Exploring Computer-Aided Environmental Art Design: A Course Overview

Computer aided education is transforming with the integration of technology. In the context of advancing art education, there is a pressing need for innovation to enhance student engagement and learning outcomes. This study introduces an innovative approach by employing an Adaptive Kookaburra Optimized Dynamic Recurrent Neural Network (AKO-DRNN) with the framework of computer-aided environmental art design courses. The traditional methods of teaching art are being complemented by computer-aided tools and intelligent systems. This research explores the application of AKO-DRNN in revolutionizing art education, focusing on environmental art design. The primary goal is to develop an instructional system that leverages advanced algorithms to offer personalized, accurate aesthetic guidance, enhance creative exploration, and elevate students' practical skills in environmental art design. This study integrates AKO-DRNN into the course structure, which combines deep learning (DL) models with environmental design principles. The AKO-DRNN model utilizes dynamic recurrent networks optimized by a Kookaburra-inspired optimization algorithm to effectively analyze and predict artistic styles and features. This model provides real-time feedback and adaptive learning paths tailored to individual student needs. Implementation of the suggested model has demonstrated significant improvements in students’ design quality, creativity, and skill acquisition. The adaptive nature of the model enhances learning outcomes and engagement. The established framework offers a robust solution for modernizing art education in environmental design, fostering greater innovation and practical skills among students.