The Cultivation of Innovative Modeling Design Ability in the Teaching of Art Courses with the Help of AI Technology
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Abstract
The emergence of the artificial intelligence-driven digital era has significantly affected the teaching of traditional art courses. Traditional art education emphasizes hands-on skills, traditional materials, and strict standards for learning processes and result presentation, whereas AIGC technologies create new opportunities for curriculum reform and innovation-oriented talent cultivation. In engineering-related design fields, AI-assisted visual modeling may also support the morphology exploration of antenna arrays, electromagnetic metasurfaces, and periodic functional structures where form, spatial arrangement, and visual logic influence technical performance. Based on these considerations, this paper employs a quasi-experimental research design to evaluate the efficacy of AI-assisted pedagogy. The study formulates the hypothesis that AI-driven visual deep learning significantly improves students’ form-creation scores compared with traditional methods. Over a 16-week academic term, an experimental group using AI tools was compared with a control group through pre- and post-test evaluations. Student outcomes were assessed using a dual-blind peer-review rubric focusing on technical precision and design innovation, followed by independent-samples t-tests to determine statistical significance. The study clarifies the role of AI tools in process-oriented learning, outcome-based education, and innovative modeling design, and suggests that AI-supported visual generation can extend from art education to structure-oriented electromagnetic design education.
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References
O. Babiak, “THREADS AND UPGRADES OF AI TECHNOLOGY IN THE NEW MEDIA ERA,” Grail of Science, 2023, doi: 10.36074/grail-of-science.17.02.2023.080.
L. Zeng, “Evaluation method of the impact of AI technology application on the quality of smart teaching,” International Journal of Continuing Engineering Education and Life-Long Learning, vol. 35, no. 1-2, pp. 95-111, 2025, doi: 10.1504/IJCEELL.2025.143794.
J. Sang, “The Intersection of Technology and Art: A Study on AI-Driven CTCL Music Teaching Paradigm,” 2024, doi: 10.21203/rs.3.rs-5430174/v1.
S. S. Ma, Z. Qu, X. Li, et al., “The innovative application and prospects of AI and gene editing technology in high-yield crop cultivation,” Geographical Research Bulletin, vol. 3, no. 3, pp. 219-222, 2024, doi: 10.50908/grb.3.0_219.
S. V. Singh and K. K. Hiran, “The Impact of AI on Teaching and Learning in Higher Education Technology,” Journal of Higher Education Theory & Practice, vol. 12, no. 13, 2022, doi: 10.33423/jhetp.v22i13.5514.
Z. Cui, “Research on the Application of AI Technology in Music Composition Teaching,” Journal of Higher Education Research, vol. 5, no. 6, pp. 595, 2025, doi: 10.32629/jher.v5i6.3396.
M. H. A. Azis and H. Isam, “AI Technology: A Lifeline for the Continuity of Art in the Millennial Era,” Advances in Social Science, Education and Humanities Research, pp. 392-405, 2024, doi: 10.2991/978-2-38476-321-4_29.
O. Shevchenko, “THE USE OF AI TECHNOLOGY IN LANGUAGE LEARNING,” European Science, no. sge29-03, pp. 233-240, 2023, doi: 10.30890/2709-2313.2024-29-00-07.
X. Zhong, “Trends and Prospects for the Use of AI Technology in Language Courses,” Journal of Education and Educational Research, vol. 8, no. 2, pp. 99-102, 2024, doi: 10.54097/s0h9cd69.
C. Tang, “Application and Evaluation of Blended Courses Based on AI Technology in Design Specialty Teaching,” Learning and Analytics in Intelligent Systems, pp. 479-489, 2024, doi: 10.1007/978-3-031-69457-8_44.
J. Zhao-Hui, “The Application of AI Technology in the Reform of College English Teaching,” Sino-US English Teaching, vol. 22, no. 7, pp. 230-234, 2025.
F. Du, “The Role of AI Technology in Digital Media Technology Course Teaching,” Journal of Education, Humanities and Social Sciences, vol. 41, pp. 162-167, 2024, doi: 10.54097/9jsex297.
M. Giannakos, M. Horn, M. Cukurova, et al., “Learning, design and technology in the age of AI,” Behaviour & information technology, no. 1/5, pp. 44, 2025, doi: 10.1080/0144929x.2025.2469394.