대학생의 인간-AI 협력 역량 프로파일 탐색
Latent profiles of human-AI collaboration competency among college students
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본 연구는 대학생의 인간-AI 협력 역량에 따른 잠재 프로파일을 탐색하는 데 목적이 있다. 또한, 이러한 프로파일이 학습자의 개인적 특성(성별, 연령, 학년, 전공의 AI 관련성)에 따라 어떻게 예측되는지, 그리고 프로파일 유형에 따라 AI 관련 진로 선택과 연관된 심리적 변인들(자기효능감, 결과기대, 흥미) 에 어떤 차이가 나타나는지 분석하고자 하였다. 이를 위해 서울 소재 국립 종합대학교에 재학 중인학부생 418명을 대상으로 설문조사를 실시하였다. 인간-AI 협력 역량의 구성 요소인 AI 이해, 인간-AI 상호작용, AI 윤리와 가치, 인간-AI 협력에 대한 태도를 기준으로 대학생의 잠재 프로파일은 ‘저역량․부정적 태도’와 ‘평균역량․긍정적 태도’의 두 가지 유형으로 도출되었다. 또한, 인간-AI 협력 역량의 핵심 요소인 인간-AI 상호작용에 중점을 둔 추가 분석에서는 ‘상호작용 미흡’, ‘소통 미흡․조절우수’, ‘소통 우수․조절 미흡’, ‘상호작용 우수’의 네 가지 잠재 프로파일이 도출되었다. 프로파일과영향변인 간 관계를 분석한 결과, 연령이 낮고 전공이 AI와 관련성이 높을수록 더 긍정적인 프로파일에 속할 가능성이 컸다. 또한, 이들 프로파일은 AI 관련 진로 선택과 연관된 심리적 결과변인에서 모두 유의미한 차이를 보였다. 본 연구는 대학생의 인간-AI 협력 역량 강화를 위해 AI 기술에 대한 이해뿐만 아니라, AI와의 상호작용을 긍정적으로 인식하도록 유도하는 교육적 접근이 필요함을 강조하며, 이러한 역량이 AI 관련 진로 선택에 중요한 영향을 미칠 수 있음을 시사한다.
Abstract
The purpose of this study is to explore potential profiles of human-AI collaboration competencies among college students. We also aim to analyze how these profiles are predicted by learners' personal characteristics (gender, age, grade, and AI relevance of their major) and how profile types differ in psychological variables (self-efficacy, outcome expectations, and interest) associated with AI-related career choices. To do so, we surveyed 418 undergraduate students at a national university in Seoul. Based on the components of human-AI collaboration competency (AI understanding, human-AI interaction, AI ethics and values, and attitudes toward human-AI collaboration), two latent profiles of undergraduate students were derived: Low Competency․Negative Attitudes and Average Competency․Positive Attitudes. Further analysis focused on human-AI interaction, a key component of human-AI collaboration competence, and four latent profiles were identified: Low Interaction, Low Communication․High Regulation, High Communication․Low Regulation, and High Interaction. Results also indicated that younger age and more AI-related majors were more likely to belong to the more positive profile. Furthermore, these profiles also showed significant differences in psychological outcome variables related to AI-related career choices. Our findings suggest the need for educational approaches that not only develop students' understanding of AI technologies but also their positive perceptions of interacting with AI, which may have important implications for their choice of AI-related careers.
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