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The rapid advancement of artificial intelligence has been intensely employed in art teaching and learning. Including the advancement of smart technologies, there are various difficulties in improving the teaching capability of technical art design courses, including the impact of several variables and the absence of quantitative study, and the imperfection in the index system. The paper proposes the Artificial Intelligence assisted Effective Art Teaching Framework (AIEATF) to expand the ability to adapt to AI-oriented art instruction, develop intelligent teaching styles, and enhance AI-oriented art teaching art knowledge and environment. The potential of improving AI’s effects on major art courses’ teaching effect has been illustrated in detail. On this basis, an assessment model has been developed to consider the enhancing effects. The study’s findings include a valuable guide for educators in art design to strengthen their teaching ability. The experimental results have shown that Modern Painting Perfection Ratio is 87.66%, Computer graphical representation ratio is 88.77%, Photographical Design Percentage ratio is 84.50%, Performance of Carving in Sculpture Ratio is 82.26%, Construction Development Ratio is 93.83%, Expressive Musical Performing Ratio is 92.70%, Energized Dance Performance Ratio is 84.26%, and overall performance ratio is 92.30%.
The rapid advancement of artificial intelligence has been intensely employed in art teaching and learning. Including the advancement of smart technologies, there are various difficulties in improving the teaching capability of technical art design courses, including the impact of several variables and the absence of quantitative study, and the imperfection in the index system. The paper proposes the Artificial Intelligence assisted Effective Art Teaching Framework (AIEATF) to expand the ability to adapt to AI-oriented art instruction, develop intelligent teaching styles, and enhance AI-oriented art teaching art knowledge and environment. The potential of improving AI’s effects on major art courses’ teaching effect has been illustrated in detail. On this basis, an assessment model has been developed to consider the enhancing effects. The study’s findings include a valuable guide for educators in art design to strengthen their teaching ability. The experimental results have shown that Modern Painting Perfection Ratio is 87.66%, Computer graphical representation ratio is 88.77%, Photographical Design Percentage ratio is 84.50%, Performance of Carving in Sculpture Ratio is 82.26%, Construction Development Ratio is 93.83%, Expressive Musical Performing Ratio is 92.70%, Energized Dance Performance Ratio is 84.26%, and overall performance ratio is 92.30%.
Recently, the teaching and learning method in the conventional engineering education system needs a group of learners with personalized learning paths. The introduction of technologies like Artificial Intelligence will aid the learners to identify and detect learning opportunities utilizing historical information, present student profile and success data from an institution, and recommend learning measures to enhance their performance. This study proposes an Artificial Intelligence-based Meta-Heuristic Approach (AIMHA) for personalized learning detection systems and quality management. The proposed model has been utilized to optimize learning effectiveness by considering the nature of the learning path and the number of simultaneous visits to every learning action. In addition, a quality resolution can be determined by a meta-heuristic approach. The simulation findings of the learning actions have been utilized to examine the efficiency of the suggested method. The proposed method is evaluated learning activities achieved an efficiency ratio of 92.3%, sensitivity analysis ratio of 88.4%, performance ratio of 92.3%, precision ratio of 94.3% compared to other existing models.
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