KJCKorea
Journal Central

This study aimed to develop an training program for the BIBFRAME implementation in Korea through a literature review, case studies of BIBFRAME implementation processing and educational program at national libraries overseas, and interviews with staff in charge of BIBFRAME-related work at the National Library of Korea. As a result, a training program for BIBFRAME in Korea was developed as follows. First, the training was divided into one for BIBFRAME implementation, and another for cataloging librarians focusing on training of BIBFRAME editor after its development. Second, the training topics were designed by identifying topics based on an analysis of BIBFRAME implementation processing and training for cataloging librarians, and organizing them into six areas aligned with the core competencies for cataloging and metadata professional librarians by ALCTS. Third, the training program for BIBFRAME implementation includes the semantic web and linked data, conceptual models, RDA, and BIBFRAME ontology as well as the application profiles of ontology as a three-day course combining theory and hands-on practice. Fourth, the training program for cataloging librarians includes an introduction to the semantic web and linked data, BIBFRAME concepts and tools, an understanding of the BIBFRAME editor, and the BIBFRAME input practices as a two days course consisting of theory and practical exercises. The training program for BIBFRAME proposed in this study can be utilized in various ways as a training program to develop the essential competencies required of librarians for BIBFRAME implementation in Korea.

This study reviewed trends in revisions to MARC 21 following the 2023 revision of KORMARC, and analyzed the content from MARC 21 Discussion Papers proposed between 2022 and 2025 that had been incorporated into MARC 21 as of December 2025, in order to propose adoption strategies for KORMARC. The analysis revealed that revisions to MARC 21 from 2022 to 2025 are progressing in a direction that strengthens interoperability within a linked data environment through item-centric description, expansion of URI-based description, and relationship-centric description. Based on these findings, adoption strategies for fields, subfields, and indicators in the KORMARC formats for Bibliographic, Authority Control, and Holdings records were proposed in the following five categories. Clarification of transcription methods, title context, scope of materials, and description of applying institutions. Clarification of description for digital archive information, electronic resources, and various material types. Ensuring consistency of description elements between control fields and variable-length data fields. Expansion of identifier and URI-based description, along with LRM-based structured description of source information. Granular specification of relationships between entities such as persons, corporate bodies, and families.

The rapid spread of generative artificial intelligence(Generative AI) is reshaping how people seek and process information, increasing the need for AI literacy education. This study aims to identify the key elements of AI literacy education by examining the output generation structure of generative AI systems. To achieve this goal, the study theoretically analyzes how generative AI operates and produces responses, and explores how the structural characteristics of the generation process influence users’ information judgment. The analysis suggests that outputs produced by generative AI should not be understood merely as information delivery; rather, they represent discursive reconstructions influenced by probabilistic computations and platform-level design considerations. Drawing upon these structural insights, the study proposes several critical elements for AI literacy education within generative AI contexts: (1) input structure literacy, which involves recognizing the structure of prompts and problem framing; (2) computational process literacy, involving comprehension of computational procedures such as tokenization and embedding; (3) generation principle literacy, focused on grasping the probabilistic foundations underpinning text generation; (4) response structure literacy, which refers to understanding how responses are organized and presented; (5) platform structure literacy, emphasizing awareness of the mediating influence of platform-level design; and (6) critical evaluation literacy, highlighting the capacity to critically assess generated outputs. By systematically organizing these elements in relation to the output structure of generative AI, this study contributes to the conceptual framework of AI literacy education and suggests directions for AI literacy education in generative AI environments.