Achievement goal theory in AI-supported learning: Psychological pathways and configurations leading to student engagement

Abstract

Artificial intelligence (AI) is increasingly integrated into higher education, yet little is known about how students’ achievement goal orientations are associated with their engagement in AI-supported learning. Grounded in Achievement Goal Theory, this study examined the relationships among mastery-approach, mastery-avoidance, performance-approach, and performance-avoidance goals, enjoyment, flow, and student engagement. Survey data were collected from 662 Chinese university students with experience in AI-supported learning. Structural equation modeling was employed to examine psychological pathways, while fuzzy-set qualitative comparative analysis was used to identify alternative configurations associated with high engagement. The results showed that both mastery goals were positively associated with enjoyment and flow, whereas both performance goals were associated with flow but not enjoyment. Enjoyment mediated the relationships between mastery goals and engagement, while flow mediated the relationships between all four goal orientations and engagement. The configurational analysis further identified multiple sufficient pathways to high engagement, with no single condition being necessary. These findings extend Achievement Goal Theory by revealing differentiated affective and immersive mechanisms and multiple motivational configurations underlying student engagement in AI-supported higher education.