Examining the effect of AI-integrated case-based learning on undergraduate students’ critical thinking skills in mathematics education

Abstract

Purpose: This study aims to investigate the impact of AI-Integrated Case-Based Learning (AI-CBL) on undergraduate students’ critical thinking abilities in mathematics education, addressing the lack of empirical evidence on AI’s role in fostering higher-order mathematical reasoning. Method: A quantitative quasi-experimental study using a post-test-only control group design was employed. The participants were 51 undergraduate mathematics education students from Universitas Khairun selected through purposive sampling, with 26 students in the experimental group and 25 in the control group. The experimental group received eight AI-CBL sessions, while the control group received traditional lecture-based instruction. Data were collected using a validated open-ended mathematics critical thinking test covering Analysis, Algebra, Geometry, Statistics & Probability, and Calculus (scores 40–160). Statistical analyses included descriptive statistics and an independent-samples t-test conducted using SPSS. Findings: The experimental group obtained significantly higher post-test critical-thinking scores than the control group (M = 123.08, SD = 6.70 vs. M = 90.12, SD = 6.33; p < .001). These findings indicate that students who participated in AI-CBL demonstrated higher post-test critical-thinking performance than those who received traditional instruction. Significance: This study provides evidence that integrating adaptive AI with case-based instruction can promote higher-order thinking in mathematics. Findings are relevant for educators, curriculum designers, and policymakers seeking innovative strategies to develop critical thinking skills in mathematics education.