The Effect of Generative Artificial Intelligence-Assisted Project-Based Learning on Vocational High School Students' Statistical Literacy and Critical Thinking Skills - Lestari, Siti; Setyawati, Astri (Desimal: Jurnal Matematika, 2026)
The rapid advancement of Generative Artificial Intelligence (GenAI) has created new opportunities to enhance mathematics instruction by supporting authentic, inquiry-based learning. However, empirical evidence regarding the integration of GenAI into Project-Based Learning (PjBL) to improve statistical literacy and critical thinking among vocational high school students remains limited. This study aimed to examine the effect of GenAI-assisted Project-Based Learning on students' statistical literacy and critical thinking skills. A quasi-experimental research design employing a non-equivalent control group design was conducted with 61 eleventh-grade vocational high school students, comprising 32 students in the experimental group and 29 students in the control group. The experimental group received Project-Based Learning integrated with Generative Artificial Intelligence, while the control group experienced conventional Project-Based Learning. Data were collected using validated essay-based tests of statistical literacy and critical thinking and analyzed using descriptive statistics, Shapiro–Wilk normality tests, Levene's homogeneity tests, independent-samples t-tests, normalized gain (N-gain), and Cohen's d effect size. The results revealed that students in the experimental group achieved significantly higher posttest scores and learning gains than those in the control group for both statistical literacy (t = 5.87, p < .001; d = 1.52) and critical thinking (t = 5.12, p < .001; d = 1.31). These findings indicate that integrating Generative Artificial Intelligence into Project-Based Learning provides substantial educational benefits by promoting higher-order thinking and data interpretation skills. The study offers an evidence-based instructional framework for integrating artificial intelligence into vocational mathematics education to better prepare students for data-driven and technology-rich learning environments.