On digital transformation, the integration of artificial intelligence and digital learning materials into teaching has attracted increasing attention in the field of education. At the same time, the Mathematics General Education Curriculum encourages students to actively explore and construct knowledge. Therefore, digital learning materials are considered a promising tool to support students’ knowledge construction through experience, interaction, and self-regulation. This study adopts a theoretical research approach to propose a process for applying artificial intelligence to develop digital learning materials for teaching functions and graphs based on constructivist theory. In addition, a quasi-experimental method involving 68 students examined the feasibility and effectiveness of the proposed process. The data were analyzed using paired-sample t-tests to compare pre-test and post-test results within each group, and independent-sample t-tests to compare post-test results between the experimental and control groups. The results indicate that the experimental group achieved significantly higher learning outcomes, with a statistical significance level of 0.000 and a very large effect size (Cohen’s d = 2.94). Classroom observations further reveal that the use of digital learning materials facilitates students’ self-correction and cognitive self-regulation, which is consistent with the principles of constructivist theory in mathematics education.