Author(s)

XianJun Yu

Affiliation(s)

The School of Fine Arts, Nantong Normal College, Nantong 226000, Jiangsu, China.

Corresponding Author

XianJun Yu

ABSTRACT

This study proposes a dual-algorithm approach for integrating artificial intelligence into art and design education, combining a genetic algorithm and a cultural algorithm. We applied these methods in two pedagogical interventions focused on Dunhuang Murals and the Classic of Mountains and Seas (Shan Hai Jing). Using recursive generation and generative adversarial network (GAN) training, the genetic-algorithm pipeline preserved approximately 92% of traditional cultural symbols as measured by a symbol-matching metric. The cultural algorithm—designed to simulate sociocultural selection and incorporate iterative user-feedback—achieved an 88% symbol-matching rate and demonstrated positive cross-cultural reception in IP adaptation trials. These results suggest that algorithmic systems can function not only as technical tools but also as vectors for rematerializing cultural memory and accelerating cultural evolution. In light of these findings, contemporary art and design education should move beyond teaching tool use toward cultivating "digital humanists"—practitioners who can design, tune, and critically evaluate cultural algorithms.

KEYWORDS

Genetic algorithm; Cultural algorithm; Dunhuang murals; Shan Hai Jing (Classic of Mountains and Seas); Art and design education; Cultural memory

CITE THIS PAPER

XianJun Yu. From Dunhuang to the Shan Hai Jing: algorithmic culture facilitates contemporary translation of cultural heritage and the reconstruction of art design education. World Journal of Educational Studies. 2025, 3(8): 82-86. DOI: https://doi.org/10.61784/wjes3112.

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