Enhancing Trigonometry Conceptual Understanding Through an AI-Driven Learning Management System

Self-regulated learning (SRL) plays a crucial role in mathematics, particularly in trigonometry, which requires conceptual understanding and higher-order thinking. However, many students encounter difficulties in managing their own learning effectively. This study aims to analyze the impact of integrating artificial intelligence (AI)-based learning management system (LMS) on students’ SRL, focusing on whether adaptive and personalized digital environments can enhance learning autonomy in trigonometry. A quasi-experimental design with a pretest–posttest control group was employed. The participants consisted of sixty high school students divided equally into two groups: the experimental group utilized an AI-based LMS, while the control group engaged in conventional learning. Data were collected using a Likert-scale SRL questionnaire covering six indicators: goal setting, strategic planning, self-monitoring, self-control, help-seeking, and self-evaluation. Statistical tests of normality, homogeneity, and parametric comparisons were applied to examine differences between the groups. The findings revealed that the experimental group achieved a higher average SRL percentage score (79.5%) compared to the control group (71%), indicating stronger SRL behaviors among students using the AI-based LMS. The most notable improvements were found in goal setting and self-monitoring, suggesting that the AI-based LMS effectively guided students in formulating clearer objectives and tracking progress more consistently. Furthermore, the improvement in SRL was positively associated with better comprehension of trigonometry concepts. The integration of AI-based LMS substantially enhances students’ SRL and supports deeper mathematical understanding. These findings highlight the potential of adaptive digital technologies to foster independent, reflective learners with greater control over their learning processes.