Artificial Intelligence-Driven Interactive Learning Methods for Enhancing Art and Design Education in Higher Institutions

Abstract

To address the problem of the classroom practice teaching of art and design, this paper proposes an interactive learning method in universities, followed by the use of AI technology to evaluate the quality of art and design teaching. The study aims to achieve the following objectives: 1) To introduce the current research status of art and design and interactive teaching methods used in other countries; 2) To discuss the essential ideas and mechanics of Back Propagation Neural Networks (BPNN) and other standard teaching approaches based on interactive learning; and 3) To input test data into the trained model and compare the obtained results with the evaluation results of experts. The findings of this study indicate that the model used to evaluate art and design instruction is accurate. The proposed interactive learning method is beneficial for art and design majors as it allows them to improve their practical skills and learn more engagingly and effectively. The use of AI technology for evaluation purposes can also improve the quality of art and design education in universities.