The analysis of generative artificial intelligence technology for innovative thinking and strategies in animation teaching
Abstract This work examines the application of Generative Artificial Intelligence (GAI) technology in animation teaching, focusing on its role in enhancing teaching quality and learning efficiency through innovative instructional strategies. Compared to traditional animation teaching methods, GAI te...
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| Format: | Article |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-03805-y |
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| author | Xu Yao Yaozhang Zhong Weiran Cao |
| author_facet | Xu Yao Yaozhang Zhong Weiran Cao |
| author_sort | Xu Yao |
| collection | DOAJ |
| description | Abstract This work examines the application of Generative Artificial Intelligence (GAI) technology in animation teaching, focusing on its role in enhancing teaching quality and learning efficiency through innovative instructional strategies. Compared to traditional animation teaching methods, GAI technology introduces a novel pedagogical paradigm characterized by adaptive personalized learning pathways, intelligent teaching resource optimization, and immersive interactive learning models. A mixed-methods research approach is adopted, integrating quantitative analysis (experimental data and questionnaire surveys) and qualitative analysis (behavioral observations) to systematically assess the educational effectiveness of GAI technology. The experiment, conducted over 12 weeks, involved 120 students divided into an experimental group and a control group. Data sources included pre- and post-test evaluations, learning feedback surveys, and classroom behavior analysis. The results indicate that, compared to conventional teaching methods, GAI technology significantly enhances learning outcomes, knowledge application abilities, learning motivation, and student satisfaction. The adaptive personalized learning pathway dynamically adjusts content based on students’ progress, improving their mastery of foundational knowledge and skill transferability. Intelligent teaching resources automatically generate high-quality animation examples and provide dynamic feedback mechanisms, fostering creative expression and practical efficiency. The immersive interactive learning model effectively increases classroom engagement, teamwork skills, and problem-solving abilities. These findings demonstrate that GAI technology has the potential to transform animation teaching by optimizing the learning experience and advancing intelligent teaching methodologies. Beyond offering personalized learning solutions, GAI technology plays a crucial role in cultivating students’ creativity, critical thinking, and autonomous learning abilities. This work provides theoretical support and practical guidance for the digital transformation of animation teaching while underscoring the broader applicability of GAI technology in the education sector, offering new directions for the future development of intelligent education. |
| format | Article |
| id | doaj-art-d737a32bc704453cb630eb6a3e17c4c1 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d737a32bc704453cb630eb6a3e17c4c12025-08-20T03:22:12ZengNature PortfolioScientific Reports2045-23222025-05-0115112210.1038/s41598-025-03805-yThe analysis of generative artificial intelligence technology for innovative thinking and strategies in animation teachingXu Yao0Yaozhang Zhong1Weiran Cao2School of Animation and Digital Arts, Communication University of ZhejiangJiangxi College of Applied TechnologySchool of Art and Archaeology, Hangzhou City UniversityAbstract This work examines the application of Generative Artificial Intelligence (GAI) technology in animation teaching, focusing on its role in enhancing teaching quality and learning efficiency through innovative instructional strategies. Compared to traditional animation teaching methods, GAI technology introduces a novel pedagogical paradigm characterized by adaptive personalized learning pathways, intelligent teaching resource optimization, and immersive interactive learning models. A mixed-methods research approach is adopted, integrating quantitative analysis (experimental data and questionnaire surveys) and qualitative analysis (behavioral observations) to systematically assess the educational effectiveness of GAI technology. The experiment, conducted over 12 weeks, involved 120 students divided into an experimental group and a control group. Data sources included pre- and post-test evaluations, learning feedback surveys, and classroom behavior analysis. The results indicate that, compared to conventional teaching methods, GAI technology significantly enhances learning outcomes, knowledge application abilities, learning motivation, and student satisfaction. The adaptive personalized learning pathway dynamically adjusts content based on students’ progress, improving their mastery of foundational knowledge and skill transferability. Intelligent teaching resources automatically generate high-quality animation examples and provide dynamic feedback mechanisms, fostering creative expression and practical efficiency. The immersive interactive learning model effectively increases classroom engagement, teamwork skills, and problem-solving abilities. These findings demonstrate that GAI technology has the potential to transform animation teaching by optimizing the learning experience and advancing intelligent teaching methodologies. Beyond offering personalized learning solutions, GAI technology plays a crucial role in cultivating students’ creativity, critical thinking, and autonomous learning abilities. This work provides theoretical support and practical guidance for the digital transformation of animation teaching while underscoring the broader applicability of GAI technology in the education sector, offering new directions for the future development of intelligent education.https://doi.org/10.1038/s41598-025-03805-yGenerative artificial intelligenceAnimation teachingInnovative thinkingInstructional strategies |
| spellingShingle | Xu Yao Yaozhang Zhong Weiran Cao The analysis of generative artificial intelligence technology for innovative thinking and strategies in animation teaching Scientific Reports Generative artificial intelligence Animation teaching Innovative thinking Instructional strategies |
| title | The analysis of generative artificial intelligence technology for innovative thinking and strategies in animation teaching |
| title_full | The analysis of generative artificial intelligence technology for innovative thinking and strategies in animation teaching |
| title_fullStr | The analysis of generative artificial intelligence technology for innovative thinking and strategies in animation teaching |
| title_full_unstemmed | The analysis of generative artificial intelligence technology for innovative thinking and strategies in animation teaching |
| title_short | The analysis of generative artificial intelligence technology for innovative thinking and strategies in animation teaching |
| title_sort | analysis of generative artificial intelligence technology for innovative thinking and strategies in animation teaching |
| topic | Generative artificial intelligence Animation teaching Innovative thinking Instructional strategies |
| url | https://doi.org/10.1038/s41598-025-03805-y |
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