AI 활용 진로교육에서 대학 신입생의 유용성 인식과 동기 요인이 만족도에 미치는 영향

Abstract

The purpose of this study was to examine the structural pathways through which perceived usefulness, effort, and value recognition influence satisfaction with AI use among first-year university students participating in AI-based career education. To achieve this aim, a mediation model integrating perceived usefulness from the Technology Acceptance Model(TAM) and the motivational constructs of effort and value recognition derived from Self-Determination Theory(SDT) was established. Survey data were collected from first-year students enrolled in a career exploration course. The findings revealed that perceived usefulness did not have a significant direct effect on satisfaction with AI use; rather, it exerted an indirect effect through effort and value recognition. Perceived usefulness significantly increased effort, and both effort and perceived usefulness positively influenced value recognition. Notably, value recognition emerged as the strongest predictor of satisfaction with AI use. These results indicate that satisfaction with AI in career education is shaped not merely by the technological usefulness of AI itself but predominantly by learners’ internal motivational processes. This study contributes to the field by examining not only the technological dimension of AI-based career education but also the psychological mechanisms underlying learners’ engagement and perception. The findings suggest that effective integration of AI into career education requires attention to both technological usefulness and the motivational factors that drive learners’ meaningful engagement.