| Title |
Innovation and Performance Analysis of Architectural Theory Education Using Generative AI |
| DOI |
http://doi.org/10.14774/JKIID.2024.33.5.045 |
| Keywords |
Generative Artificial Intelligence; Architectural Theory Education; AI Integrated Educational Model; Educational Effectiveness Evaluation |
| Abstract |
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. |