인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
This study aims to evaluate the educational impact of integrating generative AI tools into architectural theory courses by analyzing student learning outcomes and experience. The research applied the ADDIE model (Analyze, Design, Develop, Implement, Evaluate) to develop and implement a generative AI-integrated curriculum in a first-year architecture theory class. The study examined four key aspects through quantitative and qualitative methods: (1) understanding the relationship between architectural forms and language, (2)ease of use, (3)perceived advantages, and (4)perceived limitations of AI tools. The results showed that 78% of the students reported a positive impact on their understanding of architectural forms and language with an average satisfaction score of 3.84 out of 5. The ease of use was highly rated, with 84% of the students expressing satisfaction and an average score of 4.20, highlighting the intuitive nature of AI tools in architectural design visualization. Student feedback further identified four major advantages of using AI tools: 1)Efficient Image Creation and Time Savings, 2)Concrete Visualization of Abstract Concepts, 3)Creative Inspiration and Idea Generation, and 4)High-Quality Visual Representation. However, key limitations included 1)Need for Precise Commands and Limited Expression, 2)Image Distortion and Unreliability of Results, 3)Creativity Inhibition and Idea Constraints, 4)Lack of Design Control, and 5)Copyright and Ethical Concerns. Overall,ᅠthe integration of generative AI in architectural education was found to significantly enhance students' learning experiences by facilitating conceptual understanding and creative exploration. However, addressing specific challenges such as command precision, design control, and ethical considerations is crucial for maximizing its educational potential. This study offers a foundation for refining AI-integrated educational programs and suggests future research directions focusing on diverse student groups, broader AI tools, and long-term learning impacts.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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