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This study investigates EFL learners’ attitudes towards AI writing tools and explores how both native language (L1) and AI features influence their writing strategies. Participants completed surveys before and after multiple writing and drafting sessions, reporting on their use of metacognitive, cognitive, methodological, L1, and AI tool strategies. The survey results revealed that Naver Papago was the most popular AI tool among learners, reflecting their overall confidence in using and controlling such tools. However, the correlative and interdependent nature of these results suggests that Korean EFL learners may struggle to integrate L1 and AI tools effectively to enhance their metacognitive writing strategies and develop as competent and independent writers. The study concludes by discussing its limitations and recommending further research to support EFL learners in using these tools alongside L1 resources for better writing outcomes.

본 연구에서는 인공지능 활용 및 이론 교육을 위해 자연어처리 기술을 갖춘 네이버의 ‘요약봇’과 음성인식 및 텍스트 음성 변환 기술을 응용한 동영상 편집 프로그램인 ‘Vrew’를 활용한 국어과 융합 교육 프로그램을 고안하 였다. 6학년 2학기 국어 6. 정보와 표현 판단하기 단원에서 ‘인공지능 기술을 활용한 뉴스 만들기’를 주제로 총 6 차시의 수업을 구성하였으며, 이를 통해 학생들은 인공지능의 유용성과 편의성을 인식하고, 생활 속에서 인공지 능 활용능력을 기를 수 있다. 본 연구에서 고안한 교육 프로그램은 국어과 융합을 통해 교육과정 운영에서 인공 지능 교육을 위한 시수 부족 문제를 보완하고, Chat GPT와 같은 즉답 AI로 학생들이 과제를 수행하는 문제점 을 해결하는데 하나의 방안을 제시한다. 아울러 인공지능의 활용과 이론을 함께 교육하여 학생과 교사가 흥미를 잃지 않고 인공지능 교육을 이어갈 수 있도록 기여한다.

For artificial intelligence utilization and theoretical education, this study devised a Korean convergence education program using Naver's 'Summary Bot' with natural language processing technology and 'Vrew', a video editing program applying voice recognition and text-to-speech technology. In the second semester of the sixth grade Korean ‘Chapter 6. Judging information and expression’, a total of six classes were organized under the theme of "Making News Using Artificial Intelligence Technology," through which students can recognize the usefulness and convenience of artificial intelligence and develop their ability to use artificial intelligence in their lives. The educational program devised in this study compensates for the lack of time for artificial intelligence education in curriculum operation through convergence with Korean, and suggests a way to solve the problem of students performing tasks using instantaneous AI such as Chat GTP. In addition, it contributes to educating students and teachers about the use and theory of artificial intelligence so that they can continue their artificial intelligence education without losing interest.

AI도구의 발전은 일본어를 비롯한 언어 학습에도 큰 변화를 가져오고 있다. 본 연구에서는 AI시대의 일본어 연구 성과를 바탕으로 일본어교육의 실천 사례를 소개하였다. 먼저, AI시대의 일본어 연구에서는 텍스트 마이닝 기법을 활용하여 AI 관련 일본어 연 구의 성과와 동향을 기술하였다. 그 방법으로 Google Scholar 검색 도구를 사용하여 “AI”와 “日本語”를 모두 포함하는 발표･연구 논문에 수록된 연구타이틀을 추출하였다. 이를 통해 얻은 176건의 데이터를 대상으로 텍스트 마이닝을 실시하였다. 추출어 분석 결과, AI 관련 일본어 연구는 ‘일본어교육’ 분야에서 가장 활발히 진행되고 있음을 확인 하였다. 구체적인 연구 대상으로는 [翻訳]이 가장 많았으나, 그밖에도 [作文] [文章] [音 声] [文法] 등 폭넓게 AI가 활용되고 있다. 다음으로, AI번역 도구를 활용한 일본어교육 실천 사례를 제시하였다. 한국의 대학 에서 진행한 “이문화 커뮤니케이션” 수업의 활동으로서 ‘AI 도구를 활용한 동영상 자막 제작’을 소개하였다. 동영상 자막 제작 활동의 의의로는 동영상 콘텐츠를 활용함으로써 수업에 대한 학생들의 관심을 끌 수 있었다는 점이다. 또한, 학생들이 직접 동영상을 선택하고 스스로 번역과 자막을 만들어 발표함으로써 수업에 적극적으로 참여하는 모 습을 보였다. 전체적인 성과로는 교사의 언어 능력과 학생의 디지털 능력을 융합한 형 태의 교실활동으로서 AI 번역을 통한 언어 능력 및 디지털 활용 능력의 교육적 가능성을 확인하였다.

