Intelligent Lighting Angle Dynamics for Enhanced Cinematic Shadows in VR-based Serious Games

This study addresses the challenge of achieving optimal cinematic lighting in virtual cinematography education, particularly under the constraints of physical infrastructure. The relationship between light intensity and angle in virtual cinematography is examined using an Unreal Engine-based serious game-based learning platform. Cinematic outcomes are enhanced by fuzzifying intensity and angle lighting, with moderate intensity and medium angle improving the shadow quality. The Fuzzy Inference System (FIS) classifies shadows as cinematic, harsh, and dark. Validation using Ordinal Logistic Regression (OLR) and t-test revealed significant effects of intensity (p=0.031) and angle (p<0.001) on the lighting results. This serious game improved learning, with the experimental group's post-test scores being 42% higher than those of the control group. This technology provides scalable and immersive cinematography training without the need for expensive infrastructure. Future applications involve AI-based adaptive lighting systems for cinematography learning and pre-production. The findings show that fuzzy-based classification significantly improves the cinematic quality of shadows, contributing to scalable VR-based cinematography learning and adaptive lighting design in educational contexts.