Understanding Behavioral Intention to Use AI Note-Taking Tools in Online Learning: A Self-Determination Theory Perspective

With the rapid development of artificial intelligence (AI), AI note-taking has become an important learning support tool for knowledge integration and thinking development, yet it is still mainly viewed as an efficiency-enhancing supplement to traditional note-taking. Therefore, understanding the factors associated with learners’ behavioral intention (BI) to use AI note-taking tools is important for promoting AI-assisted learning. Based on self-determination theory (SDT), this study examines the associations of processing fluency (PF) and learning agility (LA) with learners’ BI to use AI note-taking tools, as well as the moderating role of cognitive load (CL), in online learning environments. A total of 380 questionnaires were collected, and 308 valid responses were retained after data screening. Structural equation modeling was used for analysis. Results showed that PF and LA were significantly and positively associated with perceived autonomy (PA), perceived competence (PC), and perceived relatedness (PR). PA, PC, and PR were significantly and positively associated with BI. However, PF and LA showed significant indirect associations with BI only through PC and PR, whereas the indirect associations through PA were not statistically significant. Furthermore, CL scores moderated several first-stage associations. The positive associations between LA and PA and between PF and PC were stronger at lower CL scores than at higher CL scores. Given the context-specific and positively scored CL measure, these findings indicate variations in the associations across CL score levels rather than differences between higher and lower overall cognitive load.