Yao Xu, Zhong Yaozhang, Cao Weiran
School of Animation and Digital Arts, Communication University of Zhejiang, Hangzhou, 310000, China.
Jiangxi College of Applied Technology, Ganzhou, 341000, China.
Sci Rep. 2025 May 28;15(1):18618. doi: 10.1038/s41598-025-03805-y.
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.
这项工作探讨了生成式人工智能（GAI）技术在动画教学中的应用，重点关注其通过创新教学策略提高教学质量和学习效率的作用。与传统动画教学方法相比，GAI技术引入了一种新颖的教学范式，其特点是适应性个性化学习路径、智能教学资源优化和沉浸式互动学习模式。采用了混合方法研究途径，整合了定量分析（实验数据和问卷调查）和定性分析（行为观察），以系统评估GAI技术的教育效果。该实验为期12周，涉及120名学生，分为实验组和对照组。数据来源包括测试前和测试后的评估、学习反馈调查以及课堂行为分析。结果表明，与传统教学方法相比，GAI技术显著提高了学习成果、知识应用能力、学习动机和学生满意度。适应性个性化学习路径根据学生的进展动态调整内容，提高了他们对基础知识的掌握程度和技能转移性。智能教学资源自动生成高质量的动画示例并提供动态反馈机制，促进了创造性表达和实践效率。沉浸式互动学习模式有效提高了课堂参与度、团队合作技能和解决问题的能力。这些发现表明，GAI技术有潜力通过优化学习体验和推进智能教学方法来改变动画教学。除了提供个性化学习解决方案外，GAI技术在培养学生的创造力、批判性思维和自主学习能力方面也发挥着关键作用。这项工作为动画教学的数字化转型提供了理论支持和实践指导，同时强调了GAI技术在教育领域的更广泛适用性，为智能教育的未来发展提供了新方向。
