Transforming Design Education with AIGC: A Fuzzy AHP and Cloud Model Evaluation Framework

The fast evolution of Artificial Intelligence Generated Content (AIGC) is remaking higher education, especially in design disciplines where creativity and technology come together. This study looks into the main factors that affect how design educators take up AIGC tools and puts forward an evaluation framework for AIGC-assisted teaching platforms. Using a four-dimensional model which includes technical functionality, teaching support, role adaptability, and ethical risk perception, this research makes use of the Fuzzy Analytic Hierarchy Process (FAHP) to figure out the priorities of the faculty. Data from surveys and experts in four universities shows that the effectiveness of teaching support has the highest weight (0.3000). After that comes the satisfaction with technical functionality (0.2667), then role adaptability (0.2333), and finally risk and ethical perception (0.2000). At the sub-criterion level, things like making teaching efficiency better, enhancing content generation, and optimizing instructional design have come up as really important needs. The study gives some practical suggestions, such as making the integration of AIGC content better, allowing for teacher-AI co-creation workflows, and strengthening the transparency of the platform and its ethical safeguards. These findings offer both theoretical understanding and practical help for building AIGC teaching systems that are effective and can be trusted, and that support the collaboration between humans and AI in creative education. This study also comes up with a cloud model algorithm to evaluate China's mainstream AIGC platform, Deepseek. This way is used to check if the evaluation system proposed in this study is scientific and strict.