<![CDATA[The ability to comprehend texts and mathematical literacy are crucial competencies for future elementary school teachers. However, research shows that students in the Elementary School Teacher Education (PGSD) program often struggle with interpreting texts and solving mathematical problems, indicating low levels of disciplinary literacy. This study aims to examine the implementation and effectiveness of the Anticipation Guides reading strategy integrated with Generative Artificial Intelligence (GenAI) in improving students’ text comprehension and mathematical literacy. A quasi-experimental design with a nonequivalent control group was used, involving 50 PGSD students divided into experimental and control groups. The experimental group received instruction using Anticipation Guides integrated with GenAI, while the control group received conventional instruction. Data were collected through pretest and posttest instruments measuring both competencies and analyzed using Independent Sample t-tests and MANOVA. The results showed that the experimental group achieved significantly higher N-gain scores in text comprehension (0.65) and mathematical literacy (0.60) compared to the control group. The statistical analysis indicated a significant effect (p < 0.001) with large effect sizes and a multivariate impact based on Wilks' Lambda (Λ = 0.291, F(2,46) = 55.93). These findings suggest that integrating Anticipation Guides with GenAI provides effective scaffolding and fosters higher-order thinking skills. The strategy enhances student engagement, supports personalized learning, and strengthens cross-disciplinary literacy. Therefore, this approach offers a relevant and innovative solution for improving essential literacies among teacher education students in the digital era. Keywords: Anticipation Guides; Generative AI; text comprehension; mathematical literacy; PGSD students]]>
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