<![CDATA[The rapid integration of Artificial Intelligence (AI) into higher education has created new opportunities for learning while raising concerns about students’ cognitive autonomy. This study investigated the effects of AI literacy on AI dependency and computational thinking skills, as well as the relationship between AI dependency and computational thinking skills in Linear Algebra learning. The population comprised undergraduate students enrolled in Linear Algebra, with 120 students selected through purposive sampling based on their involvement in the course and experience with AI-supported learning. A quantitative survey method was employed, and the data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The model consisted of AI Literacy (LA), AI Dependency (KD), and Computational Thinking Skills (CT). The initial measurement model included eight LA indicators, six KD indicators, and six CT indicators. Three indicators (LA7, KD6, and CT5) were removed because their loading factors were below 0.60. After re-estimation, all retained indicators exceeded 0.70, with AVE values of 0.842, 0.844, and 0.753 for AI literacy, AI dependency, and computational thinking skills, respectively. AI literacy positively and significantly predicted AI dependency (β=0.629, t=11.849, p<0.001) and computational thinking skills (β=0.527, t=4.893, p<0.001). AI dependency had a negative but non-significant relationship with computational thinking skills (β=-0.171, t=1.449, p=0.074). The R² was 0.198, while effect sizes were 2.073 for AI literacy and 0.022 for AI dependency. The findings indicated that AI literacy is more influential than AI dependency in explaining students’ computational thinking.]]>
Copyrights © 2026
