Revolutionizing art education: Integrating AI and multimedia for enhanced appreciation teaching

Abstract

Recent teaching trends are increasingly integrating diverse multimedia and computer-aided practices to enhance representation and understanding. Leveraging data-focused strategies, these methods are further refined with artificial intelligence and decision-making techniques. To improve data handling in multimedia-based teaching representation, this article introduces an integrated data representation model aided by regression learning (IDR-RL). The proposed model satisfies the data required for different teaching methods/ subjects based on curriculum and student requirements. The data integration from different sources is performed using checksum assessment. The checksum assessment for data provides precise assimilation and content-related data-to-visual representation. In this representation, the checksums are verified for their linearity, wherein the saturation/ threshold factors are identified, and data integration is recommended. This recommendation is performed as the decision-making for preventing paused representation in live teaching sessions. The checksums are used for content coherency and complete data representations to prevent time lags in teaching. The proposed model’s performance is verified through data handling rate, integration ratio, pause time, representation time lag, and failures.