AI 기반 고등교육 환경에서 대학 교수자의 디지털 역량 구성과 활용 방식 연구 : 체계적 문헌 고찰과 내용분석의 혼합적 적용

초록

Research topics: This study aims to analyze how university instructors’ digital competence has evolved and been restructured within AI-based higher education environments, and to explore its core components and functional operating structure. Research background: Recent advances in generative AI and multimodal learning analytics have significantly transformed teaching and learning in higher education. However, prior studies have largely focused on K–12 teachers or on ICT tool usage, failing to capture the integrated competence structure required in AI-driven higher education. Although recent research highlights a shift in instructors’ roles from technology users to data-informed decision-makers, systematic analyses of this transformation remain limited. Differences from prior research: Unlike previous studies that present digital competence as fragmented components or framework-dependent lists, this study integrates a systematic literature review and content analysis to identify the relative emphasis of competence elements and their temporal evolution. It further conceptualizes digital competence as a structurally interconnected and cyclical system embedded in teaching practices. Research method: A mixed-method approach was employed. A total of 97 studies published between 2015 and 2026 were selected using the PRISMA procedure. Content analysis was conducted based on six competence domains—understanding, design, implementation, analysis, reflection, and expansion—derived from DigCompEdu, AI-TPACK, and UNESCO’s AI competency framework. Frequencies and patterns were analyzed to identify structural characteristics and temporal changes. · Research results: The findings indicate that digital competence has evolved through three stages: ICT-centered competence (2015–2018, 19.6%), AI-centered competence (2019–2021, 26.8%), and data-driven teaching expertise (2022–2026, 53.6%). Digital competence consists of six functional domains, with the “analysis” domain (20.6%) showing the highest proportion. The results also reveal that these domains operate as a cyclical system—understanding, design, implementation, analysis, reflection, and expansion—reflecting the dynamic nature of AI-based teaching practices. Contribution points and expected effects: This study contributes theoretically by reconceptualizing digital competence as a dynamic and cyclical system centered on data-informed teaching. Methodologically, it demonstrates the value of combining systematic literature review and content analysis for identifying structural patterns in competence research. Practically, the findings provide a foundation for developing competence assessment tools, designing faculty development programs, and establishing AI-based teaching support systems in higher education.

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