three-session format. It suggests that retranslation-comparison activities offer a pedagogically viable structure for Korean language instruction in an era of generative AI, and calls for expanded implementation and empirical validation in future research.

본 연구는 인공지능, 즉 AI가 기술적 숭고와 알고리즘의 불투명성을 기반으로 인간의 정치적 책임을 은닉하는 기제를 ‘AI의 알리바이화’ 로 개념화하고, 동시대 예술이 이를 해체하는 비판적 방법론을 고찰한다. AI는 결정권자의 윤리적 책무를 비가시적 기술의 심부로 유폐시키는 ‘은폐된 장소성’의 아키텍처로 기능하며, 이는 ‘객관성의 환상’에 의해 공고해진다. 본 연구는 행정·사법·군사 영역의 알고리즘 오남용 및 비윤 리적 결정 사례들을 분석하여, 그 핵심에서 ‘현장 부재 증명’을 전용한 비평적 개념을 도출하고 이를 기술적 숭고와 알고리즘적 결정론, 매체 고고학을 경유하여 미학적 작동 기제를 규명한다. 나아가 동시대 미술현장에서 AI의 알리바이화를 해체하는 예술 실천을 분석한다. 포렌식 아키텍처와 트레버 패글런의 ‘가시화의 정치학’은 은폐된 장소성을 해체하고, 제임스 브라이들과 아담 하비의 ‘교란의 수사학’은 객관성의 환 상을 파기한다. 이를 통해 동시대 예술이 비판적 디지털 리터러시이자 사회적 실천으로 기능함을 논증한다. 결과적으로 이러한 예술 실천은 기술적 결정론 속에 매몰되었던 인간 주체를 다시 현실의 장소로 강제 소환하며, 인적 책무를 복원하는 저항의 이정표로서 의의를 지닌다.

This study investigated the relationships among learners’ perceptions of AI chatbot use, self-directed learning, and self-efficacy in an interview English course utilizing ChatGPT, and explored the educational implications of generative AI-based instructional design. The course was redesigned around AI tools and implemented in a cyclical structure consisting of model answer generation, instructor feedback, and speaking practice. Answer sharing through Padlet and TTS-based pronunciation practice were also incorporated. Survey data from 49 out of 63 enrolled students majoring in Airline Cabin Service were analyzed. The results indicated that learners perceived the AI chatbot as practically helpful for organizing interview responses and preparing for speaking tasks, with generally high levels of positive responses across related items. Experiences of adjusting question difficulty through prompting and generating responses were associated with increased learning efficacy and speaking confidence. Subcomponents of self-directed learning and self-efficacy showed significant correlations with perceptions of AI chatbot use, and regression analysis revealed that the model including these two variables significantly explained learners’ perceptions of AI use. This study shows that systematically integrating generative AI with instructor feedback can enhance learners’ self-directedness, self-efficacy, and speaking confidence, suggesting the value of positioning AI as a structured learning partner in speaking-focused courses.

This study provides a corpus-based, data-driven synthesis of research on AI-assisted English language assessment published between 2015 and 2025. Using a corpus of 275 peer-reviewed articles indexed in the Web of Science Core Collection, the study applies an integrated text-mining approach to identify dominant research themes and their structural relationships. The findings indicate that the field has been strongly anchored in automated writing evaluation and automated essay scoring, with writing assessment serving as the central research axis. At the same time, recent studies have increasingly emphasized feedback provision, learner engagement, human–AI comparison, and issues of transparency and fairness. Automated speaking assessment based on ASR technologies has also emerged as a growing but still underrepresented area. Overall, the results suggest an increasing thematic orientation toward learner-oriented and feedback-driven assessment practices. The study offers an empirical knowledge map of AI-assisted English language assessment and provides implications for the development of human–AI hybrid assessment systems and responsible use of AI in language assessment.

Purpose: This study aimed to examine the experiences of nursing students developing and implementing generative artificial intelligence (GenAI)-based oncology emergency nursing simulation scenarios. Method: This qualitative content analysis included 23 senior nursing students in the Republic of Korea. Participants worked in teams to develop oncology emergency nursing simulation scenarios using ChatGPT, participated in simulation practice, and completed reflective journals after the program. Data were analyzed using qualitative content analysis. Results: Four themes and twelve categories were identified: “AI-Based Scaffolding for Structuring Clinical Situations,” “Verification of AI-Generated Information and Knowledge Reconstruction,” “Enhancement of Clinical Reasoning and Collaborative Problem-Solving Competencies,” and “Professional Identity Formation and Preparation for Future Practice.” Participants described GenAI as a cognitive scaffolding tool that helped structure complex clinical situations, identify knowledge gaps, critically evaluate AI-generated information, and integrate fragmented knowledge. The program also enhanced clinical reasoning, SBAR-based communication, and collaborative problem-solving. Conclusion: GenAI-based oncology emergency nursing scenario development and simulation may be an effective educational strategy for promoting self-directed learning, critical thinking, clinical reasoning, and practice readiness among nursing students. GenAI can serve as an educational scaffolding tool that supports active knowledge construction and reflective thinking rather than replacing clinical reasoning.

