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생성형 디자인 도구에 대한 시각디자인 학습자의 전환의도

Visual Design Learners’ Switching Intention to Use the Generative Design Tool

김아람
한국기초조형학회 (Korean Society Of Basic Design & Art)|2024.12|기초조형학연구 (Journal of Basic Design & Art)|제25권 제6호|pp. 97-114|DOI 10.47294/KSBDA.25.6.8

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키워드

artificial intelligenceswitching intention인공지능 기술전환 의도시각디자인generative design생성형 디자인visual design학습자 수용learner acceptance
주제분류예술일반

초록

본 연구는 인공지능 기술 기반의 생성형 디자인 도구에 대한 시각디자인 학습자의 전환의도 형성 과정을 탐색함에 그 목적이 있다. 생성형 디자인 도구는 전통적인 디자인 교육 방식에서 벗어나 창의적인 디자인 학습 환경을 구축하며, 학습자의 디자인적인 문제 해결 능력을 강화하는데 중요한 역할을 담당할 것으로 여겨진다. 그러나 생성형 디자인 도구에 대한 시각디자인 학습자의 사용 의도를 평가한 연구의 사례는 관찰되고 있지 않다. 본 연구는 연구모형과 연구가설의 설계를 위하여 인공지능 장치 사용자 수용 수정모형(MAIDUA)을 이론적 프레임워크로 선정하였다. 구체적으로, 본 연구는 생성형 디자인 도구에 관하여 학습자가 인식하는 실용적 동기와 상호작용 편의성, 작업-기술 적합성이 생성형 도구로부터 지각되는 역량에 긍정적인 영향을 미칠 것으로 예상하였다. 또한, 지각된 역량은 학습자가 체험하는 플로우 경험에 영향을 미치며, 플로우는 학습자의 전환의도에 부정적인 영향을 미칠 것으로 예측하였다. 본 연구는 우리나라 특성화 고등학교에 재학 중인 시각디자인을 학습하는 272명의 고등학교 학생으로부터 자료를 수집하였다. 수집된 자료에 대하여 PLS(partial least square) 기반의 구조방정식 모형분석을 수행하여 연구 가설을 통계적 유의수준에서 검증하였다. 본 연구의 주요 결과는 다음과 같다. 첫째, 지각된 역량에 미치는 실용적 동기의 영향은 유의하지 않은 것으로 나타났다. 둘째, 상호작용 편의성과 작업-기술 적합성은 모두 지각된 역량에 유의한 정(+)의 영향을 미치는 것으로 나타났다. 셋째, 지각된 역량은 플로우 경험에 유의한 정(+)의 영향을 미치는 것으로 나타났다. 넷째, 플로우 경험은 학습자의 전환의도에 유의한 부(-)의 영향을 미치는 것으로 나타났다. 이와 함께, 본 연구가 활용한 MAIDUA는 생성형 디자인 도구에 대한 학습자 전환의도의 총변동을 26.2% 설명하는 타당한 예측모형으로 판단할 수 있다.

Abstract

The present study aims to explore the process by which visual design students develop switching intentions toward AI-based generative design tools. Generative design tools are considered to play a key role in moving away from traditional design education methods, creating a more creative design learning environment, and enhancing learners' design problem-solving abilities. However, there are no studies evaluating the acceptance of visual design learners to use generative design tools. Most importantly, this work was framed using the Modified Artificially Intelligent Device User Acceptance (MAIDUA) as a theoretical framework. In the present work, a series of research hypotheses were designed by relating six study constructs of utilitarian motivation, interaction convenience, task-technology fit, perceived competence, flow, and switching intention. The conceptual model was validated on survey data collected from two-hundred and seventy two high school students enrolled in visual design programs at specialized high schools in South Korea. A PLS(partial least square)-based structural equation modeling analysis was conducted to verify the conceptual model and five research hypotheses. A scale refinement process was conducted on the measures of measurement items, and a confirmatory factor analysis was performed to assess the construct validity of measurement model. The results from a path analysis indicate that the impact of utilitarian motivation on perceived competence was not statistically significant. However, two constructs of interaction convenience and task-technology fit emerged as positive and significant predictors of perceived competence. Furthermore, perceived competence was found to be significantly predictive of flow experience, which in turn, have negatively and significantly impact on switching intention. In addition, MAIDUA is a valid predictive model, explaining 26.2% of the total variance in switching intentions toward AI-based generative design tool.

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