Art and Design Classroom Driven by Artificial Intelligence: A Study of Teaching Mode Innovation under the Perspective of Multiple Intelligences Theory

Abstract

Based on the theory of multiple intelligences, this paper explores the innovative path of teaching mode driven by artificial intelligence technology. Image processing technology is used to optimize the art design system, and the image brightness equalization and fusion effect is improved by grid chunk repair and wavelet noise reduction. The visual expression sensitivity difference method is introduced to realize high-precision super-resolution reconstruction. Simulation experiments are designed, and the average signal-to-noise removal rate of the proposed system is 56.04% higher than that of the traditional system when the detection time is 100s. The image processing technique proposed in this paper provides better quality input features for subsequent classification tasks by optimizing image brightness equalization and feature fusion. The model shows comprehensive advantages on all types of images, and in the subjective evaluation, it scores 4.67 and 4.78 in both global reconstruction quality and detail reconstruction artistry, respectively, which are better than the control model.