Article: Teaching content generation and semantic information extraction for art and design courses targeting AIGC Journal: International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL) 2026 Vol.36 No.11 pp.201 - 229 Abstract: Traditional methods for generating course content rely on manual rules, lacking dynamic knowledge support and semantic monitoring, leading to deviations between generated content and teaching objectives. Therefore, this study proposes an AI-powered framework for generating art and design course content that integrates knowledge graphs and multimodal semantic information extraction. Results show that, in tests on a self-built dataset, the framework achieved knowledge accuracy of 91.21%, knowledge graph relationship consistency of 89.91%, and graph-text relevance of 0.33%, representing average improvements of 39.3%, 29.8%, and 13.8% compared to the best baseline. The model converges quickly with a response time as low as 1.0 millisecond, a service error rate of only 4%, and maintains 92% stability under 100 concurrent requests. This framework demonstrates excellent scalability and service robustness, providing core technical support for the transformation of art and design education in the era of artificial intelligence. Inderscience Publishers - linking academia, business and industry through research

Title: Teaching content generation and semantic information extraction for art and design courses targeting AIGC

Authors: Bo Zhang

Addresses: Art College, Chongqing Technology and Business University, Chongqing, 400067, China

Abstract: Traditional methods for generating course content rely on manual rules, lacking dynamic knowledge support and semantic monitoring, leading to deviations between generated content and teaching objectives. Therefore, this study proposes an AI-powered framework for generating art and design course content that integrates knowledge graphs and multimodal semantic information extraction. Results show that, in tests on a self-built dataset, the framework achieved knowledge accuracy of 91.21%, knowledge graph relationship consistency of 89.91%, and graph-text relevance of 0.33%, representing average improvements of 39.3%, 29.8%, and 13.8% compared to the best baseline. The model converges quickly with a response time as low as 1.0 millisecond, a service error rate of only 4%, and maintains 92% stability under 100 concurrent requests. This framework demonstrates excellent scalability and service robustness, providing core technical support for the transformation of art and design education in the era of artificial intelligence.

Keywords: artificial intelligence generation; art and design courses; knowledge graph; multi-modal semantic information extraction; teaching content generation; adversarial optimisation; intelligent teaching assistance system.

DOI: 10.1504/IJCEELL.2026.154669

International Journal of Continuing Engineering Education and Life-Long Learning, 2026 Vol.36 No.11, pp.201 - 229

Received: 07 Nov 2025
Accepted: 23 Jan 2026
Published online: 09 Jul 2026 *

Title: Teaching content generation and semantic information extraction for art and design courses targeting AIGC

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