<![CDATA[This study is motivated by students’ low level of mathematical conceptual understanding and the limited integration of adaptive technology in problem-based learning in the classroom. It aims to analyze the effect of Artificial Intelligence (AI)-assisted Problem-Based Learning (PBL) on high school students’ mathematical conceptual understanding. This research employs a quantitative approach with a quasi-experimental design, specifically a pretest–posttest non-equivalent control group design. The sample consists of two classes: an experimental class implementing AI-assisted PBL and a control class using conventional PBL. Data were collected through a mathematical conceptual understanding test and analyzed using a t-test. The results indicate a significant difference between the two groups (p < 0.05), with the experimental group’s mean score (M = 76.2; SD = 5.2) higher than that of the control group (M = 69.5; SD = 6.1). These findings suggest that integrating AI into PBL is more effective in improving students’ mathematical conceptual understanding. The implications of this study highlight the importance of utilizing AI as cognitive scaffolding in problem-based learning to enhance the quality of mathematics instruction in a more adaptive and meaningful way.]]>
Copyrights © 2026
