ART CURATION ALGORITHMS MACHINE LEARNING IN MUSEUM EDUCATION

Abstract

This paper introduces a consolidated machine learning framework for adaptive art curation for improving museum education. It proposes a system that combines computer vision, natural language processing, recommendation algorithms, and multimodal fusion in order to interpret the works of art and curatorial metadata, and create custom learning pathways given to visitors. A mathematical model is used to formalize the representation of the artwork, the dynamics of visitor preferences, the computation of thematic similarity and the optimization of education, offering a constructed basis for the adaptive curation. The framework illustrates how machine learning can reveal relationships that are not obvious in a collection, promote more compelling interpretive stories and react to individual interests of the visitor in real-time. It further adds the explainability mechanisms and ethical constraints to guarantee the transparency, cultural sensitivity, and fairness in algorithmic recommendations. The findings point to the prospect of the ML-inspired curation to turn museums into a dynamic and learner-focused space but not eliminate the human curatorial skills but augment them. The research adds a practical and theoretically-based model for incorporating machine learning in museum education in an ethical and transparent way.

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