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Research on the Construction of AIGC Modular Teaching System for Art and Design Education Driven by New Quality Productive Forces
DOI: https://doi.org/10.62381/H261717
Author(s)
Yanchi Chen*
Affiliation(s)
Jiangsu Maritime Institute, The School of Cruise and Art Design, Nanjing, China *Corresponding Author
Abstract
AIGC technology has thoroughly reshaped the production modes of the art and design industry as well as standards for talent demand. The traditional linear and rigid teaching system for art and design can hardly meet the demands of cultivating high-order innovative talents. Based on modular design theory and adopting design research methods, this paper constructs, iterates and optimizes an AIGC modular teaching system for art and design education. By deconstructing typical job tasks in the design industry under new quality productive forces and reconstructing the curriculum system, combinable and dynamically iterative functional modules covering AI general literacy, human-machine collaborative creation and interdisciplinary integrated application are established to break disciplinary barriers and realize precise alignment between teaching content and industrial frontiers. The research shows that the system constructs a triadic interactive teaching structure of "Teacher–Machine–Student", effectively reduces students’ cognitive load in basic skill training, and transforms the supply of teaching resources from one-way indoctrination to multi-modal personalized intelligent provision. Supported by a data-driven real-time feedback mechanism, teaching objectives evolve from conventional skill training to the cultivation of high-order competencies including critical thinking, AI literacy and design ethics. Practical verification proves that the modular framework greatly improves curriculum flexibility and industrial adaptability, facilitates students’transition from single-skill acquisition to intelligent creative production, and provides a replicable practical path for the intelligent transformation of art and design education amid human-machine collaboration.
Keywords
New Quality Productive Forces; Art And Design Education; Aigc; Modular Teaching; Human-Machine Collaboration
References
[1] Peng Honglu. Teaching of University Digital Expression Courses Based on AIGC Technology[J]. Higher Education Development and Evaluation, 2025. [2] Zhang Mu, Gu Qunye, Tian Jinliang. Opportunities and Challenges Brought by AIGC to New Media Art Education[J]. Youth Journalist, 2024. [3] Zuo Zhixin. New Quality Boosts Audio-visual Industry, Integration Usher in the Future — Special Topic of the 4th China Radio and Television Media Integration Development Conference[J]. Media, 2024. [4] Peng Honglu. Teaching of University Digital Expression Courses Based on AIGC Technology[J]. Higher Education Development and Evaluation, 2025. [5] Zhang Mu, Gu Qunye, Tian Jinliang. Opportunities and Challenges Brought by AIGC to New Media Art Education[J]. Youth Journalist, 2024. [6] Wei Yue, Xu Yanli. Research on Optimization of Vocational Education Talent Training Model for New Quality Productive Forces[J]. Chinese Vocational and Technical Education, 2024. [7] Sun Xiaoye. Multi-modal Framework Research on Constructing AIGC-enabled Creative Collaborative Classroom in University Music Education[J]. China University Teaching, 2025. [8] Shi Wanruo, Han Xibin. Impacts of Generative Artificial Intelligence on Learning Analytics Research: Current Status and Prospect — Review of Learning Analytics and Knowledge Conference 2024 (LAK24)[J]. E-education Research, 2024. [9] Zhu Shiming. Serving the Development of New Quality Productive Forces with High-quality Vocational Education[J]. Vocational and Technical Education, 2024. [10] Xia Deyuan. Appearance Originates from Mind: New Landscape of Art Production and Aesthetics in the AIGC Era — Reflections Inspired by Text-to-Video AI Model Sora[J]. Studies in Culture & Art, 2024. [11] Liu Siyuan, He Miao. Coupling Mechanism, Risk Review and Path Exploration of Generative AI Empowering Integrated Ideological and Political Courses in Primary, Secondary and Tertiary Education[J]. Journal of China University of Mining and Technology (Social Sciences Edition), 2024. [12] Wang Youmei, Zhang Tiantian, Mao Congcong. Application Scenarios, Risks, Challenges and Countermeasures of Generative Artificial Intelligence in Global Vocational Education[J]. Chinese Vocational and Technical Education, 2025. [13] Chen Qingsheng, Li Shengkai, Li You. Research on the Construction of High-level Professional Groups in Vocational Colleges[J]. Theory and Practice of Education, 2021. [14] Chen Yasei. AIGC Technology Integration and Remodeling of Talent Training Model in University Publishing Education[J]. Journal of Editors, 2025. [15] Tao Hongshan, Qie Haixia. What Kind of Curriculum System is Needed for Undergraduate Artificial Intelligence Majors in Universities — A Comparative Analysis based on Carnegie Mellon University and Nanyang Technological University[J]. Research on Higher Education of Chongqing, 2021. [16] Zhang Mianhao, Dong Kelei, Zhang Yuehong. Logical Thinking and Promotion Path of Modularization of Basic Vocational Courses in Secondary Vocational Schools[J]. Education and Vocation, 2022. [17] Wang Qiang. Grouping Logic and Construction Path of Art and Design Professional Groups in Higher Vocational Colleges under the Background of "Double High Plan"[J]. Education and Vocation, 2022.
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