Research on the Mechanisms of the Digital Art Talent Cultivation Mode in Colleges and Universities Based on the AIGC

Authors

  • Yihan Wang School of Arts, Wuhan Business University, Wuhan 430056, China
  • Yu Zhou School of Arts, Wuhan Business University, Wuhan 430056, China

DOI:

https://doi.org/10.53469/jerp.2024.06(11).21

Keywords:

AIGC, Higher Education, Talent Cultivation Model, Digital Talent, Animation Major

Abstract

In the field of digital art, artificial intelligence-generated content (AIGC) technology is gradually becoming the core driving force of innovation. AIGC technology can automatically generate images, music, text and other types of artworks by simulating the creative process of human beings, providing new tools and platforms for digital art creation. Through the three methods of the literature review method, case study method, and interviews and questionnaire surveys, this paper thoroughly explores the current status of the application of AIGC in digital art education in colleges and universities, its teaching effect and its impact on the construction of teaching resources, teaching modes and evaluation systems. The research results show that AIGC technology can be used not only as a teaching tool but also as a platform to stimulate creativity and provide students with a new learning experience. Through data analysis, this paper reveals the potential of AIGC technology in enhancing students' interest in learning, creative expression, and technical mastery and proposes corresponding optimization of the teaching mode and evaluation system. In addition, this paper explores the challenges faced by AIGC technology in educational applications, such as ethical and legal issues, technological and cost challenges, insufficient faculty, and students' adaptability, and proposes corresponding countermeasures. Finally, this paper looks forward to the future development trend of AIGC in digital art education, highlights the contributions and innovations of the study, and provides references and suggestions for universities to cultivate new digital art talent in the era of AIGC.

References

Wang X. Collaboration and Reshaping: Communication Strategies of Chinese Traditional Calligraphy and Painting Art in the AIGC Era[J]. Art Communication Research, 2024, (6): 21-30.

Jia H. The Impact of Generative AI AIGC on Animation Creation[J]. Journal of Suifenhe University, 2024, 44(11): 48-50.

Yi X., Qin T., He J. Research on High-Precision Path Planning and Experimental Study of Intelligent Drawing Robot Based on AIGC[J]. China-Arab States Science and Technology Forum (English and Chinese), 2024, (11): 98-102.

Qin S., Li X. Reshaping Production Process and Coping with Trust Crisis in the AIGC Revolution of the Film and Television Industry from ChatGPT to Sora[J]. Audio-Visual, 2024, (11): 3-8.

Fu J., Wei J., Liu M., et al. AIGC Technology Empowers the Transformation of Engineering Education: Innovation of Teaching Methods and Learning Experience[J]. Higher Engineering Education Research, 1-7 [2024-11-15]. http://kns.cnki.net/kcms/ detail/42.1026.G4.20241028.1033.038.html.

Zhang N., Chen J., Yuan Q. Integration and Separation Dilemma: A Study on the Willingness of Academic Users to Use AIGC Technology under Algorithm Alienation[J]. Modern Information, 1-25 [2024-11-15]. http://kns.cnki.net/kcms/detail/22.1182.g3.20241023.1928.004.html.

Wu Z. The Integration of Electronic Games and AIGC under the Empowerment of AGI[J]. internet Weekly, 2024, (20): 17-21.

Meng K. Digitalization of Presence: The Digital Path of Drama under the Perspective of Virtual Reality[J]. Theatre Arts, 2024, (5): 20-29+49+6.

Sun J. Exploration of AIGC Background in College Design Basic Course Teaching - Taking Design Sketch as an Example[J]. Fine Arts Education Research, 2024, (19): 128-130.

Zheng X., Jia Y., Huang H. Construction and Practice of Interdisciplinary Training Model for Digital Art Talents in the AIGC Era[J]. Film and Television Production, 2024, 30(10): 48-54.

Cai W. The Prospect and Application of AIGC in Short Video Generation Field[J]. China Newspaper Industry, 2024, (19): 90-91.

Ma D. Application of AIGC Large Model in Digital Media Art Design Education[J]. Shanghai Packaging, 2024, (10): 193-195.

Wang R., Li M., Song W., et al. The Role Positioning and Functional Extension of AIGC Empowering Computer Basic Education - A Teaching Design and Practice Based on Double Chain Iteration[J]. Computer Education, 2024, (10): 159-163+168.

Li F., Lv X. Research on Application Scenarios and Production Mechanism of AIGC in Media Art Education[J]. Media, 2024, (19): 12-15.

Li Y., Hu B. Technology Empowers Innovation: Transformation Opportunities for Media Art Education under the AIGC Wave[J]. Media, 2024, (19): 22-24.

Mi G., Li Z., Wen Y. Industry, Opportunities and Challenges: The Reform of College Media Art Education under the Background of AIGC[J]. Media, 2024, (19). 16-18.

Zhao Y. Q. The Copyright and Governance Path of AI-Generated Content from the Perspective of New Quality Productive Forces[J]. Publishing Panorama, vol. 2024, (15): 60-65.

Cui H. Y. Research on Information Literacy Education Issues and the Practice of Course Ideology under the AIGC Perspective[J]. Heihe Academic Journal, vol. 2024, (5): 67-72.

Hua Z. X., Wang W., Wu K. H., et al. How to Enhance the Educational Effectiveness of AIGC? - Cultivating Digital Literacy Awareness and Ability Based on Response Surface Analysis[J]. Modern Educational Technology, 2024, 34(9): 14-25.

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Published

2024-11-28

How to Cite

Wang, Y., & Zhou, Y. (2024). Research on the Mechanisms of the Digital Art Talent Cultivation Mode in Colleges and Universities Based on the AIGC. Journal of Educational Research and Policies, 6(11), 89–94. https://doi.org/10.53469/jerp.2024.06(11).21