디자인 전공 수업에서 GenAI 디자인 도구 활용에 대한 학습자 인식과 평가 -혁신기술수용모델 관점-
Learners’Perceptions and Evaluations of GenAI Design Tools in Design Education -a technology acceptance model perspective-
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
본 연구는 디자인전공 수업에서 GenAI 디자인 도구 활용에 대한 학습자들의 수용 및 지속 사용 인식을 파악하여 교육적 활용 전략을 모색하고자 하였다. 이를 위해, 한 학기 동안 학습자들이 다양한 GenAI 디자인 도구를 익혀 실습프로젝트에 적용한 뒤, TAM2(기술수용모델2)의 구성 요소를 토대로 재정의한 7개 항목(필요성 인식, 아이디어 발상, 심미성, 표현 원활성, 학습 효과성, 작업 효율성, 지속사용의도)에 대해 인터뷰 조사를 시행하고 사용경험에 따른 반응의 변화를 분석하였다. 분석 결과, 첫째, 필요성 인식과 지속사용의도는 사용경험 정도와 관계없이 높게 나타났으나, 부정적 견해를 지닌 학습자는 지속사용의도에 대해서도 소극적인 태도를 보였다. 둘째, 사용 초기에 심미성과 표현 원활성 만족도는 낮았으나, 경험 축적 후 작업 효율성과 함께 유의미한 향상을 보였다. 그럼에도 이 두 항목은 다른 요소들보다 낮은 만족도를 유지하였다. 인터뷰에서는 조형 표현을 위한 언어적 묘사의 어려움, 제어 불가능성, 개성 부족 등이 만족도 저하 요인으로 지적되었다. 셋째, 학습 효과성은 초기부터 중간 정도로 나타났고 숙련 후에도 큰 변화 없이 유지되었으나, 작업 효율성은 경험 축적에 따라 크게 증가하였다. 이는 GenAI 디자인 도구가 효율성을 높이는 반면에, 직접적 조형 경험을 중시하는 교육 맥락에서 정서적 몰입과 디자인전공 학습자의 창작 경험을 약화시킬 수 있음을 시사한다. 따라서 디자인 교육 현장에서 GenAI 활용 전략을 재고하고, 도구 활용 과정에서 창의적 표현 주체성을 유지하며 효율성을 극대화할 수 있는 디자인 교육 과정 및 교육 모델을 개발할 필요가 있다.
Abstract
This study aimed to examine learners’ acceptance and perceptions regarding the continuous use of AI Design Tools (GenAI DT) in design-major courses, and to explore strategies for their educational implementation. Over the course of one semester, students learned to use various GenAI DT and applied them to a practical project. Subsequently, an interview survey was conducted to analyze changes in their responses based on usage experience. Seven evaluation items were derived from the components of the Technology Acceptance Model 2 (TAM2) and redefined accordingly. The analysis revealed three key findings. First, perceived necessity and intention to continue use were consistently high regardless of the degree of usage experience. However, those harboring negative views remained reluctant about continued use even after gaining experience. Second, while satisfaction with aesthetics and expressive fluency was initially low, it improved significantly alongside work efficiency as learners became more accustomed to the tools, yet these two items still lagged behind others in terms of satisfaction. The interviews indicated that difficulties in verbalizing form-based expressions, lack of control, and insufficient individuality were identified as factors that reduced satisfaction. Third, although learning effectiveness remained at a moderate level from the beginning and showed little change even as learners became more proficient, work efficiency increased markedly with accumulated experience. These findings suggest that while GenAI DT can enhance efficiency, they may also reduce emotional engagement and the sense of creative involvement among design-major students, especially in educational contexts that emphasize hands-on, formative design processes. Consequently, there is a need to reconsider strategies for integrating GenAI DT into design education and to develop educational models and content that maintain creative autonomy while maximizing efficiency.
