The analysis of generative artificial intelligence technology for innovative thinking and strategies in animation teaching

动画 计算机科学 生成语法 人工智能 多媒体 数据科学 人机交互 计算机图形学(图像)
作者
Yao Xu,Ying Zhong,Weiran Cao
出处
期刊:Scientific Reports [Nature Portfolio]
卷期号:15 (1) 被引量:1
标识
DOI:10.1038/s41598-025-03805-y
摘要

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.
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