There are significant changes to learning languages, such as Japanese, with the advancement of AI tools. In the AI era, text mining techniques are employed to describe the achievements and trends in AI-related Japanese language research. Specifically, I used the Google Scholar search tool to detect research titles containing both “AI” and “日本語” (Japanese language) from published papers and research articles. The 176 data points obtained were then mined for text. Analysis of the extracted terms confirmed that AI-related Japanese language research is most actively conducted in the ‘Japanese language education’ field. The most common specific research subject was [translation], but AI is also being widely used in areas, such as [Composition], [Sentence], [Phonetics], and [Grammar]. In this study, I present practical examples of Japanese language education using AI translation tools. My study introduces ‘Video Subtitle Creation Using AI Tools,’ an activity implemented in an ‘Inter-Cultural Communication’ course at a Korean university. The significance of the Video Creation (translation and subtitling) activity was that it captured the interest of students in the class by utilizing video content. Furthermore, students actively participated by selecting the videos themselves, independently created translations and subtitles, and presented their work. Overall, this activity was confirmed to be effective to enhance both language proficiency and digital literacy skills through AI translation. This represents a classroom activity that combined the linguistic competence of the teacher with the digital capabilities of the students. Overall, the findings of my study confirm the educational potential of classroom activities that integrate linguistic proficiency of teachers with digital literacy of students by utilizing AI translation to enhance both language skills and digital application abilities.

본 연구는 패키지 디자인 개발 과정에서 텍스트 생성 AI(ChatGPT), 이미지 생성 AI 미드저니(Midjourney), 달리 (Dall-E), 어도비 파이어플라이(Adobe Firefly) 등을 활용한 AI 기반 디자인 프레임워크를 제안하였다. 디자이너와 AI 의 협업 구조를 바탕으로 디자인 씽킹의 주요 단계(공감, 정의, 아이데이션)에서 AI의 실제 활용 가능성을 확인하였 다. ChatGPT는 컨셉 기획, 스토리텔링, 프롬프트 구성에서 핵심적인 역할을 수행했으며, 미드저니(Midjourney) 등 이 미지 생성 AI를 활용해 디자인안을 다양하게 확장하였다. 제안된 프레임워크는 교육과 실무 모두에 적용 가능하며, 학생들의 창의성과 실무 역량 강화를 위한 가치도 지닌다. 따라서 본 연구는 산학협력을 기반으로 한 확장성과 실용 성을 갖춘 AI 기반 디자인 협업 모델로서의 의의를 가진다. 향후에는 제안된 AI 기반 디자인 프레임워크를 다양한 산 업 분야와 교육 현장에 확대 적용하여 실효성을 검증하고, 각 프로세스 단계별 최적화된 AI 활용 방안을 구체화할 예 정이다. 또한, 장기적으로는 산학협력 네트워크를 강화하여 산업 현장에서의 적용 사례를 축적하고, 지속가능한 디자인 전략 수립에 기여할 수 있는 확장형 협업 모델로 발전시킬 계획이다.

It is intended to establish AI-based design planning solutions that effectively integrate artificial intelligence (AI) in the design development process of research backgrounds and purpose companies to improve creativity and efficiency. To this end, we analyzed the design development process of Chong Geun Dang Health and presented a strategic framework to maximize the IDIT performance in the design thinking methodology using the generic AI tool. As digital conversion accelerates, creative problem-solving approaches such as design thinking are also changing. At the center of this change is the development of the productive AI, which is widely applied not only to analytical and perceptual function but also to areas where creativity is required. The research method of research is empirically analyzed through the web design and package design project when the generated AI is used as a tool for the design thinking process (Discover - Define - Develop - Deliver). Studies show that the study results increase the progress of each stage, improve the data collection accuracy, and high quality visuals. We have confirmed that we can support prototype development. In addition, each step suitable AI tools and its features were derived, and the actual application plan was proposed. Research results will provide real guidelines for the use of AI in design planning, and to contribute to expanding the theoretical exterior of AI integrated design methodology in the corporate environment.

