Cognitive load, prior knowledge, and sustained learning intention in a generative-AI-supported digital cultural learning context

BackgroundGenerative artificial intelligence is increasingly embedded in learning as a source of explanation, feedback, and interactive support. However, it remains unclear how such support shapes learners' cognitive processing and their subsequent willingness to continue learning.ObjectivesFrom an educational psychology perspective, this study examines how information quality, perceived ease of use, and perceived interactivity are associated with sustained learning intention through intrinsic and extraneous cognitive load, and whether prior cultural knowledge moderates the associations between cognitive load and sustained learning intention.MethodsThis cross-sectional survey study was conducted in The Art of Life: Mawangdui Han Culture Immersive Digital Exhibition as a high-complexity digital cultural learning context. Survey data were collected from 572 learners who reported a recent and complete experience of using generative AI tools for understanding or further learning content related to Mawangdui Han culture. Partial least squares structural equation modeling was used to test net effects, statistical indirect associations, and moderation, and fuzzy-set qualitative comparative analysis was used to identify configurations associated with high and low sustained learning intention.Results and conclusionsInformation quality and perceived ease of use were significantly negatively associated with both intrinsic and extraneous cognitive load, whereas perceived interactivity was significantly positively associated with both forms of load. Intrinsic and extraneous cognitive load were both negatively associated with sustained learning intention. Prior cultural knowledge weakened the negative association between extraneous cognitive load and sustained learning intention but strengthened the negative association between intrinsic cognitive load and sustained learning intention. The fsQCA results further revealed multiple asymmetric configurations associated with high and low sustained learning intention. These findings suggest that the value of generative AI-supported learning in this context lies not in maximizing interaction, but in providing cognitively manageable support. In this study, sustained learning intention refers to a self-reported intention measure rather than observed long-term learning behavior.