This study aims to compare four common types of feedback used in university English presentation activities: artificial intelligence (AI), professor, peer, and self-feedback. A total of 38 first-year students enrolled in a first-year English course at a Korean university participated in this study by recording and evaluating practice videos to provide feedback to help improve their final presentations. Each of the two practice videos received evaluation scores and written comments from multiple sources, including AI tools, the professor, their peers, and themselves. To examine students’ perceptions of these feedback sources, data were collected through Likert-scale survey items measuring perceived effectiveness and preference, as well as open-ended responses explaining students’ choices. Data analysis included descriptive statistics for the survey responses and content analysis for the qualitative comments. The research findings are as follows. Firstly, professor feedback was perceived as the most effective source for improving presentation performance. Secondly, peer feedback was viewed as helpful for providing additional perspectives on presentation quality. Thirdly, AI and self-feedback were generally perceived as less reliable than human feedback. These findings suggest that combining multiple feedback sources may support speaking development in university English presentation tasks, with pedagogical implications and directions for future research discussed.

As pavements age and traffic loads increase, the importance of reliably evaluating pavement deterioration has increased. Therefore, this study proposes an Artificial Intelligence(AI)-based rating-classification method that integrates Falling Weight Deflectometer(FWD) deflection, the elastic modulus, and the crack rate obtained through coring to construct a continuous comprehensive index and classifies pavement deterioration into four stages based on the index. For this analysis, the deflection and elastic modulus obtained from the FWD test (a nondestructive test) and the crack rate calculated through coring (a destructive test) were converted into a continuous normalization index. Subsequently, a comprehensive index graph was constructed using a weighted integration method, and an AI-based slope change analysis was performed to determine the threshold value for classifying the pavement conditions. The step-by-step scoring-based and continuous comprehensive index-based analyses showed similar overall results. However, the continuous comprehensive index-based analysis was more effective for mitigating the stepwise distribution effect and reflecting the pavement deterioration trend in more detail. In both analysis methods, the slope change point was confirmed as the threshold value. Using this value, the pavement state can be classified into four stages, which reflect the deterioration characteristics of each pavement type. However, this study has several limitations. To improve the reliability of the system, additional detailed data, such as crack location, crack shape, and other field information used in the comprehensive index calculation, should be continuously incorporated. This approach enables the establishment of an evaluation system that integrates nondestructive and destructive test data.

This study employed latent profile analysis (LPA) and latent transition analysis (LTA) to examine the short-term longitudinal development of L2 reading comprehension among young Korean EFL learners in Grades 1 and 2 participating in an AI-integrated gamebased learning program. L2 reading comprehension was assessed at three time points across vocabulary, sentence-level, and discourse-level comprehension. The LPA identified five distinct reading profiles, revealing substantial heterogeneity prior to formal English instruction. The LTA showed pronounced upward transitions during the early phase of the program, particularly among lower-performing learners, followed by strong profile stability in the later phase. These findings suggest that the instructional environment may support initial improvement in foundational reading-related skills. However, the limited later profile transitions indicate possible constraints in sustaining continued developmental progress. The findings highlight both the potential and limitations of AI-supported game-based learning and underscore the importance of examining heterogeneous learner trajectories in early technology-mediated language learning contexts.

본 논문은 생성형 AI 비즈니스의 무형자산 기여도 측정에 관한 선행연구를 검토한 뒤, 생성형 AI 서비스의 성과를 데이터, 모델, 평가/안전, 조직/채널 등 복수의 무형자산으로 분해 하여 직접 효과와 시너지로 설명하는 실무형 모형을 제시한다. 도입 전후 성과에서 기본 성장률을 제외해 순수 개선분을 구하고, 소규모 실험전후 비교, 로그 분석으로 자산지표의 민감도(탄력도)를 추정해 자산별 직접기여를 산출한다. 잔차는 시너지로 정의하고, 초기에는 균등 배분, 이후에는 직접효과 비중에 따라 배분하며, 핵심 자산에는 간이 Shapley를 보조 적용한다. 이 결과는 로열티 설계, 공동개발 분담, PPA 등 현금흐름 의사결정에 바로 활용될 수 있다. 데이터 사전, 버전 고정, 사건 로그, 품질 점검으로 재현성을 확보하고, 신뢰도 가중, 민감도 검증, 상 하한(collar/floor), 정기 재산정으로 운영 안정성을 높인다. 본 모형은 간결하고 검증 가능하나, 강한 비선형성, 지연 효과, 네트워크 효과 등을 포착하기에는 한계가 있어 구간별 재추정과 보완적(추가) 식별이 필요 하다.

