Sensing, thinking, acting: how AI autonomy in higher education reshapes student knowledge management via self-determination needs

Purpose This study aims to investigate how the artificial autonomy of artificial intelligence (AI) educators influences students’ knowledge management performance (i.e. knowledge acquisition, internalization and application) through affecting their self-determination motivations. Design/methodology/approach This study conducted an online survey. Data from 775 valid responses were analyzed by partial least squares structural equation modeling. Findings The analysis reveals that sensing and action autonomy of AI educators can improve students perceived autonomy, competence and relatedness, while thought autonomy can only significantly increase students’ autonomy and competence perception. Moreover, students’ intrinsic motivations of autonomy and competence are positively related their performance across three dimensions of knowledge management. However, the satisfaction of students’ relatedness in AI learning journey can only increase their knowledge acquisition, but is not significantly correlated with knowledge internalization and application. Originality/value This study highlights the research gap by investigating the critical roles of AI autonomy in students’ intrinsic motivation in AI learning and knowledge management performance. It provides new theoretical understanding for the literature on AI in higher education, knowledge management and the application of self-determination theory, as well as offering practical implications for AI designers and human educators aiming to adopt AI teaching tools.