Leveraging AI to Enhance Art Education

Learning experiences in museums significantly enhance the arts education that students receive in school, fostering creative thinking, critical analysis, and empathy [1, 2]. However, the current approach in many art museums and exhibitions restricts visitors’ ability to fully understand the artists and their works. Art museums often provide minimal contextual information about artworks, as curators prioritize aesthetic experience over detailed labeling [3]. Consequently, learning about artworks and artists often takes place outside the museum, which can diminish the overall learning experience [4, 5]. The challenge is even greater with new, unfamiliar, and underrepresented artworks and artists, whose work may be less recognized.

In response, researchers have developed systems such as virtual guides [6, 7, 8 ] and robots [9 , 10] and implemented in the museums. These solutions, however, lack the ability to engage visitors in more extensive dialogue or support inquiry-based learning [ 11], limiting deeper exploration and critical thinking about the artwork in the exhibition. Recently, museums have begun leveraging advancements in Large Language Models (LLMs) to enhance art education within museum settings. For example, IBM Brazil developed A Voz da Arte (”The Voice of Art”), an Artificial Intelligence (AI)-driven system that utilizes data from museum archives, news sources, and the internet to answer visitor inquiries about artworks [12]. While these systems are effective at engaging visitors, their reliance on online data (e.g., [12]) can potentially lead to AI hallucinations [13 ] that can misrepresent artists and their work, hindering learners’ ability to accurately understand both the artwork and the artist. To address these challenges, this project aims to develop an AI-powered system that supports inquiry-based learning during art museum visits and evaluate its acceptance by artists and museums as well as its impact on art students’ learning outcomes.

References

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