Research on the Dynamic Optimization Mechanism of Animation Course Group Based on Human Computer Collaboration from the Perspective of Digital Humanities
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Abstract
Under the deep integration of digital humanities and intelligent education, animation education is shifting from skill training toward a composite model of cultural translation, creative generation, and human-machine collaborative creation. Traditional animation course groups suffer from fixed content, fragmented structure, static resources, single evaluation, and disconnection from industry needs, making them insufficient for AIGC, virtual production, interactive storytelling, and cultural digitization. From the perspective of digital humanities, this study constructs a dynamic optimization mechanism for animation course groups based on human-machine collaboration. The proposed mechanism follows the full chain of demand perception, structural reorganization, content iteration, teaching collaboration, evaluation feedback, and dynamic updating. It includes a four-tier curriculum structure, a digital humanities resource center, role division among teachers, AI systems, and students, and multimodal data-driven regulation. The framework improves curriculum adaptability, cultural depth, teaching efficiency, and industry relevance. Its emphasis on virtual production, computational imaging, optical propagation, and networked collaboration also supports engineering-oriented animation education systems.
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