Q 방법론을 적용한 대학생의 생성형 AI 활용 인식 유형과 교양 교육적 함의 = Types of University Students’ Perceptions of Generative AI Use and Their Implications for General Education: A Q-Methodological Study

다국어 초록 (Multilingual Abstract)

· Research topics: This study aimed to identify and analyze types of university students’ subjective perceptions of generative AI use through Q methodology and to explore directions for AI literacy education in general education.
· Research background: Although the use of generative AI has become increasingly prevalent in university students’ learning and everyday lives, prior studies have tended to examine overall usage patterns and average attitudes or to focus on specific tools, activities, courses, and issues. Consequently, they have not sufficiently explained how diverse usage contexts and perceptual elements are combined within individual perspectives.
· Differences from prior research: This study used educational and non-educational everyday contexts, use strategies, motivations, and concerns as domains guiding the construction of the Q sample. In doing so, it differs from previous research by identifying more complex types of university students’ subjective perceptions of AI use.
· Research method: A Q sample comprising 31 statements was constructed through a literature review and in-depth interviews with university students. Q sorting was conducted with 32 students at University A. The collected data were analyzed using principal component analysis with varimax rotation in PQMethod 2.35.
· Research results: Of the 32 participants, 31 were classified into three types: the Self-Control Confidence Type, the Pragmatic Caution Type, and the Skeptical Distrust Type. Together, the three types explained 50.5% of the total variance. They differed in their perceptions of control over AI use, overreliance, trust in human competence and information reliability.
· Contribution points and expected effects: By demonstrating that university students’ perceptions of generative AI use are not homogeneous, this study provides an empirical basis for designing common AI literacy content and type-specific instructional emphases in general education classrooms where students with diverse perceptions learn together.