Middle-income states like Thailand face a structural dilemma: EU-style AI regulation exceeds administrative capacity, while voluntary models fail to protect fundamental rights. Leveraging Thailand’s 2025 BRICS Partner status, this study proposes a Thai–BRICS Hybrid Governance Model based on functional modularity. This approach avoids wholesale transplantation, instead selectively adapting regulatory mechanisms from BRICS nations to fit Thailand’s specific legal and fiscal constraints. The model addresses five critical gaps: infrastructure dependency, algorithmic opacity, accountability deficits, institutional fragmentation, and labor displacement. The study’s central thesis is that rights remain symbolic without developmental sovereignty, the material control over digital infrastructure. By prioritizing sovereign capacity, Thailand can ensure that algorithmic accountability is enforceable rather than aspirational. This framework reconciles human rights with developmental goals, avoiding the prohibitive compliance burdens seen in previous GDPR-inspired legislation and positioning infrastructure as a prerequisite for genuine rights protection.

본 연구는 신흥시장 환경에서 환경(E), 사회(S), 지배구조(G) 성과와 인공지능(AI) 도입이 기 업가치와 어떠한 관계를 가지는지를 분석한다. 2015년부터 2024년까지의 중국 상장기업을 대상 으로 한 비균형 패널 데이터를 이용하였으며, 기업가치는 토빈의 Q(Tobin’s Q)로 측정하였다. ESG 성과는 블룸버그 ESG 점수를 통해 포착하였고, AI 도입 수준은 기업의 연차보고서 공시 텍 스트로부터 도출된 지표를 활용하여 측정하였다. 이해관계자이론, 신호이론, 자원기반관점에 근거 하여, 연구자는 이분산성을 완화하고 추론의 정확성을 제고하기 위해 횡단면 가중치를 적용한 기업 고정효과 모형을 추정하였다. 분석 결과, ESG 성과와 AI 도입 모두 토빈의 Q와 유의한 정(+)의 관계를 보였다. 반면, 부채비율과 기업규모는 토빈의 Q와 부(-)의 관련성을 나타내어, 자본시장이 지속가능성과 첨단 디지털 역량을 보유한 기업에는 긍정적으로 반응하는 반면, 재무적 위험과 규모 관련 비효율성에는 부정적인 평가를 내림을 시사한다. 전반적으로, 본 연구는 급변하는 제도적 환 경에 있는 중국 시장에서 투자자들이 우수한 ESG 성과와 전략적 AI 활용을 보여주는 기업에 대해 가치평가 프리미엄을 부여함을 보여준다. 이러한 결과는 신흥경제 하에서 지속가능경영 성과와 AI 도입이 시장 기반 기업가치 형성에 동시에 미치는 역할을 실증적으로 제시함으로써, ESG·디지털 전환·기업가치 연구의 교차 영역에 대한 최신 근거를 제공한다.

본 연구는 생성형 AI 음악 플랫폼 Suno를 중심으로, 플랫폼화된 창작 환경에서 이루어지는 음악 창작 활동의 구조와 모순을 분석하였다. 이를 위해 인간의 행위를 사회문화적 맥락 속 활동체계로 이해하는 활동이론(Activity Theory)을 분석 틀로 채택하고, Suno 기반 음악 창작을 주체, 객체, 도구, 규칙, 공동체, 분업의 상호작용 속에서 파악하였다. 분석은 연구자 1인의 직접 창작 실천에 기반한 자기관찰 자료를 활용하여, 활동이론의 여섯 요소에 대응시키는 방식으로 수행되었다. 분 석 결과, 세 가지 핵심 모순이 도출되었다. 첫째, 사용자의 창작 의도와 AI 자동생성 결과 사이의 긴장으로, 프롬프트를 통한 창작 방향 제시가 실제 산출물에 온전히 반영되지 않는 구조적 불일치 가 반복적으로 나타났다. 둘째, 창작 참여의 확대와 통제·전문성 재편 사이의 긴장으로, 접근성 의 확대가 세밀한 창작 통제의 확대로 반드시 이어지지 않으며 기존 음악적 전문성의 위상을 재편 하는 압력으로 작용하였다. 셋째, 공동창작 인식과 저자성 판단 사이의 긴장으로, 사용자는 창작 과정에 분명히 개입하면서도 결과물을 온전히 자신의 창작물로 인정하기 어려운 이중적 구조를 경험하였다. 이러한 분석은 생성형 AI 환경에서 음악 창작자의 역할, 통제 방식, 저자성 인식이 변화하고 있음을 보여주며, 음악 창작 주체가 단일한 인간 저자가 아니라 플랫폼, 기술, 규칙, 공동체와의 상호작용 속에서 재구성되는 관계적 존재로 이해될 필요가 있음을 시사한다.