Effect of AI-Based Learning Tools on Student Engagement and Academic Performance in Higher Education: A Self-Determination Theory Perspective

The study aimed to evaluated how students use of AI-based learning tools shapes their engagement and academic performance in higher education using the self-determination theory (SDT). The study investigated how the three psychological need dismissions of students which are autonomy, competence, and relatedness according the SDT are supported by their use of AI-based learning tools. A cross-sectional survey research design was used in the study. Data were collected using a structured questionnaire from 452 undergraduate computing students who were purposively sampled based on their use of AI-based learning platforms. From the finding of the study, the students indicated that, AI-based learning tools support their autonomy needs through self-paced, personalized and self-directed learning. The corrective immediate feedback feature of AI-based learning tools also supports their confident and mastery of complex concepts. However, their relatedness need was least satisfied which reflects a limitation of AI-based learning tools as they fail to replicate genuine peer connections. All the three dimensions were positively and significantly correlated with academic performance which was measured with their grade point average (GPA). Competence emerging as the having strongest correlation with academic performance (r = .387, p < .01), followed by autonomy (r = .241, p < .01), and relatedness (r = .198, p < .01). The findings suggest that AI tools support key psychological needs associated with student engagement, although their contribution to academic performance appears indirect and mediated through motivational processes.