Exploitation for Multimedia Asian Information Processing and Artificial Intelligence-based Art Design and Teaching in Colleges

Abstract

Artificial intelligence has been widely used in art education and learning due to its quick progress.  Any creation made with the help of artificial intelligence is referred to as art design. It covers works generated independently by AI systems and works created in collaboration with humans and AI systems. The objective of directing the invention of environmental art design thinking is to stimulate students' learning and innovation abilities and teach students how to put design ideas into effect. Despite the progress of smart technologies, there are several challenges in increasing the teaching capabilities of technical art design courses, such as the influence of different variables, the absence of quantitative research, and the imperfection in the index system. In this paper, the Artificial intelligence-based Art design and teaching (AI-ADT) method in colleges increases the capacity to adapt to AI-oriented art education, establish intelligent teaching methods, and improve AI-oriented art teaching art knowledge and environments. The widespread application of artificial intelligence in design education has become a trend in development. Self-Learning Systems are software that incorporates machine learning techniques to allow computers to learn from and make judgments based on data without the need for explicit programming instructions. The art design profession should confirm and actively adapt to this development trend. Modify the original teaching mode, invent their teaching methods, continually enrich the teaching methods, enhance the quality of teaching, and constantly foster high-quality art design talents in the new age. AI-ADT investigates the optimization of the art design curriculum system in higher education institutions in the context of artificial intelligence. The experimental results show that the proposed method develops smart teaching (98.1%), flexibility (96.5%), performance (93.6%), participation (94.9%), and interaction (95.1%).