Z세대의 AI 학습 전략: Kolb 학습유형을 통한 맞춤형 접근

Abstract

Following the COVID-19 pandemic, the rapid expansion of remote learning environments has accelerated the adoption of Generative Artificial Intelligence (Gen AI) as a learning support tool in higher education. Despite its widespread use, however, universities still lack clear institutional guidelines and systematic educational strategies for AI integration, while students’ reliance on AI tools continues to deepen. In response, this study empirically investigates the current patterns of Gen AI usage among Generation Z (Gen Z) university students born between 1995 and 2006. Grounded in Kolb’s Experiential Learning Theory, the study classifies students’ learning types and proposes customized AI education strategies tailored to each type. An online survey was conducted with 151 university students, and to enhance the validity of the findings, in-depth interviews were additionally carried out with four experts in management information systems and education. The survey results indicate that the Diverger learning type accounted for the largest proportion (43.3%), followed by Accommodators, Assimilators, and Convergers. Based on these findings, students’ AI usage orientations were conceptualized as AI Explorers, AI Actors, AI Conceptualizers, and AI Implementers, respectively. For each group, differentiated learning strategies were proposed by considering their distinctive strengths and limitations. This study contributes to the literature by systematically categorizing and interpreting Gen Z university students’ learning behaviors through the integration of Gen AI usage with learning type theory. Furthermore, it offers practical implications for higher education by emphasizing the need for institution-level strategies that strengthen ethical awareness and critical engagement with AI, as well as for instructor-centered AI pedagogical design guidelines.