AI-ASSISTED MACRO PHOTOGRAPHY LEARNING MODELS - Das, Nihar; Kaur, Savinder; Das, Sidhant; Prajapati, Darshana; Mithun M S,; Hingmire, Anil (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Macro photography requires a lot of control in focus, lighting, depth of field and stability of the camera which makes it one of the most technical challenging technique of photography to a beginner. Conventional learning techniques are too much guided by trials and errors and without real time corrective feedback, there is a tendency to make slow progress and unstable outcome. This paper outlines an AI-Assisted Macro Photography Learning Model as an implementation of deep learning and reinforcement learning with a generative simulation that would deliver context-aware and customized feedback to learners. The system uses hybrid CNN-Transformer frameworks to analyze macro images and measure sharpness, illumination, exposure, and composition, and a reinforcement learning engine uses it to suggest the best camera settings depending on the conditions of the image. A virtual macro simulation environment also allows safe repeatable practice with photorealistic: diffusion-based synthetic scenes. Assessment on 60 beginner photographers indicates that AI-aided trainees gained up to 32 percent in sharpness, 28 percent in light precision and used 40 percent fewer techniques to record usable photographs than their conventional partners. These findings underpin the usefulness of AI-based feedback in enhancing the acceleration of skills acquisition as well as the enhancement of technical and aesthetic skill. The offered structure provides a scaffoldable way of developing macro photography learning into a technology-intensive, adaptive, and structured learning process.