Relationship Between Artificial Intelligence, Learning Motivation, and Academic Achievement of Secondary School Students

Artificial Intelligence (AI) is increasingly integrated into secondary school education through adaptive learning systems, intelligent tutoring applications, automated assessments, and data-driven feedback tools. While AI technologies are designed to improve instructional efficiency and personalize learning, their influence on students’ psychological and academic outcomes remains insufficiently explored at the school level. The present study investigates the relationship between AI usage, learning motivation, and academic achievement among secondary school students. A quantitative correlational design was employed, and data were collected from 520 students enrolled in Grades IX–XII. Standardized instruments measured AI usage and learning motivation, while academic achievement was obtained from official examination records. Structural Equation Modeling (SEM) was used to examine direct and indirect relationships among the variables. The findings revealed significant positive relationships between AI usage and learning motivation, learning motivation and academic achievement, and AI usage and academic achievement. Furthermore, learning motivation partially mediated the relationship between AI usage and academic performance. The results suggest that AI-supported educational environments enhance academic achievement by fostering motivational factors such as intrinsic interest, academic confidence, and engagement. The study contributes to the growing body of research on AI in school education and provides practical implications for educators and policymakers.