참고문헌 (31)
- Agarwal, R., "Time flies when you’re having fun : Cognitive absorption and beliefs about information technology usage", MIS Quarterly, 24(4), pp.665~694 (2000)[DOI]
- Ajzen, I., "A Bayesian analysis of attribution processes", Psychological Bulletin, 82(2), pp.261~277 (1975)[DOI]
- "Theatre D’opera Spatial" (2022)[URL]
- Bang, J., "Analysis of Impact of Generative Artificial Intelligence Technology on Product Design Process", 2023 KSDS SDC Fall International Conference, pp.46~51 (2023)
- Bhattacherjee, A., "Understanding information systems continuance : An expectationconfirmation model", MIS Quarterly, 25(3), pp.351~370 (2001)[DOI]
- Cheon, S., "Educational application of ChatGPT : Its impact on Korean university students’English speaking skills", The Journal of Linguistic Science, 107, pp.469~496 (2023)[DOI]
- Choi, E., "A Study on the Utilization and Implications of Expression Education in After Effects Using Generative AI, with a Focus on ChatGPT", Cartoon and Animation Studies, 74, pp.569~602 (2024)[DOI]
- Choi, S., "Automated Generation of 3D Rendering Graphic Floor Plan based on Gen AI", Korean Institute of Spatial Design, 18(7), pp.263~272 (2023)[DOI]
- Chung, E., "Direction for AI-based Tools to support Designers’Work Process", Archives of Design Research, 35(4), pp.269~282 (2022)[DOI]
- Davis, F. D, "A technology acceptance model for empirically testing new end-user information systems. Theory and Results", doctor, pp.1986
- Davis, F. D., "Perceived usefulness, perceived ease of use, and user acceptance of information technology", MIS Quarterly, 13(3), pp.319~340 (1989)[DOI]
- Goodfellow, I., "Generative adversarial networks", Communications of the ACM, 63(11), pp.139~144 (2020)[DOI]
- Han, S., "Consideration of Writing Teaching Methods Using ChatGPT and Investigation of Learner Responses and Perceptions", The Journal of General Education, 26, pp.43~75 (2024)[DOI]
- Hwang, Y., "The Usage of Generative AI in Poster Design", Archives of Design Research, 36(4), pp.291~308 (2023)[DOI]
- "The Next Rembrandt" (2016)[URL]
- Jho, H., "Understanding of Generative Artificial Intelligence Based on Textual Data and Discussion for Its Application in Science Education", Journal of the Korean Association for Science Education, 43(3), pp.307~319 (2023)
- "Generative model" (2016)[URL]
- "Davos, The Future of Jobs Report: Women Are More Affected by Declining Jobs" (2016)[URL]
- Kim, Y., "Exploring the Potential of Generative AI in UX Practice : A Case Study on Scenario Development for Large Indoor Facility Robots", Design Works, 7(1), pp.29~44 (2024)[DOI]
- Kwon, D., "Analysis of Prompt Elements and Use Cases in Image-Generating AI : Focusing on Midjourney, Stable Diffusion, Firefly, DALL·E", Journal of Digital Contents Society, 25(2), pp.341~354 (2024)[DOI]
- LeCun, Y., "Deep learning", Nature, 521(7553), pp.436~444 (2015)[DOI]
- Lee, S., "Exploration of Domestic Research Trends on Educational Utilization of Generative Artificial Intelligence", The Journal of Korean Association of Computer Education, 26(6), pp.15~27 (2023)[DOI]
- Oh, I., "A Study on the Application of A. I in Industrial Design-Focusing on the Problems of A. I Utilization and Educational Needs of College Students-", The Korean Society of Science & Art, 42(2), pp.199~209 (2024)[DOI]
- Hyu-Yong Park, "The Possibilities and Limitations of Generative AI Image Conversion Tools and Their Implications for Design Education", The Journal of Korean Association of Computer Education, 26(5), pp.155~170 (2023)[DOI]
- Ryu, J., "Design of Generative AI Fine-Tuning Process for Brand Logo Design-Focusing on the Use of DALL-E", Design Works, 7(2), pp.61~75 (2024)[DOI]
- Shin, S., "Creativity Education of Design Students in the Era of Generative AI:A Design Student Perspective", Proceedings of 2024 KSDS Spring International Conference, pp.132~133 (2024)
- Venkatesh, V., "A theoretical extension of the technology acceptance model : Four longitudinal field studies", Management Science, 46(2), pp.186~204 (2000)[DOI]
- "The Future of Jobs Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution. Global Challenge Insight Report" (2016)[URL]
- Yi, Y., "An Exploratory Experiment Using ChatGPT in the Idea Generation Process for Product-Service System", Archives of Design Research, 36(4), pp.271~288 (2023)[DOI]
- You J., "A Comprehensive Review of Technology Acceptance Model Researches", Entrue Journal of Information Technology, 9(2), pp.31~50 (2010)
- "What is machine learning?"[URL]