AI-Supported Personalized Learning and University Students’ Learning Motivation: A Qualitative Study Based on Self-Determination Theory

Yi Jia Li(Sultan Idris Education University)
TANG TSIAO YIN(Sultan Idris Education University)

Abstract

AI-supported personalized learning is increasingly adopted in higher education, yet students’ motivational experiences in such environments remain insufficiently understood. Guided by Self-Determination Theory (SDT), this qualitative study explores how AI-supported personalized learning shapes university students’ learning motivation, focusing on autonomy, competence, engagement, and perceived risks of overdependence. Semi-structured interviews were conducted with eight Chinese undergraduates who used AI-supported tools for coursework. Data were analyzed through thematic analysis using NVivo 14. Findings indicate that AI-supported personalization can enhance motivation by supporting autonomy (flexible pacing and individualized pathways) and competence (timely, specific, scaffolded feedback), which strengthens engagement, confidence, and persistence. However, participants also reported concerns about excessive reliance, shallow processing, and reduced independent thinking. Overall, the motivational value of AI-supported personalized learning depends on balancing technological support with instructional designs that protect learners’ cognitive autonomy. 

Keywords

AI-supported personalized learning; learning motivation; self-determination theory; higher education; qualitative study

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DOI: http://dx.doi.org/10.12345/jetm.v10i1.34846