The study aims at exploring cognitive factors that might have an impact on the effect of the use of ChatGPT as a writing feedback giving tool. Five Form four students in a Band two secondary school in Hong Kong participated in the study. In each meeting session, they were asked to write a writing task and revise it based on the feedback received from ChatGPT. To understand more about the cognitive processes involved when the feedback is processed, a stimulated recall interview was conducted after each revision process. In the fourth meeting, some questions regarding how they feel about the use of this kind of feedback were asked. Two factors were found to be related to the effectiveness of the WCF: (1) whether the feedback could be processed with understanding; (2) whether the learners can pay enough attention to the areas for improvement provided by the feedback the next time they write. Students generally value the use of feedback but admitted that sometimes failing to understand the feedback or pay enough attention to the language items targeted. Further research into the effect of ChatGPT as a writing feedback giving tool with variation can help teachers make pedagogical decisions to maximize the effect of the feedback.

這項研究旨在探討可能影響ChatGPT作為寫作反饋工具效果的認知因素。研究參與者為香港一所第二組別中學的五名中四學生。在每次會面中，他們被要求完成一項寫作任務，並根據從ChatGPT收到的反饋進行修改。為了了解反饋處理過程中涉及的認知過程，在每次修改過程後，學生均參加刺激回憶訪談。在第四次會面中，他們亦被問及對這種反饋使用感受。此項研究發現兩個與寫作反饋效果相關的因素：（1）反饋能否被理解；（2）學習者能否在下次寫作時，對反饋提供的改進方面，給予足夠的關注。學生們普遍重視反饋的使用，但承認有時無法理解反饋或未能足夠關注反饋所針對的語言項目。建議進一步研究ChatGPT作為寫作反饋工具及其不同的應用方法，有助於教師做出教學決策，以最大程度發揮反饋的效果。

Background: Generative artificial intelligence (AI) is rapidly proliferating in educational settings, influencing a range of learning activities such as information retrieval, conceptual understanding, and writing assistance. However, most studies have focused on engineering disciplines, and research on sex-based differences in adopting AI remains limited. Purpose: This pilot study aimed to examine the current use, perceptions, and ethical attitudes toward generative AI tools among undergraduate students in health science disciplines, and identify sex-based differences. Study design: Cross-sectional survey. Methods: A cross-sectional survey was conducted among health science undergraduates. The questionnaire comprised 20 items across six domains: demographic characteristics, AI usage experience, purpose of use, perceptions of learning outcomes, ethical awareness, and intentions for future use. Descriptive statistics, cross-tabulation, and chi-square tests were used to analyze the data, with statistical significance set at p<.05. Results: All respondents reported prior experience with generative AI tools. The most frequently cited purposes were information retrieval (80.0%), and summarization and organization (57.1%). Reported benefits included time saving (65.7%), although concerns were noted regarding lack of accuracy (77.1%) and reliability (65.7%). Only 8.8% did not consider the direct use of AI-generated text in assignments as plagiarism, while 26.5% indicated they would provide citations. Sex-based comparisons showed that men were more likely than women to perceive a positive impact of AI on critical thinking, with higher expectations regarding future contributions of AI to learning, and stronger intention for active use. Conclusions: Although health science students actively integrated generative AI into their learning and generally perceived positive effects, appropriate actions regarding potential ethical concerns were limited. These findings underscore the need for AI ethics education and establishing clear institutional guidelines. In addition, the observed sex-based differences in technology acceptance suggest that instructional strategies should be aligned with the needs of diverse learner groups.

