Analyzing the network structure of students' motivation to learn AI: a self-determination theory perspective.

Abstract

Motivation is a key driver of learning. Prior work on motivation has mostly focused on conventional learning contexts that did not necessarily involve AI. Hence, little is known about students' motivation to learn AI. This study examined the structure of students' AI motivational system using self-determination theory as the theoretical framework. Self-determination theory posits that there are qualitatively distinct types of motivation, including intrinsic motivation, identified regulation, introjected regulation, external regulation, and amotivation. Students' motivation, in turn, is strongly shaped by whether their basic psychological needs for competence, autonomy, and relatedness are satisfied. We used network analysis to explore the structure of students' AI motivation. Participants included 1465 students from 47 universities. Introjected regulation was central to the AI motivational system but intrinsic motivation was less central. This meant that many students learned AI primarily out of guilt or shame and not because of personal enjoyment. Furthermore, competence satisfaction seemed more important than autonomy and relatedness satisfaction in AI-enriched learning environments. Hence, key practical implications include the need to have clear goals and standards as well as to build students' competence in using AI tools. This study enriches the AI education literature by focusing on students' motivational systems and suggesting ways to cultivate better engagement with AI.