Revolutionizing the way students learn photographic arts through experiential education using AI and AR systems

The evolution of educational environments has seen a shift from conventional classrooms to technology-enhanced smart classrooms, driven by the rapid advancement of digital tools. The integration of traditional art education and modern technologies lacks interactivity and personalized feedback, which limits student engagement and creative progression. The objective of this research is to assess how AI and AR can be combined to improve student engagement, creativity, academic performance, and aesthetic understanding in art education. Data were collected from smart classroom sessions involving educational videos and interactive AR applications focused on photography. The pre-processing stage automatically filters low-quality images, retaining those with high saliency and clarity scores to ensure meaningful input for analysis. Using a TensorFlow-based experimental framework, a Deep Recurrent Neural Network (DRNN) algorithm was employed for intelligent image synthesis and feedback, allowing real-time analysis of composition and augmented visual storytelling. Results indicated notable improvements in student, Accuracy (97.18%), precision (97.33%), recall (96.95%), F1 score (97%). Students responded positively to the immersive experience, showing increased appreciation for cultural and visual diversity. In conclusion, the study demonstrates that integrating AI and AR in smart classroom environments can redefine art education by fostering experiential learning and providing dynamic, student-centered educational opportunities.