This paper presents a comprehensive review of performance evaluation and optimization (PEO) methodologies for sixth-generation (6G) networks. It systematically analyzes the key performance indicators (KPIs) defined by major standardization bodies such as ITU-R, 3GPP, IEEE, & TIA and industry fora such as 5G-PPP, 6G-RIC, NGMN, and 6G-IA. Beyond summarizing KPI targets, this study classifies evaluation tools and AI-driven optimization frameworks into a unified taxonomy, highlighting how artificial intelligence enables adaptive, sustainable, and cross-layer performance control. Comparative synthesis reveals that most AI-driven approaches remain simulation-centric, lack benchmark alignment across KPIs, and face challenges in scalability, trustworthiness, and real-time inference. The paper concludes by identifying research gaps in AI-driven PEO frameworks and emphasizing future directions toward autonomous, energy-efficient, and explainable 6G systems.

Purpose: Wordvice AI Proofreader is a recently developed web-based artificial intelligencedriven text processor that provides real-time automated proofreading and editing of user-input text. It aims to compare its accuracy and effectiveness to expert proofreading by human editors and two other popular proofreading applications?automated writing analysis tools of Google Docs, and Microsoft Word. Because this tool was primarily designed for use by academic authors to proofread their manuscript drafts, the comparison of this tool’s efficacy to other tools was intended to establish the usefulness of this particular field for these authors. Methods: We performed a comparative analysis of proofreading completed by the Wordvice AI Proofreader, by experienced human academic editors, and by two other popular proofreading applications. The number of errors accurately reported and the overall usefulness of the vocabulary suggestions was measured using a General Language Evaluation Understanding metric and open dataset comparisons. Results: In the majority of texts analyzed, the Wordvice AI Proofreader achieved performance levels at or near that of the human editors, identifying similar errors and offering comparable suggestions in the majority of sample passages. The Wordvice AI Proofreader also had higher performance and greater consistency than that of the other two proofreading applications evaluated. Conclusion: We found that the overall functionality of the Wordvice artificial intelligence proofreading tool is comparable to that of a human proofreader and equal or superior to that of two other programs with built-in automated writing evaluation proofreaders used by tens of millions of users: Google Docs and Microsoft Word.

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.

최근에 생성형 인공지능 활용에 관한 많은 연구가 진행되고 있다. Generative AI 도구와 플랫 폼 수량이 증가하고 있는 추세에 따라, Generative AI 도구는 텍스트와 이미지, 소리, 비디오, 그리고 3D 모델을 생성하는 기능을 가지고 있을 뿐만 아니라 프롬프트만으로도 아이디어와 콘텐츠를 포함하여 복잡한 작품을 생성할 수 있다. 본 연구는 Generative AI 도구와 플랫폼이 디지털 아트 전공학생들에게 미치는 영향을 탐구 하였으며, 특히 창작 과정에 그들의 역할과 작품에 미치는 영향을 구체적으로 살펴보았다. 또한, 본 연구는 Generative AI 작품에 의해 디지털 아트 전공 학생들에게 필요한 기술이나 특징에 대한 질적 연구법 중의 주체 분석 방법으로 자세히 연구하였다.

This study explored the impact of generative AI tools and platforms on digital art students, specifically exploring the influence on their roles and the overall creation process of their artworks, focused on lies in a comprehensive, qualitative survey involving twenty student participants with expertise in digital art. Therefore, utilizing rigorous thematic data analysis, the research revealed participants' perspectives on AI-generated content, including their attitudes towards this emerging technology, identifiable advantages and disadvantages, ethical considerations, and the anticipated future of AI in the realms of digital art.

Generative artificial intelligence (GenAI) tools such as ChatGPT and Grammarly have rapidly entered English as a Foreign Language (EFL) education, offering new opportunities for feedback and learning autonomy while raising ethical concerns. This exploratory study examined Vietnamese translation stu-dents’ perceptions of GenAI through the combined lenses of the Technology Acceptance Model (TAM) and Self-Determination Theory (SDT). A survey of 44 undergraduates measured self-report ratings on perceived usefulness (PU), perceived ease of use (PEOU), autonomy, competence, and ethical use using a 32-item Likert-scale questionnaire. Results revealed uniformly high self-perceptions across all con-structs, indicating strong subjective acceptance and confidence. Correlational analyses showed signifi-cant positive relationships among all variables, while regression analyses demonstrated that PU signif-icantly predicted competence and autonomy, and competence alone predicted ethical use. These find-ings suggest that students’ ethical engagement with GenAI stems primarily from their perceived com-petence rather than ease or usefulness alone. The study highlights a developmental shift from mere AI acceptance toward ethical empowerment, emphasizing competence-based ethics and autonomy-sup-portive pedagogy. Integrating TAM and SDT provides a holistic framework to understand how cogni-tive beliefs and motivational needs jointly shape responsible AI literacy in EFL education. Pedagogical implications include designing guided AI literacy tasks that transform technological utility into ethical capability.

