대학생의 AI 관련 진로목표에 영향을 미치는 요인: 성별과 지각된 전공 관련성의 조절효과를 중심으로

초록

This study aimed to explore the career decision-making process of university students in the field of Artificial Intelligence (AI) from the perspective of Social Cognitive Career Theory (SCCT). Specifically, it examined the effects of AI-related educational experiences on self-efficacy, outcome expectations, career interest, and career goals. Descriptive statistical analyses and structural equation modeling (SEM) were conducted, including multi-group analyses to investigate the moderating effects of gender and perceived major relevance. The results revealed that AI-related educational experiences had a positive influence on self-efficacy and career interest, whereas their effect on outcome expectations was not statistically significant. Gender-based analysis showed that the influence of career interest on career goals was stronger among male students. Additionally, analysis based on the level of perceived major relevance indicated that self-efficacy had a greater impact on career goals in the high-relevance group, while the indirect effect of outcome expectations on career goals via career interest was significant only in the low-relevance group. These findings generally support the core structure of the SCCT while suggesting that the strength and significance of its pathways may vary depending on the specific context of AI. The result of this study emphasizes the need for tailored career education approaches that consider gender and perceived major relevance in emerging technology fields such as AI. In particular, it highlights the educational implication that stimulating outcome expectations in students with low major relevance can enhance their career motivation and help them establish clearer career goals.

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