대학 수업의 생성형 AI 활용을 위한 교수의 교육요구 분석

Abstract

Objectives The purpose of this study is to analyze the educational needs of professors for the educational use of generative AI in universities.
 Methods Data from 126 professors at K University in Seoul were collected online. The prioritization of educational needs in instructional design, teaching methods, and class evaluation areas was determined through reliability analysis, factor analysis, paired-sample T-tests, Borich's Needs Assessment, and The Locus for Focus Model.
 Results Among the areas of instructional design, teaching methods, and class evaluation, instructional design emerged as the highest in educational needs. Subfactors revealed that in instructional design, the ‘development of instructional materials’ and the ‘organization of instructional content’ were prioritized. In teaching methods, ‘project-based learning’ and ‘team-based learning’ took precedence. In class evaluation, ‘report assessment’ and ‘descriptive assessment’ rankings were high.
 Conclusions To enhance the integration of generative AI in university classes, prioritized support in the field of instructional design is essential. Specific support measures are proposed based on the research findings.