Các công cụ trí tuệ nhân tạo tạo sinh (GenAI) như ChatGPT và Grammarly đang nhanh chóng được tích hợp vào giảng dạy tiếng Anh như một ngoại ngữ (EFL), mang lại cơ hội cho phản hồi và tính tự chủ trong học tập, đồng thời cũng đặt ra những vấn đề đạo đức. Nghiên cứu này khảo sát nhận thức của sinh viên chuyên ngành ngôn ngữ Anh tại Việt Nam về GenAI dựa trên Mô hình Chấp nhận Công nghệ (TAM) và Thuyết Tự Quyết (SDT). Khảo sát với 44 sinh viên thông qua bảng hỏi Likert 32 mục để tự đánh giá tính hữu ích (PU), tính dễ sử dụng (PEOU), tính tự chủ, năng lực và việc sử dụng có đạo đức. Kết quả cho thấy mức độ tự đánh giá cao ở tất cả các biến, phản ánh sự chấp nhận và tự tin mạnh mẽ của người học. Các phân tích tương quan chỉ ra mối quan hệ tích cực có ý nghĩa giữa các biến. Phân tích hồi quy cho thấy tính hữu ích dự đoán đáng kể năng lực và tính tự chủ, trong khi năng lực là yếu tố duy nhất dự đoán việc sử dụng có đạo đức. Những kết quả này cho thấy việc sử dụng GenAI có đạo đức chủ yếu xuất phát từ năng lực cảm nhận của người học, thay vì chỉ từ tính hữu ích hay tính dễ sử dụng. Nghiên cứu nhấn mạnh sự chuyển dịch từ chấp nhận công nghệ sang trao quyền đạo đức, đồng thời gợi ý thiết kế các nhiệm vụ học tập nhằm phát triển năng lực sử dụng AI một cách có trách nhiệm.

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 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.

The purpose of this study is to explore learners' and instructors' perceptions of AI-based technologies in higher education. 97 participants(learners: 61, L2 instructors: 36) participated in an online survey on academic integrity issues related to AI-based tools in educational settings. There were two research questions posed in the study. In the first question, five key factors were identified: attitudes toward AI-based tools, concerns about these tools, possible challenges, potential threats, and possible benefits. In relation to the second question, a one-way ANOVA revealed significant differences in perceptions concerning AI-based tools and potential threats, with learners and instructors showing distinct perspectives. Participants were keenly aware of the benefits and challenges of AI technologies, emphasizing the need for continuous training in their use. Consequently, in Korea, it calls for the development of flexible, culturally relevant guidelines that align with legal and regulatory standards, involving a range of stakeholders to ensure academic integrity. Research limitations and research directions were discussed further.

This study investigates the relationship between language learning strategy (LLS) preferences and AI writing tool use among 147 English as a Foreign Language (EFL) learners at a South Korean university. Participants were divided into two groups based on their preferred language learning strategies, as determined by the Strategy Inventory for Language Learning (SILL): memory/compensation strategies (Group 1) and metacognitive strategies (Group 2). The SILL survey was followed up with assessments on the participants’ use of AI writing tools and their reflections on how these tools were used in the writing process. Results revealed some minor correlations between specific strategy types and functions in AI writing tools— including preferences which suggest that metacognitive strategy users use AI writing tools for the development of ideas and structuring the writing process—but were limited due to individual learner variables which had not been measured. The findings provide directions for the consideration of language learning strategies when designing AI writing tools to create personalized and effective learning experiences.

