Fostering Continuous Innovation in Creative Education: A Multi-Path Configurational Analysis of Continuous Collaboration with AIGC in Chinese ACG Educational Contexts

Abstract

AI-generated content (AIGC) is uniquely positioned to drive the digital transformation of professional education in the animation, comic, and game (ACG) industries. However, its collaborative application also faces initial novelty effects and user discontinuance. Existing studies often employ single-variable analytical methods, which struggle to capture the complex mechanisms influencing technology adoption. This study innovatively combines necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA) and applies them to the field of ACG education. Using this mixed-method approach, it systematically explores the necessary conditions and configurational effects influencing educational users’ continuance intention to adopt AIGC tools for collaborative design learning, aiming to address existing research gaps. A survey of 312 Chinese ACG educational users revealed that no single factor constitutes a necessary condition for their continuance intention to adopt AIGC tools. Additionally, five pathways leading to high adoption intention and three pathways leading to low adoption intention were identified. Notably, the absence or insufficiency of task–technology fit, and perceived quality do not hinder ACG educational users’ willingness to actively adopt AIGC tools. This reflects the creativity-driven learning characteristics, and the flexible and diverse tool demands of the ACG discipline. The findings provide theoretical and empirical insights to enhance the effective synergy and sustainable development between ACG education and AIGC tools.

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