This study conducted a meta-analysis to investigate the impact of AI convergence education on students' cognitive and affective outcomes. A total of 96 effect sizes were extracted from 26 domestic studies published between 2010 and 2024. The overall effect size (Hedges' g) was .653, indicating a moderate positive effect on academic achievement and affective factors such as attitudes and motivation. We performed moderator analyses to identify the conditions under which AI convergence education is most effective. The effects were more pronounced among elementary school students and in programs lasting more than 13 sessions. Science and mathematics subjects yielded the highest effect sizes, and among technological features, chatbot-based applications demonstrated the greatest impact. We also found that personalized learning approaches significantly enhanced educational outcomes. Through this study, we made a unique contribution by systematically synthesizing over a decade of domestic research and empirically identifying key moderators-educational level, duration, subject domain, and technological characteristics-that influence the effectiveness of AI convergence education. Our findings offer practical implications for the optimal design and implementation of AI-based programs, emphasizing the need for structured and personalized instructional approaches.

As digital transformation accelerates, coding competency has emerged as a foundational literacy across educational contexts. Simultaneously, concerns over ethical judgment in programming practices have grown, especially with the widespread use of generative AI tools (e.g., ChatGPT, Copilot) and open-source platforms (e.g., GitHub). This study investigates how university students' ethical judgment structures are shaped by their coding comprehension, open-source practice, and ethical awareness. Additionally, it examines how these factors relate to students' self-reported coding misconduct and their perceived legitimacy of open-source use. Using data from 224 undergraduates enrolled in digital technology courses, we applied structural equation modeling to test the hypothesized model. Findings indicate that coding comprehension significantly predicts ethical awareness, while open-source practice does not. Surprisingly, higher ethical awareness was associated with an increased likelihood of misconduct experience, suggesting a post hoc formation of ethical reflection or a cognitive-action discrepancy. Ethical awareness also significantly predicted the perceived illegitimacy of open-source use in coding tasks, but no interaction effect was found between open-source practice and ethical awareness. These results highlight the complex, dynamic nature of ethical judgment in digital learning environments. The study provides empirical grounding for designing ethics education that integrates technical understanding with reflective learning, emphasizing post-behavioral ethics development in coding contexts.

This study aimed to analyze the effects of learners' participation experiences in experimental classes utilizing VR and to determine whether it could be universally expanded. To this end, this study investigated the relationships between variables that determine the effectiveness of experimental classes using VR and suggested teaching and learning strategies to consider in classes.

The purpose of this study is to give implications for designing and improving effective employment support education, through a comparative analysis of educational needs among members of universities' employment support activities. To this end, current students, graduates, and professors of T University were surveyed on the importance and performance of 22 employment support items(programs). And Borich's needs and The Locus for Focus model were applied to derive the top educational requirements for each of the three groups. As a result of the analysis, items such as 'acquiring major-related certificates', 'trend reflection education' and 'strengthening interview skills' for current students, 'self-understanding' and 'strengthening interview skills' for graduates were the top educational 'requirements, and there was confirmed a common demand between the two groups. In the case of professors, self-understanding', 'support for cultivating major knowledge', 'development of communication competencies such as PT competencies' were derived, and there was a common demand with graduates, but the results were different(gap) from those of current students. This study is meaningful in that it sought ways to improve effectiveness by revealing differences in employment support educational needs among current students, graduates, and professors.

This study aims to develop an operational model for implementing microdegree programs that enhance convergence education at W University. Focusing on academic systems, curriculum design, and institutional operations, the model proposes flexible credit structures and certification systems that align with existing academic regulations while addressing the needs of emerging industries. In particular, the academic framework was systematized based on the CIPP evaluation model, enabling a comprehensive structure from planning and implementation to feedback and improvement. Based on the ADDIE model, the curriculum framework emphasizes modular, competency-based learning tailored to student career pathways. The study presents a case application in the AI Convergence major, providing a detailed example of course structure and planning. To support implementation, an operational framework combining departmental autonomy with central support was designed. Expert validation confirmed the model's feasibility, and recommendations include encouraging departmental engagement, ensuring flexible credit requirements, and integrating co-curricular and online content. Unlike previous conceptual studies, this research presents an actionable model supported by a practical planning template and institutional alignment strategy. The findings contribute to institutional efforts to establish microdegrees not only as short-term credentials but as structural tools for realizing student-driven, convergence, and competency-based education.