This study analyzed faculty perceptions of the Major Competency Achievement Evaluation System at K University using the Concerns-Based Adoption Model (CBAM). An adapted Stages of Concern Questionnaire was administered, and data were analyzed with SPSS 26.0. Findings revealed that most faculty were in early awareness stages (0-2), underscoring the need to improve understanding of the system's goals and benefits. A notable proportion exhibited high Stage 6 (Refocusing) and Stage 4 (Consequence) concerns, indicating potential change agents for system enhancement and dissemination. Background variable analysis showed that faculty without prior training or administrative experience require targeted support. Additionally, a "tail-end uplift" pattern-where collaboration and improvement concerns rose despite low consequence concerns-suggests the effectiveness of stage-specific dissemination strategies.

This study aims to develop a competency assessment model that systematically verifies the skills of learners completing bootcamp-style advanced industry programs and certifies their achievements through digital badges. Initially, core competency elements and evaluation components pertinent to the advanced industry sector were identified through literature reviews and expert consultations. Following this, a prototype assessment model was designed featuring a dual structure: the "Curriculum Validation System" and the "Digital Badge Issuance System." Expert validation was then conducted to assess the model's alignment with its purpose, structural validity, evaluation criteria effectiveness, and practical applicability. The results indicate that key factors enhancing the model's effectiveness include demand-driven, multi-level evaluation criteria, project-based assessments linked to job competencies, and an operational framework integrated with industry partners. This study contributes to engineering education and competency certification by providing an empirical foundation for expanding the model to other emerging industries, such as secondary batteries and display technologies, and for enhancing the practical use of digital badges in workforce development.

This study reports on the design, implementation, and evaluation of an English-medium interdisciplinary liberal arts course for STEM undergraduates that integrated English language learning with critical engagement in the sociopolitical and ethical dimensions of science and technology. Offered at a Korean science and engineering institute, the course employed science fiction(SF) literature as the primary medium to foster sustained reading, discussion, and writing in English, while encouraging critical reflection on the societal implications of technological developments. Key pedagogical components included self-directed, theme-based reading of three SF works, literary response journals, group presentations, role-play activities, and a literary analysis essay. Data from the end-of-semester survey and analysis of student work indicated perceived improvement in all English skill domains and enhanced capacity to reflect critically on technology's role in society. The findings suggest that SF can serve as an effective vehicle for both language learning and interdisciplinary inquiry, addressing common limitations of English-medium instruction(EMI) in the Korean context by promoting active participation, collaborative learning, and meaningful discourse. The study concludes with recommendations for adapting similar courses to students with diverse proficiency levels and for expanding future applications to include a wider range of SF works and instructional approaches.

This study aims to evaluate the educational effectiveness of a natural language-based content recommendation system designed for elementary software (SW) education. The proposed system recommends personalized learning content by analyzing students' free-text queries and Entry Test results. It utilizes a dual recommendation mechanism: one based on semantic similarity using a pre-trained KoBERT model, and the other based on meta-learning through a Model-Agnostic Meta-Learning (MAML) algorithm. To assess its impact, a total of 40 elementary school students participated in a 4-session program, during which they interacted with the recommendation chatbot and completed assigned coding tasks. Both pre/post-intervention surveys were conducted to measure changes in learning motivation, engagement, and perceived self-efficacy. The results indicate that students exhibited increased interest and self-directed learning behavior, particularly when recommendations were aligned with their input queries and prior knowledge. The findings support the effectiveness of a natural language-based personalized recommendation approach in fostering engagement and adaptive learning in elementary SW education.

This study proposes an educational model based on generative artificial intelligence tools such as ChatGPT to support an aeronautical experiment course at the Republic of Korea Air Force Academy. To foster self-directed learning and enhance experimental design competencies, the model incorporates prompt-based worksheets and simulation-based experimental design activities. The proposed model was designed for application to wind tunnel experiments, with the aim of improving cadets' ability to integrate theoretical knowledge with laboratory practice, while also strengthening their critical thinking and creative problem-solving skills. The model developed in this study will be implemented in a regular course during the fall semester of 2025, and its pedagogical effectiveness will be evaluated through empirical analysis of the learning outcomes.