Research on Intelligent Generation and Construction of Multi-modal Teaching Resource Bank of Liaoning Regional Culture and Art Driven by AI
DOI:
https://doi.org/10.54097/f5xk2e98Keywords:
AI technology; Liaoning regional culture; graphic design.Abstract
In the context of the digital economy, the rapid advancement of artificial intelligence (AI) technology has provided new technological support for the informatization of education and the digitalization of culture. Liaoning, a key component of Northeast China's old industrial base, is rich in diverse regional cultural and artistic resources. These resources are not only important carriers of Chinese traditional culture but also valuable materials for local characteristic education. However, the current digitalization of Liaoning's regional cultural and artistic resources faces challenges such as scattered resources, limited forms, and low utilization rates, which hinder the needs of cultural inheritance and educational innovation in the new era. This study focuses on the intersection of AI and educational informatization, exploring innovative approaches to leverage AI technology for the digitalization of Liaoning's regional cultural and artistic resources. By constructing a multimodal teaching resource library, it aims to systematically protect and inherit Liaoning's distinctive cultural and artistic heritage, while also providing high-quality digital teaching resources for all levels of education, thus promoting the innovative development of regional cultural education. The theoretical value of this study lies in expanding the application boundaries of AI technology in cultural education, while its practical significance is reflected in providing replicable and scalable technical solutions for the digitalization of regional cultural resources.
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References
[1] Johnson, K., et al. Generative AI for Cultural Heritage Preservation: A 2024 Perspective[J]. ACM Computing Surveys, 2024, 57(1): 1-45.
[2] Liu Wei and Zhang Minghua. Design of an Intelligent Retrieval System for Multi-modal Cultural Resources Based on Deep Learning [J]. Modern Library and Information Technology, 2023,39(6):56-64.
[3] Chen Zhiqiang and Li Na. 2023-2024 Report on the Transformation and Development of Digital Education [M]. Beijing: Educational Science Press, 2024.
[4] Wang, L., & Chen, Y. Multimodal Learning for Cultural Heritage: New Developments in 2024[J]. IEEE Access, 2024, 12: 15678-15692.
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