The objective of this study was to identify the key themes and topics addressed in recent research on AI-assisted writing tools in English education. Additionally, the study aimed to examine the research methods and participant groups commonly used in the selected studies, explore the reported pedagogical benefits and challenges, and consider the implications of the findings for language teachers, curriculum designers, and policymakers. To collect data, journal articles were retrieved from the Research Information Service System(RISS), and a total of 16 articles were selected through a multi-stage screening process. The data were analyzed using constant comparison, thematic analysis, and inter-coder reliability to ensure the trustworthiness of the findings. Through this process, five key themes emerged. In terms of pedagogical impact, the findings highlight several benefits, including improvements in grammar, vocabulary, coherence, and overall writing quality—particularly among students who received AI-generated feedback. However, a major challenge identified is the risk of overreliance on AI, which may hinder the development of students’ independent writing and critical thinking skills. The study concludes with a discussion of implications for educational practice and policy, as well as the limitations of the current research.

Decentralized apps are now much more accessible thanks to AI-powered in-app support. Submenu after submenu is not required, nor is it necessary to guess what to click next. Come on, though, these helpful AI features bring with them some new problems. Code injection, data poisoning, and tricks that change what you see or click can all be successful. Prompt injection is a technique that attackers can use to give the AI malicious commands directly. In fact, this study explores the mechanisms behind these attacks. It looks at real-world situations, talks to security professionals, and sorts through the results of real security audits. Most problems show up directly in front of the user interface, whether it's malicious scripts or phony prompts.

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.

본 연구는 AI 도구를 활용한 음악 수업이 초등학교 3학년 학생들의 음악 수업 흥미와 자기효능감에 미치는 영향을 분석하고자 수행되었다. 연구는 전주시 소재 초등학교 3학년 학생들을 대상으로 사전 설문 조사 후 4차 시 음악 수업을 진행하고, 동일 문항의 사후 설문을 실시하는 방식으로 수행되었다. 설문 문항은 음악 수업 흥미 와 자기효능감을 5점 척도로 측정하였다. 연구 결과 AI 음악 도구를 활용한 음악 수업은 학생들의 음악 수업 흥 미와 자기효능감 모두에서 유의미한 향상을 보였다. 흥미도는 다수 문항에서 0.8점 이상 상승하였고, 자기효능감 역시 전반적으로 약 1점 증가하였다. 추가적으로 학생들의 디지털 경험 수준을 고려한 분석에서 디지털 경험이 낮은 집단이 높은 집단보다 더 큰 향상 폭을 나타냈으며, 특히 자기효능감에서 매우 큰 효과 크기가 확인되었다. 이는 디지털 친숙도가 낮은 학생에게도 AI 음악 도구가 접근 가능한 창작 환경을 제공하여 새로운 자신감 형성 에 기여함을 시사한다. 연구 결과는 AI 음악 도구를 활용한 음악 교육이 학생의 창의성, 흥미, 자기효능감 증진 에 효과적이며, 특히 디지털 경험 격차를 완화하는 교육적 전략으로 활용될 수 있음을 시사한다.

This study examined the impact of music lessons utilizing AI-based music tools on third-grade elementary students’ interest in music classes and their self-efficacy. The research was conducted with third-grade students at an elementary school in Jeonju, where a pre-test survey was administered prior to instruction, followed by four sessions of AI-integrated music lessons, and a post-test survey using the same questionnaire items. The survey measured students’ music class interest and self-efficacy on a five-point Likert scale. The results demonstrated significant improvements in both variables. Interest in music lessons increased by more than 0.8 points on many items, and self-efficacy showed an overall increase of approximately 1 point. Additionally, an analysis considering students’ digital experience revealed that the low-digital-experience group exhibited greater gains compared to their high-experience counterparts, with particularly large effect sizes observed for self-efficacy. This suggests that AI music tools provide an accessible creative environment even for students with limited digital familiarity, contributing to the development of new confidence. Overall, the findings indicate that incorporating AI music tools into music education is effective in enhancing students’ creativity, interest, and self-efficacy, and can serve as an educational strategy to help reduce gaps in digital experience.